D3.1: Technology Mapping and Scouting€¦ · D3.1: Report on technology mapping and scouting 11 1 Introduction The aim of work package 3 (WP3) is to develop a strategic research

Project ID: 768884

H2020-NMBP-CSA-2017 Mapping a path to future Supply Chains

Next generation Technologies for networked Europe

D3.1: Technology Mapping and Scouting

Disclaimer:

The NEXT-NET project is co-funded by the European Commission under the Horizon 2020 Framework

Programme. This document reflects only authors’ views. EC is not liable for any use that may be done of the

information contained therein.

WP: WP3 – Strategic Research Agenda for NEXT-NET

Task T3.1 – Technology mapping and scouting

Partner responsible: Markus Stute, Matthias Parlings, Saskia Sardesai, Josef

Kamphues (Fraunhofer IML)

Contributors:

Rosanna Fornasiero, Andrea Zangiacomi and Irene

Marchiori (CNR-ITIA), Ana C. Barros, Kerley Pires and

Pedro P. Senna (INESC TEC), Victoria Muerza (ZLC),

Dimitra Kalaitzi and Aristides Matopoulos (Aston

University)

Status: Draft

Date: 07/08/2018

Version: 1.0

Classification: Public

Ref. Ares(2018)4151078 - 07/08/2018

D3.1: Report on technology mapping and scouting

2

NEXT-NET Project Profile

Project ID: 768884; H2020-NMBP-CSA-2017

Acronym: NEXT-NET

Title: Next generation Technologies for networked Europe

URL: https://nextnetproject.eu/

Start Date: 01/10/2017

Duration: 24 Months


3

Executive Summary

This report is the Deliverable 3.1 of the NEXT-NET project. The aim of the project is to put in

place a cross-sectoral and cross-technological initiative at European level to increase

integration between production and distribution, as well as to propose research and innovation

priorities for the future of supply chain.

The Deliverable 3.1 looks to identify enabling technologies through the analyses of existing

roadmaps and studies, selection of the most important technologies, and their evaluation with

respect to their implications on the supply chain. For this purpose, expert workshops and

literature review were conducted according to three industry sectors: discrete manufacturing,

process industry and logistics & distribution.

Based on these expert workshops and literature review, the following 18 enabling technologies

were selected and evaluated:

1. Autonomous Transport Systems

2. Robots

3. Cloud Based Computer Systems

4. Internet of Things

5. Distributed Ledger / Blockchain

6. Artificial Intelligence

7. Data Science

8. Mobile and Wearable Devices

9. Communication Infrastructure

10. Identification Technologies

11. Location Technologies

12. Visual Computing

13. Additive Manufacturing

14. Energy Infrastructure

15. Alternative Propulsion Systems

16. Renewable Energy Technologies for Production and Storage

17. Smart Materials

18. Nanotechnology

The methodological approach for evaluation of these 18 technologies includes the

assessment of their Technology Readiness Level (TRL), Applicability Scoring and the

identification of the Implications on the Supply Chain Performance. Moreover, Application

Examples and Gap Analyses, including Technology Gaps and description of Implementation

Challenges, are provided.

Based on their applicability and their implications on the supply chain nine technologies

emerge to be a focus for future research: Internet of Things, Distributed Ledger / Blockchain,

Artificial Intelligence, Data Science, Identification Technologies, Autonomous Transport

Systems, Cloud Based Computer Systems, Communication Infrastructure and Additive

Manufacturing.


4

Based on the future SC scenarios generated in T2.3 (Scenario integration and assessment)

and the identified enabling technologies in this report, a mapping of the enabling technologies

necessary to implement each specific scenario will be performed in D3.2 (Report on

technology mapping on future scenarios).


5

Table of Contents

1 Introduction ...................................................................................... 11

2 Methodology ..................................................................................... 12

General Scouting Approach...................................................................... 12

Assessment Methodology ........................................................................ 13

2.2.1 TRL DEFINITION ............................................................................. 13

2.2.2 APPLICABILITY SCORING ............................................................. 14

2.2.3 IMPLICATIONS ON SUPPLY CHAIN PERFORMANCE .................. 15

2.2.4 APPLICATION EXAMPLES ............................................................. 16

2.2.5 GAP ANALYSIS ............................................................................... 16

3 Enabling Technologies ................................................................... 17

Autonomous Transport Systems ............................................................. 17

3.1.1 TECHNOLOGY EVALUATION ......................................................... 17


3.1.3 TECHNOLOGY GAPS AND IMPLEMENTATION CHALLENGES .... 21

Robots ........................................................................................................ 22




Cloud Based Computer Systems ............................................................. 25




Internet of Things ...................................................................................... 29




Distributed Ledger / Blockchain ............................................................... 35




Artificial Intelligence .................................................................................. 39


6




Data Science .............................................................................................. 43




Mobile and Wearable Devices ................................................................... 48




Communication Infrastructure .................................................................. 52




Identification Technologies....................................................................... 55




Location Technologies .............................................................................. 57




Visual Computing ...................................................................................... 61




Additive Manufacturing ............................................................................. 66




Energy Infrastructure ................................................................................ 69





7

Alternative Propulsion Systems ............................................................... 73




Renewable Energy Technologies for Production and Storage .............. 77




Smart Materials .......................................................................................... 79




Nanotechnology......................................................................................... 83




4 Conclusion and summary ............................................................... 87

References ............................................................................................... 90

Annex A: List of Acronyms ............................................................... 117

Annex B: Running EU Projects of Project Partners ....................... 119


8

List of Figures

Figure 2-1: General Technology Scouting Approach ........................................................... 12

Figure 3-1: List of the 18 enabling technologies .................................................................. 17

Figure 3-2: Technology Evaluation Autonomous Transport Systems .................................. 18

Figure 3-3: Implications of Autonomous Transport Systems on SC Performance ................ 18

Figure 3-4: Technology Evaluation Robots .......................................................................... 22

Figure 3-5: Implications of Robots on SC Performance ....................................................... 23

Figure 3-6: Technology Evaluation Cloud Based Computer Systems .................................. 26

Figure 3-7: Implications of Cloud Based Computer Systems on SC Performance ............... 27

Figure 3-8: Technology Evaluation Internet of Things ......................................................... 30

Figure 3-9: Implications of Internet of Things on SC Performance....................................... 31

Figure 3-10: Technology Evaluation Distributed Ledger / Blockchain .................................. 36

Figure 3-11: Implications of Distributed Ledger / Blockchain on SC Performance ............... 36

Figure 3-12: Technology Evaluation Artificial Intelligence .................................................... 40

Figure 3-13: Implications of Artificial Intelligence on SC Performance ................................. 40

Figure 3-14: Technology Evaluation Data Science .............................................................. 45

Figure 3-15: Implications of Data Science on SC Performance ........................................... 45

Figure 3-16: Technology Evaluation Mobile and Wearable Devices .................................... 49

Figure 3-17: Implications of Mobile and Wearable Devices on SC Performance ................. 49

Figure 3-18: Technology Evaluation Communication Infrastructure ..................................... 52

Figure 3-19: Implications of Communication Infrastructure on SC Performance .................. 53

Figure 3-20: Technology Evaluation Identification Technologies ......................................... 55

Figure 3-21: Implications of Identification Technologies on SC Performance ...................... 56

Figure 3-22: Technology Evaluation Location Technologies ................................................ 58

Figure 3-23: Implications of Location Technologies on SC Performance ............................. 58

Figure 3-24: Technology Evaluation Visual Computing ....................................................... 62

Figure 3-25: Implications of Visual Computing on SC Performance .................................... 62

Figure 3-26: Technology Evaluation Additive Manufacturing ............................................... 67

Figure 3-27: Implications of Additive Manufacturing on SC Performance ............................ 67

Figure 3-28: Technology Evaluation Energy Infrastructure .................................................. 70

Figure 3-29: Implications of Energy Infrastructure on SC Performance ............................... 71

Figure 3-30: Technology Evaluation Alternative Propulsion Systems .................................. 73

Figure 3-31: Implications of Alternative Propulsion Systems on SC Performance ............... 74


9

Figure 3-32: Technology Evaluation Renewable Energy Technologies for Production and

Storage ............................................................................................................................... 77

Figure 3-33: Implications of Renewable Energy Technologies on SC Performance ............ 78

Figure 3-34: Technology Evaluation Smart Materials .......................................................... 80

Figure 3-35: Implications of Smart Materials on SC Performance ....................................... 80

Figure 3-36: Technology Evaluation Nanotechnology ......................................................... 83

Figure 3-37: Implications of Nanotechnology on SC Performance....................................... 84

Figure 4-1: Focus technologies for future research ............................................................. 88


10

List of Tables

Table 2-1: Reference list of the analysed Roadmaps and Studies....................................... 13

Table 2-2: Types and Numbers of References used for the Identification, Selection, Mapping

and Assessment Approaches .............................................................................................. 13

Table 2-3: Definition of TRL and NEXT-NET categories [114] ............................................. 14

Table 2-4: NEXT-NET definition of TRL .............................................................................. 14

Table 2-5: Description of applicability scores ...................................................................... 15

Table 2-6: Description of supply chain performance evaluation ........................................... 15


11

1 Introduction

The aim of work package 3 (WP3) is to develop a strategic research agenda for the supply

chains of the future. This deliverable for task 3.1, the technology mapping and scouting, aims

to identify enabling technologies for the three industry sectors discrete manufacturing, process

industry and logistics & distribution through the analysis of existing roadmaps and studies

regarding enabling technologies, select the most important of them and evaluate their

applicability and implications on the supply chain performance. The deliverable also includes

a mapping of the technologies with respect to what has been already developed (Technology

Readiness Level and application examples) and an evaluation of still existing technological

gaps and implementation challenges.

Technology is not the only factor that determines a supply chain competitiveness, but it is a

key factor, that can make the difference in the implementation of a specific strategy. The

selection of raw materials and supply partners, the choice of markets to be supplied and the

way the product or service is offered (e.g. pay per use, reward systems) depend on and affect

the context of the supply chain. However, some of these decisions in the definition of the

supply chain are more changeable (e.g. production equipment vs. payment terms), and some

decisions can be made to make more assets more variable (e.g. leasing equipment vs. owning

equipment) in order to respond more quickly to changes in their environment.

Therefore, it is important to point out that technology has an important implication on improving

agility, transparency or reliability of a supply chain, but supply chain performance also depends

on other framework conditions and decisions. This deliverable aims to illustrate how a wide

range of technologies can contribute to improving supply chain performance. This deliverable

is by no a means a stand-alone deliverable, but it is explicitly meant to be considered in context

of WP2 and WP3 deliverables of the NEXT-NET project.

Open innovations are recognised as a critical tool for accelerating growth, and the rapid pace

of change in emerging technology markets heightens the importance of scouting for and

incorporating technologies from the innovation ecosystem [217]. Using a scouting process

based on the open innovation paradigm, experts were therefore involved in the identification

of new technologies necessary to support the supply chain evolution for 2030.

This deliverable is structured as follows. Chapter 2 presents the methodology used for task

3.1. In Chapter 3, the selected enabling technologies are defined and evaluated. Chapter 4

summarises the results of this deliverable and presents a preview for the next steps of the

NEXT-NET project.


12

2 Methodology

General Scouting Approach

In order to identify enabling technologies, existing roadmaps and studies at different levels

(regional, national, international; sector-specific) were analysed. To select the most important

enabling technologies, each partner held a project-internal workshop with experts from the

three sectors: discrete manufacturing, process industry and logistics & distribution. To

evaluate and validate these selected technologies, technology evaluation methodology

(applicability scoring and implications on supply chain performance), description of application

examples and gap analyses (technology gaps and implementation challenges) were gathered

for each technology through inputs by the project-internal experts from the three sectors, and

further complemented with specialised experts from different fields, as well as with literature

research. Moreover, running projects of the project partners were taken into account

considering the main programs (SPIRE, FOF, CIRC, Transportation), but also other related

programs, in which partners are involved, such as Interreg, Regional Funds (see Annex B:).

Figure 2-1 presents the general identification, selection, mapping and assessment

approaches, including five of the analysed technology roadmaps as an example.

Figure 2-1: General Technology Scouting Approach

The roadmaps and studies from the three sectors (discrete manufacturing, process industry

and logistics & distribution) shown in Table 2-1 were analysed to identify the enabling

technologies.

Identification Selection Assessment

DHL Logistics

Trend Radar

Digital Transform.

Scoreboard(EU Commission)

VDMA Future

Business Trends BVL Trends

and Strategiesin Logistics

and SCM

Visions of the

Future: Transportation

& Logistics 2030

Expert

Workshop

Assessment

Methodology

+ more Roadmaps and Studies (see 2.1.1)


13

Table 2-1: Reference list of the analysed Roadmaps and Studies

Sector References

General (all sectors) [276], [277], [289]

Discrete Manufacturing [34], [71], [111], [113], [148], [176], [214], [275], [298], [345],

[349], [369]

Process industry [34], [90], [113], [330]

Logistics & distribution [14], [56], [65], [79], [82], [89], [90], [128], [140], [196], [274],

[368], [371]

Table 2-2 classifies the references which were included for the identification, selection,

mapping and assessment approaches in four types and shows the number of references for

each type.

Table 2-2: Types and Numbers of References used for the Identification, Selection, Mapping and Assessment

Approaches

Type of reference Number of references

Institutional documents ~60

Scientific references ~150

Companies’ websites ~120

European Roadmaps and Projects ~75

The assessment methodology will be explained in more detail in the following chapter.

Assessment Methodology

Technology Readiness Level (TRL), industrial applicability scoring and their implications on

supply chains are assessed in order to evaluate the identified enabling technologies. The

assessment dimension also includes a gap analyses with technological gaps and

implementation challenges. The evaluation of the technologies is based on the inputs from the

project-internal experts and further subject-specific experts, as well as literature review.

2.2.1 TRL DEFINITION

Technology Readiness Level refers to the maturity of a technology. It specifies how far a

technology is developed and uses a scale from 1 to 9, whereby the lowest score of 1, or TRL

1, indicates that basic principles are observed and the highest score of 9, or TRL 9, means

that the technology is already used in market successfully. [114]

Table 2-3 shows the definition of the individual Technology Readiness Levels as defined in

the Horizon 2020 annex [114].


14

Table 2-3: Definition of TRL and NEXT-NET categories [114]

Level Definition

TRL 1 Basic principles observed

TRL 2 Technology concept formulated

TRL 3 Experimental proof of concept

TRL 4 Technology validated in lab

TRL 5 Technology validated in relevant environment (industrially relevant

environment in the case of key enabling technologies)

TRL 6 Technology demonstrates in relevant environment (industrially relevant

environment in the case of enabling technologies)

TRL 7 System prototype demonstration in operational environment

TRL 8 System complete and qualified

TRL 9 Actual system proven in operational environment (competitive

manufacturing in the case of key enabling technologies, or in space)

In the context of the NEXT-NET project, the nine levels are compiled to four categories. NEXT-

NET Category A includes solely TRL 9 and, therefore, the technology already shows market

presence. TRLs 7 and 8 are NEXT-NET Category B, where the technology is market-ready.

NEXT-NET Category C comprises technologies with TRLs 4 through 6 with applied research.

In addition, the lowest NEXT-NET Category D contains technology with basic research which

includes TRLs 1 to 3.

Table 2-4 shows the four levels of TRL used for the NEXT-NET project.

Table 2-4: NEXT-NET definition of TRL

Level NEXT-NET

Category

Definition

TRL 1-3 D Basic research

TRL 4-6 C Applied research

TRL 7-8 B Market-ready

TRL 9 A Market presence

The TRL scoring for each technology is based on the level of the application examples, the

technology gaps and the implementation challenges identified by the experts, complemented

by literature review.

2.2.2 APPLICABILITY SCORING

The applicability scoring was defined within the NEXT-NET project and refers to the extent of

the applications of a specific technology for the three sectors: discrete manufacturing, process


15

industry and logistics & distribution. The applicability scoring uses a scale from 1 to 4, whereby

the score 1 means that the technology is not widely applicable, while score 4 implies that the

technology will have a broad application.

Table 2-5 shows the four levels of the applicability scoring and its descriptions.

Table 2-5: Description of applicability scores

Scoring Name Description

1 No significant

applicability expected

No significant application cases are expected

2 Limited applicability Applicability is limited to special application cases

3 Moderate applicability The technology will have several application cases

4 Broad applicability The technology will have broad application throughout

the industry sector

2.2.3 IMPLICATIONS ON SUPPLY CHAIN PERFORMANCE

This part describes the implication of the technology on the supply chain performance based

on six criteria. The six criteria were chosen based on the SCOR performance attributes [23]

and the Roland Berger Supply Chain Excellence Study [288]. The criteria agility, costs,

responsiveness and reliability are part of the SCOR performance attributes. On the other hand,

the criteria transparency and sustainability were chosen from the Roland Berger Supply Chain

Excellence Study. These two criteria expand the economic view of the SCOR performance

attributes to macro-economic aspects and thus complete the comprehensive view of the

implications on the supply chain performance.

Table 2-6 shows the six criteria of the supply chain performance evaluation, its descriptions

and the references.

Table 2-6: Description of supply chain performance evaluation

Criteria Description Reference

Agility Agility is defined as the ability to react to external influences and marketplace changes in order to achieve or maintain competitive advantages. The criterion includes flexibility and adaptability.

[23]

Costs The criterion costs refers to the cost of operating the supply chain processes such as labour costs, material costs, management and transportation costs. A typical cost metric is Cost of Goods Sold.

[23]

Transparency / Traceability

The ability to track a product’s flow throughout the production process and supply chain

[99; 104; 205; 288]


16

Responsiveness Responsiveness describes the speed at which a supply chain provides products to the customer. A metric for responsiveness is the cycle-time.

[23]

Reliability Reliability is defined as the ability to perform tasks according to expectations and thus emphasises the predictability of the process output. Metrics for this criteria are for example: On-time, the right quantity, the right quality

[23]

Sustainability Defined along with the UN Sustainable Development Goals: esp. 7 - Affordable and Clean Energy; 9 - Industry, Innovation and Infrastructure; and 12 -Responsible Consumption and Production.

[288; 347]

The experts from the three industry sectors (Discrete Manufacturing, Process Industry and

Logistics & Distribution) rated each supply chain performance criterion on a scale from strongly

negative to strongly positive (with five rating levels: strongly negative, negative, neutral,

positive, strongly positive) for each technology. Based on the ratings, the average value was

determined for each sector. The implications on the supply chain performance were described

by the experts and based on literature review. Due to the inclusion of experts from different

industry sectors and specialist areas, there are differences in the evaluations due to their

different points of view regarding the technologies and their implications on the supply chain,

which will be presented in Chapter 3 for each technology.

2.2.4 APPLICATION EXAMPLES

For each technology, application examples in the industry sectors were gathered by means of

expert workshops and companies’ references covering the three main sectors of analysis

(discrete manufacturing, process industry and logistics & distribution). They include typical

fields of application or specific product examples from research and practice.

2.2.5 GAP ANALYSIS

The gap analysis includes the technology gaps and the implementation challenges for each

selected technology. Technology gaps consider technological issues which occur in the

current Technology Readiness Level, and that may inhibit the expansion of the technology.

The implementation challenges address all technological, organisational, cultural and

processual barriers. Some preliminary ideas on means to overcome these barriers are also

provided.


17

3 Enabling Technologies

Based on the methodology described in chapter 2, the 18 enabling technologies in Figure 3-1

were identified.

Figure 3-1: List of the 18 enabling technologies

This list represents one of the possible classification of the technologies since there are many

different classifications occurring in these years, especially due to digitalisation. This

classification tries to put together the technologies’ implications on the supply chain from the

three perspectives we are considering in the project (discrete manufacturing, process industry

and logistics & distribution).

These 18 technologies cannot be considered separate from each other since there are

important interrelationships and dependencies between them, which will be described in the

following chapters.

Autonomous Transport Systems

Autonomous transport systems are machines that are able to act autonomously (without

human interaction) based on an individual sensing of the environment, to perceive the

environment and to decide which route to take [376]. Hence, an important success factor for

this technology are advanced navigation systems, due to the ability to react to a changing

environment, such as sea and weather conditions [200]. Autonomy in transport systems cuts

across work in robotics, autonomous and embedded systems.

Autonomous transport systems include drones, unmanned aerial vehicles (UAV) that are

remotely controlled or can fly autonomously through software-controlled flight plans [292], and

autonomous vehicles such as autonomous trucks, ships and trains.

3.1.1 TECHNOLOGY EVALUATION

The technology Autonomous Transport Systems was evaluated based on expert workshops

and literature review. As shown in Figure 3-2, the TRL Categories range from C (Applied

research) to B (Market-ready), considering the level of application examples, the technological

Robots and

Automation

Internet of Things

Distributed Ledger /

Blockchain

Artificial Intelligence

Autonomous Transport

Systems

Cloud Based Computer

Systems

Alternative Propulsion

Systems

Renewable Energy

Technologies

Smart Materials

Nanotechnology

Communication

Infrastructure

Identification

Technologies

Visual Computing

Mobile and Wearable

Devices

Location Technologies

Additive

Manufacturing Data Science

Energy Infrastructure


18

gaps and implementation challenges. There is broad applicability of Autonomous Transport

Systems for all three SC sectors.

Figure 3-2: Technology Evaluation Autonomous Transport Systems

Figure 3-3 shows the implications of Autonomous Transport Systems on the supply chain

performance for the three sectors (Manufacturing, Process and Logistics).

Figure 3-3: Implications of Autonomous Transport Systems on SC Performance

The implications of Autonomous Transport Systems on the supply chain are characterised by

the following performance criteria. The differences of the evaluation by the different sectors

are also explained.

Agility: Generally, autonomous systems are easily reconfigurable, and they result in

increased agility in cases where cloud computing leads to increased share of

information and resources. However, for the process industry sector, the use of

autonomous systems has no implication on the agility in the form of reaction to external

influences.

Costs: Autonomous systems will create opportunities to reduce costs (lower errors

margins, less labour costs and more efficient transportation) due to shared resources

and information, as well as an increase in safety [50].

-2

-1

0

1

2

Implications on the Supply Chain Performance

Manufacturing Process Logistics

Strongly positive

Positive

Neutral

Negative

Stronglynegative


19

Transparency: For the logistics sector communication will be enhanced, since, in many

cases, autonomous systems and information will be uploaded and available in real-

time. For the manufacturing sector, autonomous systems may have a slightly positive

implication on the transparency due to better communication between and within

autonomous systems (e.g. autonomous trucks communicating their contents and

destination) [258]. For the process sector, autonomous systems do not have any

implication on the transparency as they are not changing information monitoring and

capturing when compared to actual systems.

Responsiveness: In general, autonomous systems will be able to react to changing

conditions due to the increased ability to gather real-time data and connect to other

nodes/partners that can help deal with any situation without human intervention.

However, they might not run as fast as automated systems controlled by central IT

systems. Moreover, they may not be able to react to situations where human

intervention is necessary (e.g., customer complaints about delivery).

Reliability: As autonomous systems have a high scalability and interchangeability of

single devices, their reliability in logistics might be higher. They may not be as reliable

as specialised ICT systems, although they seem to be working well based on

successful applications in industry so far. Moreover, autonomous systems are safer

than human-conducted systems. They also have general advantages when compared

to human operators, such as the number of working hours, efficiency ratings (for given

activities) and lack of stress build. Autonomous systems can improve daily production

monitoring and optimisation tasks. Low-level equipment monitoring, planning and

overhaul activities will be handled by autonomous systems. Improvements include

equipment uptime and longevity, non-stop operations and production, and improved

health, safety and environment [256]. On the other hand, they require constant

preemptive maintenance and supervision from human counterpart, as well as

scheduled and routing algorithm introduction or modification.

Sustainability: Autonomous systems are more sustainable and will lead to better

resource efficiency due to emissions reductions, fuel efficient driving, better planning,

improvement of traffic and most likely less accidents [154]. Autonomous systems can

substitute the human worker in dangerous tasks. Legal restrains and regulations are

needed in order to have positive impact. Also, there is the need for labour regulations

and cultural norm to be changed to embrace such technologies.


Application examples of autonomous vehicles

Application 1: Autonomous vehicle for last mile delivery

Autonomous vehicles for last mile delivery are unmanned vehicles used to transport packages

or goods over short distances to the final location.

Example: Starship robots are self-driving delivery robots able to carry items within a 2-

mile radius. When customers order via an app, parcels and food are delivered directly


20

from stores or specialised hubs. Starship’s robots move at pedestrian speed and it takes

between 5 and 30 minutes to arrive at the customer who can monitor the robot’s journey

and the time of arrival on a smartphone. The robots are able to navigate around objects

and people and the cargo bay can only be opened by the recipient. [325]

Application 2: Outdoor Automated Guided Vehicles on Private Property

Outdoor automated guided vehicles on private property are Automated Guided Transport

(AGT) systems used for heavy transports or in-plant shuttle traffic [122].

Example: Automated guided vehicles used for moving containers at a terminal between

the container cranes and the stacking area. The project “Safe autonomous logistics and

transport vehicles” (SaLsA) led to further developments that made the combination

between AGT systems, truck and people in the outdoor environment more harmonious.

[126]

Application 3: Autonomous truck for road freight transport

Autonomous trucks for road freight transport are able to navigate on their own. Truck drivers

will be able to rest during the drive, and safety will increase due to decision-making software

that enables the right reaction in different situations [280].

Example: Mercedes Future Truck 2025 promises increased safety, lower fuel

consumption and improved working conditions for professional truck drivers. Improved

vehicle / transport management and app-based solutions will facilitate fleet operators to

save money [72].

Application 4: Autonomous ships/vessels

Autonomous vessels (ASVs) are entirely unmanned, or launch with a small crew before

becoming self-piloting pre-programmed vessels that operate using algorithms [307; 248].

Example 1: Wärtsilä successfully tests remote control ship operating capability, directs

vessel over 8,000 km distance [157].

Example 2: In 2018, Yara Birkeland, a crewless 100-150 container capacity ship, will

initially deliver fertiliser over a distance of 37 miles in Southern Norway [248].

Application 5: Autonomous trains

Autonomous trains decide for themselves in real-time and without any human intervention

whether they continue running or stop [247].

Example: Alstom is testing autonomous freight trains on a 100 km stretch of track in the

Netherlands [198].

Application 6: Autonomous Underwater Vehicle (AUV)

An autonomous underwater vehicle is a robotic device driven through the water by a

propulsion system. It is controlled and steered by an on-board computer and is maneuverable

in three dimensions. Therefore, the vehicle is able to follow precise preprogrammed

trajectories. Sensors on board of the vehicle measure the water environment and provide the

ability to perform spatial and time series measurements. [354]


21

Example: TOTAL uses AUV for exploration (pipeline inspection) and production

activities [53].

Application examples of drones

Application 1: Autonomous drones for delivery services

Example: Amazon Prime Air is an unmanned aerial delivery system by Amazon

designed to safely get packages to customers within a maximum of 30 minutes. Prime

Air offers great potential for developing the delivery services through fast parcel delivery.

[16]

Application 2: Autonomous drones for maintenance and surveillance services

Example 1: The multinational company Enel Group is a leader in electricity and gas

distribution. Recently they have chosen the start-up Percepto for implementing a

maintenance system based on drone technology. Percepto’s Sparrow is a high-tech

drone which is able to fly in automated mode under the supervision of an operator for

conducting monitoring mission and inspections. Sparrow has been tested in

Torrevaldaglia Nord power plant. [268]

Example 2: Drones are used for flying inspection runs over gas fields in Queensland. A

single drone can fly over and inspect more than a hundred wells each flight, while an

operator might visit just 6 per day. [333]

3.1.3 TECHNOLOGY GAPS AND IMPLEMENTATION CHALLENGES

Technological Gaps:

The main technology gaps of autonomous transport systems are low speed and not 100

percent accurate location and auto-positioning systems (geospatial technology). Thus, the

current autonomous systems are not deemed safe enough [156].

There are some specific technological gaps for drones as they have to fly in complex

environments (e.g. through narrow gaps). Therefore, there are technology gaps regarding

design, the manufacturing methods and problems related to power supply and endurance

[153].

Implementation Challenges:

Real autonomous transport systems have to work without a central control center and thus

without human intervention [107; 290]. The autonomous systems will need effective sensors,

GPS data, high-powered cameras and onboard powerful computer systems enabling the

autonomous system to make self-relying decisions in real-time [155].

Other implementation challenges are legal constraints (safety and security issues), the

demand for ecological systems and social acceptance of the technology. Therefore, the three

areas sensor fusion, control algorithms and communication and connectivity technologies

must be investigated and improved [290].


22

The integration into existing infrastructure, systems and the production and supply chains is

also a challenge for the implementation of autonomous transport systems. For this challenge,

standardisation of the technology will be decisive. [158]

Robots

Robots are machines with superior sensing, control, and intelligence to automate or assist

human activities. Associated with artificial intelligence (AI), robotics will influence the labour

market (especially benefiting countries with an aging population) in the next few years, e.g.

companies build factories where robots will replace 90% of human workers in China [365].

Robots can be classified into collaborative and autonomous robots. Collaborative robots

physically interact with humans in a shared workspace (´cobotics´) and are also widely used

on specific ergonomically challenging tasks, for example within the aerospace and automotive

industries [320]. By contrast, autonomous robots work without human intervention and do not

need to be programmed any more.


The technology Robots was evaluated based on expert workshops and literature review. As

shown in Figure 3-4, the TRL Category is B (Market-ready), based on the level of application

examples, the technological gaps and implementation challenges. For the manufacturing

sector the applicability of robots is broad, while the process industry sector sees a moderate

applicability. In the logistics sector there is a moderate to broad applicability of the technology.

Figure 3-4: Technology Evaluation Robots

Figure 3-5 shows the implications of Robots on the supply chain performance for the three

sectors (Manufacturing, Process and Logistics).


23

Figure 3-5: Implications of Robots on SC Performance

The implications of Robots on the supply chain performance are characterised by:

Agility: Robots will handle repetitive tasks and will be quickly reprogrammable, thus

increasing agility, especially in the manufacturing sector.

Costs: Robots are able to perform non-stop tasks and carry out hard labour allowing

for reduction of costs of labour intensive processes and reduction of errors in

production. Robotics in manufacturing achieve higher throughput, so companies can

compete for larger contracts [8]. Automation can be highly cost-effective for nearly

every size of company [8]. Robots also reduce labor accidents issues, professional

diseases such as injury for repetitive effort and costs of workforce replacement. As a

result, product quality will be far more consistent.

Transparency: There will be difficulties in assessing how robots will behave or react.

Otherwise robots will enable automated and instant sharing of information.

Responsiveness: The use of robots increases the productivity since they can operate

continuously for non-stop production. Robots that are able to detect and react to

changes faster will positively influence responsiveness. If tasks are deemed too

complex for robots, there may be negative implications on overall responsiveness.

Reliability: Due the use of robots, there will be better control of systems, the possibility

to work without shift constraints and probably less errors in the processes. Robotics in

manufacturing achieve higher throughput as the mechanical and electrical reliability is

extremely high. However, sensoring is needed in order to maintain such reliability, and

the lack of sensoring may lead to negative consequences.

Sustainability: Robots save on utilities since they don't require climate control or

lighting, and they create cleaner spaces [8]. They will improve safety for workers as

they undertake repetitive and dangerous tasks [287]. However, there are also

instances which demonstrate negative outcomes, greatly depending on the context of

defined paradigms and the energy the robots need.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


24


Application examples of collaborative robots are:

The International Organisation for Standardisation (ISO) published the ISO/TS 15066 in 2016

in which four kinds of collaborative operations are specified: Safety-rated monitored stop,

Hand guiding, Speed and separation monitoring, Power and force limiting [188].

Example 1: In the STAMINA project, a mobile robotic system was used to perform

preparation and distribution operations of parts kits in the automotive industry. The

robots are used to store auto parts and grip the heavy parts such as alternators (13kg)

and starters (9kg) to deposit them in an embedded box [323].

Example 2: YuMi is a dual armed cobot developed by the ABB Robotics. Sensors,

flexibles arms, accurate vision, dexterous grippers and sensitive force control feedback,

allow YuMi to work alongside humans into assembly activities or maintenance.

Moreover, YuMi is programmable through teaching rather than coding. [3]

Application examples of autonomous robots are:

Example 1: Researchers at Siemens have developed a two-armed robot which is able

to manufacture products without having to be programmed by means of artificial

intelligence. The robot arms will autonomously share tasks and work together in the

future of automated production. [313]

Example 2: In 2016, BMW introduced the use of innovative robotic devices, including a

small fleet of smart transport robots, at some German plants [363].

Example 3: Fetch has developed a robotic arm that drives around on a mobile base

picking items from a standard warehouse shelf and putting them into an order tote. The

Fetch Robotics line of Autonomous Mobile Robots (AMRs) offers a range of AMRs for

material handling applications. AMRs deploy and redeploy in hours and move anything

from parts to pallets in warehouses, factories and distribution centers. [120]

Example 4: TORU is a pick-by-robot for warehouses that automates item specific picking

in warehouse shelves. The robot consists of a mobile base, a removable shelf and a

retractable and rotatable column with a gripper system which is able to grasp rectangular

objects. Afterwards, the robot transports the objects in its removable shelf and finally

delivers it directly to a shipping station. [218]


Technological Gaps:

The main technological gap of robots is the lack of flexibility and adaptability of systems to

changing needs. The scarcity of research funding and the limited computing power to run

advanced algorithms in real-time are, therefore, technological gaps.

Due to expensive hardware and sensors, the costs of ownership are high and there is a long-

term return on investment for robots.


25

Because of the system complexity there are user concerns and a low user awareness of

robotics technology capabilities. The vertical integration of robots is another gap, as they might

be integrated with the management systems of production.

Furthermore, there is a lack of standard interfaces between the systems and robots still have

massive deficits in terms of IT security [124].


The major implementation challenge are the effective programming of the robots and the

standardisation initiative (consistent programming language). Autonomous robots will no

longer have to be expensively programmed in a time-consuming manner, only requiring the

specified task, and the system will automatically translate these specifications into a program

[313]. Therefore, fundamental aspects of artificial intelligence for robotics, including

development of learning ability, combined advanced pattern recognition and model-based

reasoning, as well as intelligence enhancement with common sense will be big implementation

challenges. Robot swarms that allow simpler, less expensive, modular units to be reconfigured

into a team, depending on the task that needs to be performed, while being as effective as a

larger, task-specific, monolithic robot will be the future of robotic systems [162].

The mechanisms for grasping objects also need improvements in order to decrease the

number of devices used. The development of a custom control and sensing system, which

ensures smooth operation of robot and enables close proximity collaboration in automotive

factories is also a challenge for the implementation of robots.

The improvement of robots’ safety and ethics is another important implementation challenge

[162]. Robots should operate safely with due consideration of humans on the surroundings.

Furthermore, robots should be able to efficiently plan and execute routes in a dynamic and

uncertain environment.

New power sources, battery technologies, and energy-harvesting schemes for long-lasting

operation of mobile robots will be decisive for the advancement of the technology [162].

Overall, new materials and fabrication schemes for developing a new generation of robots that

are multi-functional, power-efficient, compliant, and autonomous in ways akin to biological

organisms are needed [162].

Cloud Based Computer Systems

Cloud computing is a method for information technology (IT) services in which computing

power, data storage and services are outsourced to third parties and provided to companies

and customers as goods [286]. Connections to cloud services can be established through an

internet browser or a specific client application [350]. Thus, this technology allows to deliver

data and applications on any device, while fostering trends of mobile devices. The clouds used

for providing the information technology can be private, public, hybrid or community clouds

[11].


26

The technology cloud based computer systems can be classified into Platform as a Service

(PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS) and

Infrastructure as a Service (IaaS).

Platform as a Service (PaaS) is used for applications and other development in the cloud. With

PaaS, developers have a framework to develop or adapt applications. [24]

Software as a Service (SaaS) is a software application delivery model in which the software

vendor develops a web-native software application. Usually the end user just needs to connect

to an application running on the service provider’s platform. The system can be configured by

the user to do exactly what is required with little difficulty. [350]

Business Process as a Service (BPaaS) can be defined as the provision of business process

outsourcing services that are obtained from the cloud and are multi-client capable [133].

Infrastructure as a Service (IaaS) can be defined as self-service models for accessing,

monitoring, and managing remote datacenter infrastructures, such as compute, storage, and

networking services (e.g. firewalls). By purchasing and using IaaS on the basis of

consumption, there is no need to buy the required hardware. [24]


The technology Cloud Based Computer Systems was evaluated based on expert workshops


research) to B (Market-ready), based on the level of application examples, the technological

gaps and implementation challenges. For the manufacturing sector the applicability of the

technology is moderate to broad, while the process sector sees a moderate applicability. In

the logistics sector there is a broad applicability of the technology.

Figure 3-6: Technology Evaluation Cloud Based Computer Systems

Figure 3-7 shows the implications of Cloud Based Computer Systems on the supply chain



27

Figure 3-7: Implications of Cloud Based Computer Systems on SC Performance

The implications of Cloud Based Computer Systems on the supply chain performance are

characterised by:

Agility: Flexibility and speed which a cloud architecture can add to an IT environment

are the main competitive advantages of this technology [280]. In particular, this kind of

architecture can provide faster deployment of and access to IT resources, as well as

fine-grain scalability. Cloud computing platforms are becoming more pervasive in

large-scale supply chains as enterprises look to gain agility and speed in resolving

complex problems through more effective collaboration [69].

Costs: It will lead to an easier share and process of information. Moreover, it provides

opportunities to further reduce costs due to sharing resources and information between

such platforms and different supply chain actors. Capital costs for supply chain

management software can be converted to operational costs, further enhancing the

cash flow of the company [11].

Transparency: The supply chain will become more visible due to easier and cheaper

communication. Companies have the opportunity to observe supply chain real-time

events, consequently dealing with possible problems or deviations in plans on-the-fly

[11].

Responsiveness: Cloud based computer systems will lead to an inherent

strengthening of business continuity and disaster recovery capability. Supply chains

will be able to react to changing conditions due to the increased ability to gather real-

time data and to connect with other partners.

Reliability: Generally, cloud based computer systems may not be as reliable as

specialised ICT systems. However, reliability is increasing systematically, given recent

successful applications in industry. From the logistics sector’s point of view the

reliability is negative with respect to data quality, as data cannot be further influenced.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


28

Sustainability: Cloud based computer systems are sustainable because multiple

partners in a supply chain will be able to share IT infrastructure. Cloud computing can

be considered as an emerging ‘green’ IT that can assist companies in improving their

operations’ efficiency and lessen their energy costs, as well as their environmental

impact [11]. The virtualisation offered by cloud technology leads to considerable

improvement of energy efficiency by leveraging the economies of scale connected with

the large number of organisations that share the same cloud infrastructure [11].

Moreover, through visibility, companies could optimise their inventory routes based on

real-time events and thus reduce emissions that are harmful for the environment [11].

However, there can also be seen an increase in carbon footprint due to the required

infrastructures in the data centres of the major cloud providers [51; 52].


Application examples of Platform as a Service (PaaS)

Example 1: Predix Platform is provided by GE Digital and implemented for example by

Exelon Colorado Bend Energy Center improving the reliability and availability of power

plants [271].

Example 2: Accenture Cloud Platform is implemented by GRTgaz, the leading natural

gas transmission system operator in France and one of the largest in Europe. Using the

platform GRTgaz has shortened release cycles from 8-12 weeks to 10 days, achieved

20 percent faster incident resolution during user acceptance testing phases, and

improved overall service levels. [6]

Application examples of Software as a Service (SaaS)

Example 1: Pfizer uses Oracle Health Sciences InForm Cloud Service and the Oracle

Siebel Clinical Trial Management and Monitoring Cloud Service to manage and monitor

its more than 300 clinical trials per year. The system gives Pfizer better control over its

data and provides measurable efficiency and productivity gains in data management

and remote monitoring. [263]

Example 2: The TIC Platform is a data and service platform developed to provide

information on all mobility options available in Berlin. The platform includes real-time

data from the traffic information center, mobility operators and infrastructure providers.

[112]

Example 3: Popular SaaS offering types include email and collaboration, customer

relationship management, and healthcare-related applications. Large enterprises have

started building SaaS as an additional source of revenue in order to gain a competitive

advantage. [24]

Application examples of Business Process as a Service (BPaaS)

Example: The Send Invoice Use Case relates to the implementation of a BPaaS which

is linked with the Customer Relationship Management (CRM) of the BPaaS customer,


29

to obtain client information. This client information is then used to build invoices and

send them via email. [201]

Application examples of Infrastructure as a Service (IaaS)

Examples: Amazon Web Services (AWS), Cisco Metapod, Microsoft Azure and Google

Compute Engine (GCE) are examples for IaaS [24].


Technological Gaps:

One technological gap of cloud based computer systems is the lack of interoperability caused

by the pooling of resources. The reliability of servers in the cloud and down times caused by

server failures, leading to business disruptions are other technological gaps [189].

The number of data centres involved increases technical complexity and associated costs, so

that cost management becomes a big issue for this technology. The lack of customisation is

another gap of cloud based computer systems [11].


The main implementation challenges of cloud based computer systems are data security,

protection and privacy [11; 172]. These are important concerns, especially when the data is

confidential in nature [189]. Therefore, the access to remote entities has to be controlled to

maintain the confidentiality of network communication and proper authentication techniques

and encrypt objects are required to solve the privacy problem [174; 189].

System availability, reliability and speed are also important challenges for implementation [11;

172]. Hence, the optimisation of servers’ capabilities and an increase on exchange

performance is necessary. Cloud based computer systems need to perform recovery,

reconfiguration and scalability [344].

Internet of Things

The Internet of Things (IoT) consists of autonomous collection of, exchange of and action on

data from a network of physical devices embedded with sensors, software, network

connectivity, and computer capability, ranging from devices and vehicles to appliances [277].

The use of IoT in the manufacturing and industrial sectors is called Industrial Internet of

Things.

Components of the Internet of Things are Sensor Technologies, Machine to Machine (M2M)

Communication, Cyber-Physical Systems and Process Intelligence.

Sensors can be used as a useful data source for capturing information about product, machine

or environmental properties and behaviour. Provided especially by microelectronics and

improved computation, the production of miniaturised (electronic) structures allows for more

energy-efficient sensing devices [302].

Machine to Machine (M2M) communication allows any sensor to communicate virtually,

referring to any wired or wireless machines communicating with one another [279]. Thus, the


30

systems can monitor themselves and automatically respond to changes in the environment,

resulting in a reduced necessity for human involvement.

A cyber-physical system (CPS) is a system which is controlled or monitored by computer-

based algorithms and is integrated with the internet and its users. Cyber-physical systems

integrate computing and physical processes with the key focus on the connection between

computational and physical elements [316]. Therefore, CPS are equipped with sensing,

computing, actuating and communicating capabilities, providing data, information and services

to their local or cloud-based environment. Next to the general technology of CPS, mobile

cyber-physical systems have gained increasing acceptance in recent years using the

advantages and extend the application domains of CPS [146].

Process intelligence is also a component of IoT and is defined as the analysis of data regarding

a business process. Process intelligence gives an insight and helps to understand processes

and operations by combining data and metrics to procedures in business processes. The aim

of process intelligence is to identify bottlenecks and exceptions that could endanger regulatory

compliance. [299]


The technology Internet of Things was evaluated based on expert workshops and literature

review. As shown in Figure 3-8, the TRL Category is B (Market-ready) based on the level of

application examples, the technological gaps and implementation challenges. For the

manufacturing and logistics sector the applicability of IoT is broad, while the process industry

sector sees a moderate applicability.

Figure 3-8: Technology Evaluation Internet of Things

Figure 3-9 shows the implications of Internet of Things on the supply chain performance for

the three sectors (Manufacturing, Process and Logistics).


31

Figure 3-9: Implications of Internet of Things on SC Performance

The implications of the Internet of Things on the supply chain performance are characterised

by:

Agility: The technology enables the easier and faster collection and processing of data

to monitor critical parameters. The optimisation of operational efficiency and

rationalisation, automation, autonomous maintenance decisions and actionable

intelligence are possible with the IoT [243; 253]. Overall, more connected systems

potentially lead to more flexibility as well as agility and thus to a significant

improvement in the supply chain.

Costs: The use of IoT reduces costs because of better use of resources and better

decision making processes. IoT can be applied for predictive maintenance and is able

to cut labour costs [230]. In the logistics sector, it will be used to find the best way to

ship goods, manage storage decisions much more efficiently due to tracking abilities,

and will reduce costs since problems are eliminated through inventory record

inaccuracies.

Transparency: The IoT will lead to more transparency in all sectors. Objects and

systems are better connected, so that tracking and tracing is easier and information

can be shared without much effort. The monitoring of all production steps will be

possible. The application of IoT in a petrochemical industry can improve personnel and

material location and area intrusion detection for engineering operation, safety

monitoring and personnel orientation of hazardous areas [208].

Responsiveness: The IoT leads to better responsiveness because there is no need for

human assistance to make decisions in connected systems. The decisions are based

on processes.

Reliability: Due to IoT, it is easier to access resources that are in good working

conditions, to spot and react faster to problems, and to increase access to more

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


32

information and alternative resources. Predictive maintenance, which is enabled by

IoT, will help in avoiding any break on the chain by using real-time data [97].

Sustainability: The IoT will lead to an energy reduction and to cheaper and more

efficient alternatives due to better decision making and optimisation of processes. It

will also enable a safer working environment [97]. The use of sensors and CPS can

have a positive effect on the implementation of the policies for a sustainable

manufacturing as they can enable real-time monitoring of, for example, air, pollution

and CO2 level and adapt them to sustainability targets [30].


Application examples of Sensor Technologies

Provided especially by microelectronics and improved computation, the production of

miniaturised (electronic) structures allows for more energy-efficient sensing devices,

addressing a wider range of applications [302]. The use of sensor technology for containers,

loading tools, transport, or in warehouses, is becoming increasingly attractive due to cost

reduction potential. These sensors provide a useful data source for capturing information

during shipment about temperatures, vibration, humidity, etc. In addition, machine or device-

specific sensors can be used for monitoring the operating conditions and for triggering

appropriate actions such as maintenance, change of operating conditions, or necessary

continuation of processing [196; 302]. By 2030, sensors connecting human and natural

environment are estimated to reach 100 trillion, entangled in a global distributed intelligent

network [284].

Application 1: Pressure sensing

A pressure senor detects pressure and converts it into an electric signal whose quantity

depends on the pressure applied. Pressure sensing is used, for instance in weather

instrumentation, aircrafts and vehicles. Pressure sensors can also be used to measure other

variables such as fluid/gas flow, speed, water level, and altitude. [269]

Application 2: Motion sensing

Motion sensors detect changes of positions of objects in their measurement range. They use

data processing algorithms designed on a motion interaction platform which integrates

numerous low-cost micro-electro-mechanical system (MEMS) motion sensors with a wireless

technology to carry personified interactions while working together with machines. They are

used to measure static acceleration (gravity), tilt of an object, dynamic acceleration in an

aircraft and vibration of an object. They are implemented in cell phones, washing machines

and computers, for example. [269]

Application 3: Temperature sensing

A temperature sensor collects information concerning the temperature from a resource and

changes it to a form that can be understood by another device. Temperature sensors are

weatherproof and designed for continuous temperature measurement in air, soil, or water.


33

They are exceptionally accurate, as well as stabile and, therefore, suitable for measurements

in complex industrial applications and rough operating conditions. [269]

Application 4: 3D scanning

3D scanning describes the process of capturing data from an undefined three-dimensional

surface using different types of sensors. An analogue-scanning probe is instructed to move

completely over the unknown surface and the system collects these information in the form of

numerical data generating a point’s cloud matrix. [319]

Example 1: With the single-shot method sensors are able to scan moving objects very

fast, which is required for scanning volume and classifying objects on conveying and

handling equipment [86].

Example 2: The capacity of a truck based on free loading metres, available cargo space

or used volume can be determined using 3D scanning [86].

Application example of Machine to Machine (M2M)

Example: Oil and gas companies (like PETRONAS) own and operate fleets of vehicles

around the world. They use M2M technology to ensure the safety of drivers and

compliance with international health and safety regulations. Using satellite and wireless

M2M technology, companies like American Innovations are helping pipeline companies

to minimise the number of drives to remote sites aimed at verifying the health of pipelines

and creating a verifiable record. [321]

Application examples of Cyber-Physical Systems

Applications of cyber-physical system are present in transportation, smart home, robotic

surgery, aviation, national/regional defence, security, critical infrastructure, etc. These

applications positively affect manufacturing in the form of cyber-physical production systems

in process automation and control [359]. They allow to raise the amount of computation done

per device, and, thus, increasing the level of intelligence in individual devices [302].

Example (Cyber-Physical Systems): General Eletric (GE) digital division aims to guide

customers’ digital transformation through Predix, a digital platform. On Predix platform,

GE is developing products and services for dealing with cybersecurity, cloud computing,

Internet of Things and Digital Twins. For example, every turbine that is marketed by GE

has their own Digital Twin that is upgraded in real-time with data gathered from the

sensors. This way, GE is able to predict possible failures and monitoring the turbine’

status in real-time. [272]

Example (Mobile-Cyber Physical Systems): As a representative application domain in

Intelligent Transportation System (ITS), vehicular networking systems (VNS) have been

a significant research area in CPS and mobile CPS. However, due to the inherent

mobility of Mobile CPS, VNS in Mobile CPS could diversify its application aspects.


34

Application examples of Process intelligence

Process intelligence (PI) can be implemented in any business environment, and thus helping

to improve processes in every sector. It gives real-time insights into processes, so that PI can

be used for monitoring systems, for instance. [299]

Example: The iBin by Würth monitors filling level and additional information on the

order/parts in real-time, and communicates these information to the control system for

taking action automatically [374].


Technological Gaps:

Major gaps of IoT concern the data network, data processing (integration in IoT platforms),

data analytics (apart from cloud-based solutions) as well as data sharing, which have to be

extended. Therefore, an increase of security and trust is needed. Infrastructure in sufficient

frequencies is often not available. [236]

Difficulties in integrating heterogeneous devices and applications must be solved by

developing standards, which is also needed for boxes, containers and physical handling. The

devices have limited wireless communication, computing capabilities and energy sources and

need to be identified more efficiently. Overall their reliable interoperability has to be increased.

[236]

There are limitations of the current internet architecture in terms of mobility, availability,

manageability and scalability, as well as low platform expendabilities. In general, high

interference and unreliable links prevent the extension of using IoT devises [380].

The data billing (based on fixed fees, usage, or other metrics) of the end-users is generally

missing [236]. Network operation, business processes, governance and benefit models have

to be adjusted accordingly to close the gaps.

For sensor technologies there is a need for systems that employ advanced sensor fusion and

feature extraction techniques for reliable process-state determination and diagnostic decision

making [70]. New technologies, which are process-specific and suitable for non-traditional

processes and materials, should be developed. Furthermore, scanning and identification

precision of the different objects has to be increased. Vehicles for speeding industrial

evaluation and commercialisation of new sensing technologies are needed. Additionally, there

is a lack of digital signal techniques that can accommodate uncertainty in sensor-produced

data. Moreover, errors in measurement still occur, which depend on the accuracy of digital

instrument tools with which measurements are taken, both in terms of hardware as well as

software components. Overall, the sensors’ reliability has to be increased [219]. In the

detection of humans, sensors must not affect their health through radiation transmission of

signal waves.

For Cyber-physical systems, it is necessary that appropriate connected systems exist, such

as sensor systems, which generate real-time data to deliver usable information. Emerging

CPS infrastructures as well as multiple heterogeneous devices connection lead to


35

complications in implementing the various controls and integrating the different components,

since each will need specific models and software components [212]. Some CPS cannot work

properly or efficiently without knowing the context of their systems resources. It is necessary

to further develop the communication protocols and the modelling of the processes.

Transformation of CPS data from advanced analytics to actionable knowledge, as well as a

cross-domain large-scale information integration for forward knowledge-driven decision

making have to be optimised [191]. Modularisation and certification of CPS need to be

extended. Moreover, the economic impact of CPS infrastructure cannot be designated

accurately.


Primal challenges for using IoT technologies are the interoperability of devices and their

integration into the business processes, the standardisation and integration with legacy

systems, and the solution of security, performance and stability issues [13].

IoT devices have to be easily and securely integrable to the IoT platform without a gateway.

Furthermore, unified resources and simplify usability have to be realised. To collect and

preserve high quality data, optimisations towards a robust connectivity, storing and energy

capacities, as well as choosing the right parameters and solutions for reliable fault resolving,

are required. [236]

Concerning data, full control must be handed to the owner, while fine-grained data visibility

models need to be implemented, in which the data is accessible, and the format is consistent

across multiple platforms. These platforms should be easily expandable by developers,

offering them incentives to contribute. Additionally, cross-platforms, sharing applications and

services, have to be established. Likewise, it will be challenging to develop abilities to

advertise, deliver and charge for the use of applications and data. [236]

M2M gateways have to be improved in order to make them cost and energy efficient, while

accomplishing seamless connection between internal nodes and external networks [59].

CPS design should focus on extending the life of the components using efficient and power-

aware software, alongside with hardware, and communication protocols. The software needs

to optimise operations and limit access to resources using other alternatives whenever

possible. The software used must be able to collect context data, organise it and make it

available for the CPS application to be used effectively. [238]

Lastly, high costs of devices, such as sensors, and integration costs are challenges, upon

which decision makers must be convinced in order to invest in the technology.

Distributed Ledger / Blockchain

Blockchain is characterised by being a distributed, shared and encrypted database, aimed at

providing full security for information storage, through the means of cybersecurity at

irreversible and incorruptible levels of data safety [372]. Blockchain can be used to increase

transparency through systematic and instant information exchange across different


36

stakeholders and regions. The technology is also relevant in the context of big data analytics

to ensure that the data is original [381].


The technology Distributed Ledger / Blockchain was evaluated based on expert workshops


research, for Smart Contracts) to A (Market presence, for Cryptocurrency/Bitcoin) based on

the level of application examples, the technological gaps and implementation challenges. For

the manufacturing sector, the applicability of blockchain is moderate to broad, while the

process industry sector sees a moderate applicability. In the logistics sector there is a broad

applicability of the technology.

Figure 3-10: Technology Evaluation Distributed Ledger / Blockchain

Figure 3-11 shows the implications of Distributed Ledger / Blockchain on the supply chain


Figure 3-11: Implications of Distributed Ledger / Blockchain on SC Performance

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


37

The implications of Distributed Ledger / Blockchain on the supply chain performance are

characterised by:

Agility: The distributed ledger / blockchain will have a positive implication on the supply

chain agility by reducing the settlement time as the need for intermediaries is

eliminated [179]. This will result in quicker transfer of assets’ ownership, decrease of

the complexity in supply chains and the ability to ensure data significance and provide

support on regulatory terms [96].

Costs: Decreases of process times and errors and the resulting cost reduction are key

elements achieved through increased digital efficiency. Operating cost will decrease

due to less administrative costs and faster validation, and thus, contract management

costs will be cheaper as well. [67]

Transparency: Blockchain enables non-restrictive data transparency throughout the

entire supply chain, and allows standardised and transparent business processes

[209]. Information can be uploaded and verified by multiple users simultaneously,

resulting in much faster and more open communication.

Responsiveness: Due to the increased and faster sharing of information and the instant

access to data, blockchain will dramatically accelerate the processes [96].

Reliability: The decentralised character of blockchain secures high reliability and

guarantees data availability. Blockchain characteristics involves the fact that data,

once created in the blockchain, is peer reviewed and unchangeable. Therefore,

blockchain is well-known for its data integrity and security characteristics. [210]

On the other hand, the immutability of blockchain technology contains the issue that

false data (e.g. due to human errors) is hard to fix [5], which reduces the positive

evaluation slightly.

Sustainability: The blockcchain technology will lead to a better use of resources due to

being less paper intensive and achieving faster operations. Furthermore, the

blockchain technology allows to monitor environmental compliance and to avoid

environmental pollution [185]. On the other hand, the use of blockchain technology has

also negative implications on the sustainability since the present algorithms used for

blockchains consume an exorbitant amount of energy [364; 379].


Application examples of distributed ledger / blockchain

Application 1: Smart Contracts

A smart contract is a computer program or protocol for facilitating, verifying or executing the

terms of a contract working in a decentralised immutable and irrevocable ledger technology.

Smart contracts enable automatic control of information. When the criteria of the contract are

fulfilled, the ownership or payment will be automatically transferred. The smart contract

maintains a record of all versions and modifications of the contract. [131]


38

Example 1: Finboot is a start-up that helps companies in smart contract systems in

different field of application such as Logistics or Legal issue [121].

Example 2: Ethereum is a decentralised platform that runs smart contracts. It uses

decentralised applications that run on a custom-built blockchain. With the Ethereum

Wallet the user is able to hold and secure ether (form of payment on the platform) and

other crypto-assets built on Ethereum, as well as write, deploy and use smart contracts.

[110]

Application 2: Cryptocurrency

A cryptocurrency is defined as a digital or virtual currency using cryptography for security

purposes. Due to its security characteristic, a cryptocurrency is difficult to forge. Furthermore,

a cryptocurrency is not issued by any central authority making it theoretically immune to

government interference or manipulation. [48; 186]

Example: Bitcoin has been the first and, so far, the most widely used cryptocurrency. All

transactions with bitcoins are mapped in an assigned database, combined into blocks

and linked in such a way that a complete and forgery-proof sequence of transaction

blocks (blockchain) is created. [216]

Other application examples of blockchain in general

Example 1: Hyperledger Fabric offers the possibility to use components (e.g. consensus

and membership services) plug-and-play for applications or solutions with a modular

architecture [170].

Example 2: Ondiflo is a platform built on Ethereum Blockchain. The aim of the platform

is to automate and improve management and delivery of the order-to-cash ticket-based

services to the Oil & Gas industry, resulting in significant efficiency gains and cost

savings. Ondiflo provides a verifiable and trusted tradable tool for supplier financing, so

that suppliers are paid faster. [262]

Example 3: A group of energy companies (i.a. BP and Royal Dutch Shell) is creating a

blockchain-based digital platform for energy commodities. This platform, which will be

launched at the end of 2018, will help to eliminate any confusion over ownership of a

cargo and possibly improve risk management. Furthermore, it will provide reduction of

administrative operational risks and costs of physical energy trading, and improvement

of the reliability and efficiency of back-end trading operations. [283]

Example 4: Blockchains are used to gain efficiency in the procurement, transportation

management, track and trace, customs collaboration, and trade finance. A major area

of focus for efficiency gains is ocean freight. Maersk and IBM have launched a global

blockchain-based system for the digitalisition of trade workflows and end-to-end

shipment tracking. [168]

Example 5: DHL and Accenture are driving a blockchain-based serialisation project that

provides sophisticated track-and-trace capabilities to the pharmaceutical industry facing

the problem of false medication. [85]


39

Example 6: ShipChain has designed a comprehensive blockchain-based system to track

and trace a product from leaving the factory to the final delivery to the customer [306].


Technological Gaps:

There are some technological gaps for blockchains regarding scalability. The current potential

throughput of issues in the bitcoin network is maximised to 7tps (transactions per second) and

there is a limitation in the number of transactions that can be handled at the same time. [377]

Data security and vulnerability are other important gaps for the blockchain technology. For

smart contracts and cryptocurrency there is still the risk of being hacked and the contingency

of manipulation. [377]

Another problem of the current blockchain technology is the massive energy consumption

required for the proof of work concept ($15million/day). [377]


The technological gaps, such as scalability and security, lead to countless implementation

challenges. Therefore, there is the need for development of standards and governance of the

blockchain in each industry, as well as improving blockchain technology itself, in order to

overcome the current technical limitations. Other challenges for implementation are

organisational and cultural, and the lack of willingness to share and exchange information that

may not be under complete control by the user or consumer. [92]

Thus, enterprise acceptance and the willingness of companies to disclose information and

ensure full transparency will be very important for the success of the technology [377].

In general, the blockchain technology requires hybrid specialised knowledge in IT and

legislation.

Other challenges for implementation of smart contracts are the allocation of authorities (write

permissions) and the standardisation of interfaces (interoperability) [68].

Furthermore, the social acceptance, price volatility [58] and the aligning of users on a common

cryptocurrency are specific implementation challenges for the cryptocurrency technology.

Artificial Intelligence

Artificial intelligence (AI) is a general term for information systems that are inspired by

biological systems and mimic the human thought processes and senses. AI contains multiple

sub-technologies, including machine learning, deep learning, Natural Language Processing

(NLP), and strong AI. [342]

Machine Learning is a current application of AI based around the idea that it is possible to give

machines access to data and let them learn for themselves directly from examples, data, and

experience [335]. Deep learning provides advanced analytics tools for processing and

analysing big data [358].

Natural Language Processing (NLP) is a tract of Artificial Intelligence and Linguistics, devoted

to make computers understand the statements or words written in human languages [197].


40

The main principles behind strong AI are neural networks and self-learning (auto machine-

learning) capabilities [9]. Using strong AI, computers can optimise their own behaviour based

on their past behaviour and experiences [167].


The technology Artificial Intelligence was evaluated based on expert workshops and literature



manufacturing and the logistics sector the applicability of artificial intelligence is broad, while

the process sector sees a moderate applicability.

Figure 3-12: Technology Evaluation Artificial Intelligence

Figure 3-13 shows the implications of Artificial Intelligence on the supply chain performance

for the three sectors (Manufacturing, Process and Logistics).

Figure 3-13: Implications of Artificial Intelligence on SC Performance

The implications of Artificial Intelligence on the supply chain performance are characterised

by:

Agility: AI will have a positive implication for the agility of supply chain due to the

comfortable adjustment of external parameters.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


41

Costs: AI will lead to cost reduction because of the cycle time and scrap reductions

and the improvement of resource utilisation. It will lower inventory, transportation,

labour and disruption management costs. [232]

Transparency: AI might have a positive implication for the transparency because of

more automated control and storing and sharing of data and information. For example,

AI will enable the smart tracking of goods in the supply chain. Otherwise, the decision

making processes of AI might lead to a lack of transparency due to the complex

algorithms which are often seen as a black box [93; 166; 202]. Therefore, the

implication on the supply chain transparency will depend on how algorithms and

learning processes are disclosed.

Responsiveness: AI can make forecasting and scheduling faster and more accurate

due to increased ability to generate, collect and process data and information and due

to minimising human intervention [29]. Therefore, it will improve process response time

and optimise the entire supply chain process.

Reliability: Depending on whether the algorithms are properly designed or not, the

system will be more or less reliable. In general, the trend is that they will be more

reliable due to the ability of processing more data and the ability to recognise patterns

while adapting themselves accordingly. Thus, they will decrease errors while

harmonising and increasing processes.

Sustainability: In general, the better use of resources will contribute positively to

sustainability. Furthermore, AI will help supply chains to prepare for extreme weather

events and disasters due to early warning systems [370].


Application examples of machine learning

Example 1: Google’s Cloud AutoML uses machine-learning implications to automatically

build and train a deep-learning algorithm that can recognise things and images. The

service has been tested for example by Disney who used the service to develop a way

to search its merchandise for particular cartoon characters. [199]

Example 2: In the supply chain, AI algorithms are able to detect patterns and thus

forecast demand for products over time, geographic markets, and socioeconomic

segments considering, for example, macroeconomic cycles, political developments and

weather patterns [100].

Example 3: To predict air freight transit time delays, DHL has developed a tool based

on machine learning. The tool allows a prediction of the average daily transit time a week

in advance, and therefore, making possible proactive mitigation by analysing 58 different

parameters of internal data (e.g. departure day or operational factors like airline on-time

performance). [91]


42

Application examples of deep learning

Example: The Drive.ai software developers use machine learning and deep learning

algorithms to power computer vision, which allows the vehicle to make decisions in ways

that are similar to human decision making. The developer team uses a managed

workforce to transform unstructured data from raw images into structured data using

bounding boxes to annotate objects such as road signs, traffic lights, and pedestrians.

[66]

Application examples of Natural Language Processing (NLP)

Example: Devices such as the Amazon Echo and Google Home are examples of

products using Natural Language Processing (NLP) technology to react to human

instructions [223].

Application examples of strong artificial intelligence

Example: Cogito is one example of behavioral adaptation to improve the emotional

intelligence of customer support representatives. The company is a fusion of machine

learning and behavioral science to improve the customer interaction of phone

professionals. [9]

Applications of Artificial Intelligence in general:

Application 1: Financial Anomaly detection

Logistics service providers often rely on third parties like carriers, subcontractors, charter

airlines, and other third-party vendors to operate core functions of their business which leads

to millions of invoices annually. AI technologies like natural language processing are able to

extract critical information unstructured invoice forms received by the company. [91]

Example: Ernst & Young (EY) applies such an approach to detect fraudulent invoices

using machine learning to thoroughly classify invoices from international parties and

identifying anomalies for expert review [91].

Application 2: Intelligent Robotic Sorting

Intelligent Robotic Sorting is the effective high-speed sorting of letters, parcels, and even

palletised shipments [91].

Example: ZenRobotics is producing intelligent robotic sorting systems combining

computer vision and machine learning algorithms integrated into commercially available

robot arms. Capturing real-time data from three different cameras and sensor types, the

AI engine is trained to identify different products and packaging by recognising logos,

labels, and 3D forms. [91]

Application 3: AI-Powered Visual Inspection

AI-Powered Visual Inspection is the use of cognitive visual recognition capabilities to perform

maintenance of physical assets [91].


43

Example: IBM Watson uses a camera bridge to photograph cargo train wagons, and

identify damage, classify the damage type, and determine the appropriate corrective

action to repair these assets [91].


Technology Gaps:

A major gap of artificial intelligence is that the used algorithms have difficulties to understand

the context of the data that they are handling. The patterns and correlations found by the

algorithms often go unnoticed to human experts, so that decisions may even confound the

engineers who created the algorithms. [94]

For some AI systems missing creativity might also be a problem. The technology, especially

natural language processing, has not progressed far enough currently [355]. The high costs

of implementation are also a key gap for AI.

For these reasons, the lack of human experts with specialised knowledge is another

technology gap [75].

Furthermore, the lack of standards and regulations for artificial intelligence is causing

problems [91] and the unknown consequences from using Quantum Computers with auto

machine-learning and self-learning may lead to social problems.


Important issues for artificial intelligence are the data collection, access and processes. AI

algorithms work best when there are vast quantities of data, available in real-time. Therefore,

a high data quality and availability and the implementation of the right algorithms and learning

processes, are the biggest challenges for AI [373].

Data security, digital security enhancements and privacy concerns are further implementation

challenges [295].

Regarding the technological advancement of AI, close collaboration between industry and

research is necessary to adopt new reliable technologies. Because of the complexity of the

algorithms, the understanding of AI and its impact is a big challenge, especially for the

management. [373]

From a social point of view, the automation or “robotisation” is another implementation

challenge for individuals, societies and governments to jointly develop mechanisms to adapt

to these changes [91].

Data Science

Data Science is the application of quantitative and qualitative methods to solve relevant

problems and predict outcomes using algorithms aimed at creating or extracting new

information out of vast amounts of data. The fields of application are numerous and two sub

technologies have been identified: Data Storage and Big Data Analytics. In fact, to extract

some insights from big data there are 5 stages: (i) acquisition and recording, (ii) extraction,

cleaning and annotation, (iii) integration, aggregation and representation, (iv) modeling and


44

anlysis and (v) interpretation. The first three stages form the sub-process data management

(and storing), while the last ones form the data analysis [129].

Big Data Analytics is the combination of theories, technologies, tools and processes which

allows a deep data understanding and offers the possibility to unravel new potential actions

based on data gathering [54]. Therefore, the aim of big data analytics is to draw conclusions

and insights from vast amounts of data and acquire intelligence from Big Data [129]. In many

cases, the data is formed by various structures and formats and semantic information about

origin and context of a data set is normally not given. The main types of Big Data Analytics

are descriptive, diagnostic, predictive and prescriptive [173]. Descriptive analytics summarises

and converts data into meaningful information for reporting and monitoring. Diagnostic

analytics goes deeper and tries to understand why something (a problem or event of interest)

has happened. Predictive analytics uses data mining, statistical algorithm and machine-

learning techniques to predict future results based on data [147]. Prescriptive analytics

recommends how to act for taking advantage of the circumstance and serve as a benchmark

for an organization’s analytics maturity and thus, evaluates and determines new ways to

operate and target business objectives while balancing all constraints [78]. Prescriptive

analytics includes simulation and optimisation [357]. Simulation is defined as a tool used to

evaluate the performance of an existing or proposed system under different configurations

and over long periods of real-time [222]. Optimisation refers to the study of decision problems

to minimise or maximise a function by systematically choosing the values of variables within

their allowed sets.

Data Storage is an important prerequisite for Big Data Analytics. The challenge of data storage

is to store a vast amount of data with the aim to allow fast, parallel and fault-tolerant access

to the data. Data storage considers how to store and distribute data that data analytics have

access to. [195; 265]


The technology Data Science was evaluated based on expert workshops and literature review.

As shown in Figure 3-14, the TRL Categories range from C (Applied research, especially for

prescriptive analytics models) to A (Market-presence) based on the level of application

examples, the technological gaps and implementation challenges. For the manufacturing and

the process sector the applicability of data science is broad, while the logistics sector sees a

moderate to broad applicability.


45

Figure 3-14: Technology Evaluation Data Science

Figure 3-15 shows the implications of Data Science on the supply chain performance for the

three sectors (Manufacturing, Process and Logistics).

Figure 3-15: Implications of Data Science on SC Performance

The implications of Data Science on the supply chain performance are characterised by:

Agility: Being able to monitor changes in the business environment (demand, capacity,

raw materials availability and prices, disruptions, etc.) and obtaining useful information

will make the supply chain more agile. The prediction of trends and behaviour will lead

to an improvement of the performance and a faster decision-making process.

Costs: The application of data science will reduce costs due to more accurate

forecasting (lower sourcing and inventory costs) and prevention of disruptions [7].

Simulation can help companies to minimise the cost of physical testing, develop new

technologies, evaluate novel concepts and assess product performance in a low-risk

virtual environment. However, there will be increased costs due to collection and

storage of huge amounts of data and working with unstructured data, but these costs

will constantly and swiftly decrease since there is technological advancement of hard-

and software [229].

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


46

Transparency: If the firms are able to collect, store and process large amounts of data

and can share it efficiently, it will lead to more transparent supply chains. For example,

analytics will give process engineers greater visibility into processes supporting better

management [118].

Responsiveness: Data science will lead to the ability to monitor changes and make

recommendations based on information generated from data. Predictions will be more

accurate, resulting in prescriptive policies showing how to react to changes regarding,

for example, demand, supply, capacity and production. Therefore, there is the

possibility for faster and better business decisions and to uncover new business

opportunities.

Reliability: As long as the data sources are available, and the information generated is

accurate, data science will lead to more resilient systems that are less prone to

disruptions. Predictive capabilities will raise alarms before the adverse events happen

and prepare the system to be functioning properly. Being able to get data from many

sources in large quantities will increase the reliability of the information. Simulation

models permit to decrease downtimes (e.g. planning the maintenance project in a

virtual world) [311].

Sustainability: The use of data analytics has the potential of better agricultural

practices, use of land, waste management, and therefore positive contribution to

sustainability. Collecting and understanding data using technologies like big data

analytics help companies to know how the organisation operates and have an

implication on sustainability. Data science helps to understand the entire end-to-end

impact of their businesses, throughout the value chain. The worlds of data collection

and analysis drive transparent and improved sustainability performance for companies

and their supply chains. [164]


Application examples of Big Data Analytics

Descriptive / Diagnostic Analytics

Example: IBM Watson is a software by IBM that independently manages large sets of

data, discovers relationships, tests correlations, develops dashboard for visualising

information. For example, Woodside Energy is an oil and gas Australian industry and it

uses this system which allows its employees to find detailed answers to highly specific

questions—even on remote oil and gas facilities. Watson ingested the equivalent of

38,000 Woodside documents — this would take a human over five years to read. [169]

Predictive Analytics

An application example of Predictive Analytics is the prediction of how many products will be

sold in one month, or one year. These predictions are based on correlations and patterns in

past data. A simple predictive model can be a linear regression model that assumes that the

average number of sales per day decreases each month. More complex models can take into

account other aspects that could influence the number of products that will be sold.


47

Example: An example for using predictive analytics software to forecast demand is

BlueYonder. They are using different internal and external data enabling demand

forecasts and automated replenishment decisions. It is especially used by food retailers

or mail-order companies. [40]

Prescriptive Analytics

Example: Prescriptive analytics can be used for route optimisation in the logistics

industry. UPS is analysing and combining hundreds of data source that can push

10.000s route optimisations per minute to all of their trucks. ORION (On-Road Integrated

Optimization and Navigation) uses fleet telematics and advanced algorithms to optimise

routes for each driver to deliver the assigned packages every day. [15; 348]

Example (Simulation): The use of prescriptive analytics can result in a collaborative

Shadowed Supply Chain which describes the real-time integration of system information

in the digital twin of the supply chain due to continuous data acquisition, provision of the

data on a decentralised information platform and assistance systems for data evaluation

using discrete event simulation. [267]

Example (Simulation): Siemens SIMIT software can virtually test a complete production

plant before construction begins – including all associated components, technical data,

motors, pumps, and gear units [312].

Example (Simulation): Oil and gas simulation with AnyLogic ensures effective change

implementation by enabling analysis, optimisation, and experimentation in an

environment that can fully capture the details of your operations [19].

Example (Optimisation): IBM utilised non-linear optimisation with non-linear constraints

to run simulations on historical data, creating the ability to predict emulsion rate days

ahead with over 90% accuracy, in order to improve the performance of Steam Assisted

Gravity Drainage [171].

Application examples of Data Storage

Example 1: Cassandra is a column-based database system, which provides a non-

central and fault-tolerant database with an abstraction similar to SQL and relational

databases [20].

Example 2: In contrast to Cassandra, Mongo DB is a document-oriented data storage,

which allows users to store documents with different structures in the same collection

[241].

Example 3: The Hadoop file system developed for one of the first data analytics

frameworks, aims to provide a distributed file system across multiple hosts in a cluster.

To organise the distribution of files to the hosts, Hadoop file systems uses a central

management host. [21]

Example 4: Apache Spark provides a unified framework to analyse stored data (offline

data analytics) as well as real-time data (online data analytics). Apache Spark is based

on the idea to split data in partitions, to execute computational steps in parallel on those


48

partitions and to aggregate the results into an overall result for the mass of data. The

advantage of Spark compared to similar technologies (e.g. Hadoop) is that Spark allows

an in-sequence execution of multiple computation steps on a data partitions without a

transfer of the partitions to a persistent storage. [22]


Technology Gaps:

The need for specialised workforce is growing, but statistical and technical skills related to

data science are still rare when considering the entirety of computer science specialisations

[31; 75; 317].

Another technology gap is the collection, storage and processing of the vast amounts of data

itself. Terabytes of data will take large amount of time to get uploaded in cloud. Moreover, this

data is rapidly changing rendering it hard to be uploaded in real-time. Most data storage

technologies provide a different interface and abstraction as relational databases, which

increases the complexity in adapting existing applications. At the same time, the cloud's

distributed nature is also problematic for big data analysis. Heterogeneous and incomplete

data are big problems as the algorithms expect homogeneous data and cannot understand

nuance. Therefore, it is not possible to devise absolutely foolproof, fully reliable fault tolerant

machines or software. [332]

Cybersecurity problems are another major technology gap of data science, given that strategic

information may be hackable [195].


Regarding the technology gaps, the main implementation challenges are the reduction of the

probability of failure to an acceptable level, and the creation of storage and processing

capabilities for the vast amounts of data. Therefore, the definition of the IT architecture, the

easy access to and use of data sources are decisive [207]. The testing of the data-intensive

algorithms and systems will also be a big challenge [235].

Another major implementation challenge is the training and development of data scientists

[356].

As the application of data science increases the complexity in data and computation

governance, the control of data integrity, security and distribution, as well as the development

of standards are other important challenges for implementation [207].

Mobile and Wearable Devices

Mobile or wearable devices can be defined as devices that are autonomous, non-invasive,

and perform a specific function such as monitoring or support, over a prolonged period of time.

A mobile device is a portable device like a handheld computer or smartphone. Smart wearable

devices support tasks of human workers by facilitating work steps in many different fields such

as logistics, production processes specially assembly and also in design phase and product

development for the manufacturing sector. The term ''wearable'' implies that the support

environment is either the human body or a piece of clothing [123]. Smart clothing or electronic


49

textiles are fabrics in which electronics and connections are woven and which are

characterised by physical flexibility and small size [327]. Smart clothing revenue is expected

to raise from $17.2 million in 2013 to $603 million by 2020, mainly from sports applications

[343].


The technology Mobile and Wearable Devices was evaluated based on expert workshops and

literature review. As shown in Figure 3-16, the TRL Category is B (Market-ready) based on

the level of application examples, the technological gaps and implementation challenges. For

the manufacturing and the logistics sector the applicability of mobile and wearable devices is

moderate to broad, while the process sector sees a broad applicability.

Figure 3-16: Technology Evaluation Mobile and Wearable Devices

Figure 3-17 shows the implications of Mobile and Wearable Devices on the supply chain


Figure 3-17: Implications of Mobile and Wearable Devices on SC Performance

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


50

The implications of Mobile and Wearable Devices on the supply chain performance are

characterised by:

Agility: Being able to monitor system parameters easily in real-time or automatically

generate such information due to mobile and wearable devices will make supply chains

more agile. Quality control will be easier and more accurate due to default detection.

However, for the manufacturing sector, smart devices insertion may lead to lack of

agility as machinery is still not adapted for this technology, and wearables have seen

little mechanisation efforts.

Costs: Due to more efficient processes and faster actions regarding changes in the

environment costs will be reduced. Accurate quality control will prevent defaults in the

process [55]. However, data visualisation on smart glasses must be adapted to each

process, which causes corresponding investment costs.

Transparency: Mobile and wearable devices will enable data capture in real-time and

more information will be shared and visualised automatically achieving excellent

control of machines and supporting manufacturing and logistics operations. This will

lead to a higher transparency in the processes. [150]

Responsiveness: Reacting to changes will be faster due to the easier real-time

validation of relevant information. Visual and context-sensitive information displayed in

the field of vision of the human workers ensures concentration on the most essential

and increases the performance while reducing the susceptibility to errors. However,

there are some issues regarding failed data transfers, missing communication

protocols and the small screen sizes and resolutions of the wearable devices which

could implicate the responsiveness negatively [27].

Reliability: In general, visual illustration of the data ensures high reliability in the

processes. This also refers to the susceptibility to errors, which can be considerably

reduced using mobile and wearable devices. However, mobile and wearable devices

are less tolerant to failures when compared to normal products.

Sustainability: Wearable devices can contribute to the safety of the workers, especially

in the process industry [221], which will improve the sustainability of the supply chain.

However, the disposal of such devices is more complex compared to normal materials

and recycling is almost impossible.


Application examples of smart wearable devices

Example 1: DHL introduced the use of smart glasses to enable “vision picking” where

staff is graphically guided through the warehouse. The pilot proved that augmented

reality offers added value to logistics and resulted in a 25 percent increase on efficiency

during the picking process. [84]

Example 2: The logistics sector has begun using wearable barcode scanner gloves

called ProGloves to simplify work that does not involve the use of hands [273].


51

Example 3: Warehouse control systems can be programmed into a smartwatch to boost

productivity and support data capture and analytics. Managers can also keep an eye on

key performance indicators and react faster for better-informed decision-making. [367]

Example 4: Deloitte Wearables is a Canadian company with a mining-site-focused

wearable project. They are targeting safety goals with another type of wearable smart

helmet. This new wearable device is lightweight, contains sensors to detect levels of

hazardous gases in the air and other sensors depending on the type of mine. The helmet

facilitates communication between managers and miners with yellow to red lights for

emergency [221].

Example 5: BMW announced the use of glasses and headphones to give to the workers

exact instructions about how to repair a car, while at the same time they can ask for

information about what tool is right for the next step of assembly or repair [150].

Example 6: Smart footwear devices can provide companies with an opportunity to

monitor the health and safety of workers, for example in transportation and warehousing

areas. By means of the devices, the workers can be located in an emergency, hazards

can be identified and the level of physical strain to which workers are exposed can be

measured to ensure that they do not lift too heavy loads. [4]

Application example of smart clothing or e-textiles

Example: Google and Levi's have developed the Project Jacquard Commuter Trucker

jacket enabling the wearer to connect to a variety of services, such as music and maps

directly from the jacket. The jacket is equiped with a Jacquard technology that makes it

possible to design and produce connected, interactive denim garments. [315]


Technological Gaps:

A main technology gap for mobile and wearable devices is missing connectivity since

comprehensive WLAN is required to do a real-time monitoring. Bad ergonomics and too short

battery lifes are also technological gaps of mobile and wearable devices. The miniaturisation

(smaller product sizes) can also be problematic as information is more difficult to display.

There are also technological gaps regarding data accuracy, data compliance across different

systems, data security and privacy, dealing with noisy data, and the high volumes of data that

need to be analysed and converted into meaningful outcomes [63].

For smart clothing there are some specific gaps like heat dissipation, washability and comfort.


For the implementation of mobile and wearable devices there are many cultural questions

regarding privacy, as well as technological challenges to securing both raw, personal data and

the insights that are derived. The potential for security breaches from wearable devices has

to be prevented. [25; 149]


52

Other technological implementation challenges are the improvement of the accuracy of

sensors, the adaption of machinery and equipment, and the effective integration of the

technology regarding the interfaces [57].

In order to develop from traditional industry to wearable industry, the technological and cost-

effective gap is currently huge. This evolution must be carried out in steps to achieve

significant cost reduction.

Another implementation challenge is the acceptance in the organisation. User acceptance has

to be achieved through the timely training of users. If there are works councils, they must be

included in the implementation phase as a matter of urgency. [57]

Communication Infrastructure

Network communication covers all technologies. Networks and protocols are needed for the

communication relationship between two or more Internet of Things components. The

orchestration of the used network resources may also be made over non-Industry 4.0

compliant interfaces.

Enabling communication technologies are 4G / LTE, 5G and NarrowBand-IoT. Long-Term

Evolution (LTE) or 4G is the global standard for high-speed wireless communication enabling

ultra-high speed data in mid-range frequencies (700Mhz - 2600Mhz) [204]. 5G describes the

next, fifth generation of wireless networks [101]. NB-IOT (Narrowband IOT) is defined as a

radio technology used to link IoT devices over a wide area with low power and low-speed [204]


The technology Communication Infrastructure was evaluated based on expert workshops and

literature review. As shown in Figure 3-18, the TRL Category is C (Applied research) based

on the level of application examples, the technological gaps and implementation challenges.

For the manufacturing and the process industry sector the applicability of the technology is

broad, while the logistics sector sees a moderate applicability.

Figure 3-18: Technology Evaluation Communication Infrastructure

Figure 3-19 shows the implications of Communication Infrastructure on the supply chain



53

Figure 3-19: Implications of Communication Infrastructure on SC Performance

The implications of Communication Infrastructure on the supply chain performance are

characterised by:

Agility: The developments in communication infrastructure, like 5G, bring a number of

enhancements including high speeds and low latencies, so that real-time changes can

be tracked and improve the performance of the processes.

Costs: 5G offers new network management possibilities that could enable a single

physical network to support a number of virtual networks with different performance

characteristics. Therefore, costs of grids and infrastructure will decrease. On the other

hand, new infrastructure and communications grids are being placed on otherwise

unconnected areas.

Transparency: Due to new communication infrastructure, internet is going to be

available everywhere, in real-time and full time. This will lead to completely accessible

data, resulting in more transparent processes.

Responsiveness: Due to the optimised data transfer, processes will be more

responsive.

Reliability: There will be an enhanced reliability because of improved outdoor and

indoor penetration coverage compared with existing wide area technologies, secure

connectivity and strong authentication [139].

Sustainability: The advancements in communication infrastructure will lead to lower

power consumption. Current consumption of the order of 1nA (nanoampere) enables

devices to operate for up to 10 years on a single charging cycle [139]. Furthermore, a

better and more efficient communication infrastructure will lead to smoother

communication between the different actors in the process.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


54


Application examples of 5G

Example: In cell automation, devices in an assembly line and control units communicate

wirelessly with sufficient reliability and low latency to enable flexible and highly efficient

production [264].

Application example of NarrowBand-IoT

Example: The leader on the NB-IoT market is Deutsche Telekom. They want to develop

new products for this network and quickly bring them to the European market. In 2016

they launched a special program (NB-IoT Prototyping Hub) with laboratories in Bonn,

Berlin and Krakow. The Partners participating in the program had access to NB-IoT trial

network and communications modules which are still unavailable to the market. [318;

192]


Technological Gaps:

The major technological gap regarding communication infrastructure is the development of

methods to manage the ever-increasing quantity of available data. 5G will require radically

different structures of networks, as well as small cells, which are needed to achieve the high

data rates [242]. Moreover, 5G needs to overcome deployment challenges such as spectrum

acquisition and coverage. Additionally, the necessary fibre infrastructure for rural 5G

deployments may be lacking.

For Smart Grid domain NB-IoT a large research effort is still required to meet higher and

stricter requirements. Security issues, such as authentication, data integrity and privacy were

not discussed yet [192].


The major implementation challenges are latency, reliability, data rate and the quality of

service. These features are necessary to obtain a 5G network. Low latency is critical for 5G,

since autonomous systems will rely on this standard and any delay may have serious

consequences on processes. Networks capable of transmitting data traffics with different

characteristics regarding speed, data nature and data communication requirements have to

be built. [1; 36]

Another major implementation challenge concerns the issue of security. 5G is a secure IP-

based solution which will be responsible for all the security threats in the current internet world.

Some threats can be expected while implementing the 5G network. Network operators can

share a common core network infrastructure, so a single operator can collapse the whole

network infrastructure. [117]

For NB-IoT, important implementation challenges are required software and/or hardware

upgrades to provide security [151].


55

Identification Technologies

Identification Technologies are used to identify and track goods by using different codes or

tags. Identification Technologies include RFID and Barcode Tags. [95]

Radio Frequency Identification (RFID) is a contact-free alternative or complementary

approach to 2D or bar codes [196]. RFID systems consist of three parts: RFID transceivers

for reading and writing data to RFID tags, network media for transmitting data to industrial

controllers, and the RFID tag programmed with information. These tags contain internal

circuitry and antennas to emit a radio frequency wave which is secured and can be analysed

by RFID readers [45].

A barcode tag is a label, consisting of a series of bar and space regularly arranged with a

corresponding character, that can be recognised by the human eye and shows certain

information. Due to a link between the barcode and the goods, the efficiency of goods

management can be improved through quick and easy scanning of the barcode. There are

different types of barcodes, such as EAN-13, EAN-8 or UPCA. [160]


The technology Identification Technologies was evaluated based on expert workshops and

literature research. As shown in Figure 3-20, the TRL Category is A (Market presence) based


For the manufacturing sector, the applicability of the technology is moderate to broad, while

the process sector sees a moderate applicability. In the logistics sector there is a broad

applicability of the technology.

Figure 3-20: Technology Evaluation Identification Technologies

Figure 3-21 shows the implications of Identification Technologies on the supply chain



56

Figure 3-21: Implications of Identification Technologies on SC Performance

The implications of Identification Technologies on the supply chain performance are

characterised by:

Agility: Identification technologies will provide tracking information and visibility. The

real-time monitoring can improve the agility to respond to external influences.

Costs: RFID tags improve data collection speed and accuracy. Otherwise, it will require

investments in the supply chain due to high costs associated with the tags (especially

the active tags).

Transparency: Identification technologies are enablers for data transparency. They

allow to track a product’s flow throughout the production process and supply chain,

help to ensure seamless traceability and reduce losses, while enhancing stock

management and processing speed [382].

Responsiveness: An advantage of the RFID technology is that it does not require a

line-of-sight between the object and the reader. Therefore, RFID enables the

identification and recording of goods in less time than it takes to scan a barcode

shipping label. [2]

Reliability: Tracking assets across the supply chain will make the whole process more

reliable, and prevent losses and thefts.

Sustainability: The application of RFID technology can be positively associated with

green supply chain management practices and environmental performance [18; 145].

Using RFID leads to more efficient processes, a better forecast accuracy and a

reduction of errors, as well as waste in the production process, thus improving the

energy consumption and waste management [190; 259]. Furthermore, the application

of RFID technology can reduce the paper use [259].

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


57


Application examples of Radio Frequency Identification (RFID)

RFID is used within the pharmaceutical industry to monitor portable tanks. By using RFID,

manufacturers are able to easily track the chemicals stored in the tank itself. [45]

Applications of RFID technology can be also found in the health care industry. RFID is used

for the improvement of patient monitoring and safety, which will increase asset utilisation with

real-time tracking for reducing medical errors by tracking medical devices and for increasing

the supply chain efficiency. [382]

Example: Intidex S.A., a parent company of Zara-chain uses an inventory system based

on RFID technology. During the initial phase more than 1000 stores used RFID tags as

labels in expensive goods like wallets, belts and shoes. [285]

Application examples of Barcodes

In manufacturing, barcode applications are used for warehousing, customer service functions,

package delivery and the assembly line operation itself [2], as well as other applications.


Technological Gaps:

One major technological gap is the lack of standardisation for identification technologies.

There are different types of tags. Basic tags do not use any encryption which means that they

can be counterfeited easily. Most RFID tags lack a system for detecting and distinguishing

between real and fake RFID readers. [285]

Moreover, RFID sensors are sensitive to environmental conditions, such as temperature,

ambient metal and electromagnetic radiation [233].

Other technological gaps include the reading of the RFID tags. Wrong readings, duplicate

readings (tag is observed twice) and missed readings are also gaps of RFID. [225]


Data privacy and protection are critical issues for the systems, especially if the parameters

measured are sensitive and confidential for users.

Because of the sensitiveness of the RFID sensors, calibration is an essential and sophisticated

step to achieve desired measurement accuracy [211].

Furthermore, the costs of RFID systems, application software, maintenance, security to

system and human resources are relatively high, thus requiring reduction [285].

Location Technologies

Many mass market applications require seamless positioning and tracking capabilities in both

outdoor and indoor environments [228]. Hence, passive GPS trackers, active GPS tracking

systems and wireless indoor positioning systems are important components of location

technologies.


58

With passive GPS trackers the tracking of every movement is not possible in real-time.

Instead, all the GPS activities are stored inside the tracker, and users will need to connect this

to a computer to analyse the data. In contrast to this, active GPS trackers are attached to any

object enabling all activities to be monitored from a computer or smartphone. Wireless indoor

positioning systems provide a new layer of automation called automatic object location

detection. [336]


The technology Location Technologies was evaluated based on expert workshops and

literature review. As shown in Figure 3-22, the TRL Categories range from D (Basic research)

to C (Applied research) based on the level of application examples, the technological gaps

and implementation challenges. For the manufacturing and the process sector the applicability

of the technology is moderate, while the logistics sector sees a broad applicability.

Figure 3-22: Technology Evaluation Location Technologies

Figure 3-23 shows the implications of Location Technologies on the supply chain performance


Figure 3-23: Implications of Location Technologies on SC Performance

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


59

The implications of Location Technologies on the supply chain performance are characterised

by:

Agility: Real-time location information will allow faster decision making in the supply

chain. For example, better location and traffic analysis, and tracking of goods in real-

time within the warehouse, will have a positive implication on the agility. [175; 178;

305]

For the process sector, the implication on the agility is not that positive as there are

some constraints regarding the delivery of real-time data without delay. Therefore,

some research and development is required. [39]

Costs: The costs will decrease due to identification of bottlenecks in the process and

data collection to optimise processes. However, the implementation of location

technology will require significant investments in the supply chain.

Transparency: Real-time location of items during the whole process and real-time

information sharing will enable the transparency, and have implications on the whole

supply chain [260; 331].

Responsiveness: The availability of location information will lead to a higher

responsiveness in the supply chain.

Reliability: Processes will be more reliable as the technologies will allow location

precision with high level of trust. For the process industry, location technologies can

be used to save pipeline segment geographical position and to identify objects in

complex structures more reliably.

Sustainability: These technologies will reduce energetic consumption associated with

equipment search, which will have a positive implication on the sustainability. The

location technology can also lead to sustainability if it is used for optimising driving

practices.


Application examples of passive GPS trackers

Example: Track Your Truck provides a passive GPS tracking device that matches

unique business requirements. Passive GPS tracking systems are affordable solutions

for companies with trailers and containers operating that work most of the time in cellular

coverage areas. Knowing the detailed status of the vehicles at every time, will improve

the management of company assets in terms of better security and service. [340]

Application examples of active GPS tracking systems

Example: An example of an active GPS system is Trackimo. It comes complete with a

detailed mapping system and is capable of determining an individual’s exact location.

Therefore, real-time GPS tracking technology has become more popular compared to

GPS logging, as more people see the need of monitoring their assets or loved ones in

real-time. A GPS tracker is typically hardwired to a vehicle or attached to the object you

wish to track. Users receive updates and alerts in the form of e-mail, text, or in-app


60

notifications. Trackimo covers everything from speeding beyond a set threshold to

subjects that go pass a designated safe area. [341]

Application examples of wireless indoor positioning systems

Example: The Ubisense local positioning system is a unidirectional Ultra-wideband (UWB)

location platform with a conventional bidirectional time division multiple access (TDMA)

control channel, capable of sending ultrashort pulses. The tags transmit UWB signals to

networked receivers and are located using angulation techniques and time difference of

arrival (TDOA). Thus Ubisense can reach an accuracy of 15cm.

Application examples of Location Technologies in general

Application 1: Real-Time Locating Systems (RTLS)

Real-Time Locating Systems include barcode, RFID, GPS and sensor technologies to mark,

track, and display the status of assets in real-time. While passive technologies such as

barcodes locate an item, the active RFID element is able to provide the live location and actual

status of the item. [2]

Tracking systems provide consistent information about the material flow processes.

Example 1: Material containers can be recorded according to number, location, time and

load. Operators can be guided to the right place at the right moment, gaining a significant

amount of time. Recall actions, the supply of assembly workstations or the monitoring

of maintenance intervals can be simplified and optimised. Depending on the

requirements, the operator can select a client-based with an app (positioning via Wi-Fi,

Bluetooth beacons and sensor fusion) or a server-based solution (no app required;

positioning via infsoft Locator Nodes; detection of Wi-Fi devices, Bluetooth beacons,

Ultra-wideband or RFID modules). Due to the use of Ultra-wideband (UWB) high-

precision solutions with low latencies can be realised. RFID is especially qualified for a

point-specific object identification in large quantities. [177]

Example 2: Sewio is a company whose activity consists of the implementation of RTLS

[305].

Application 2: Intelligent traffic guidance system

The intelligent traffic guidance systems helps to prevent traffic congestion and will make the

traffic smoother. The system includes location, sensor technology and communication

infrastructure. [64]


Technological Gaps:

For the location technologies there are concerns regarding costs, as well as precision and

accuracy. The high costs of the devices, are limiting the applicability of the technology on high

value assets or collective loads [159].

Especially in industrial environment with a lot of disturbances, reflections and signal strength

losses a high accuracy for indoor location, which is often not yet achieved, is necessary. A


61

leap of technological knowledge is required to cope with the industry needs for improved

scalability (e.g. density and coverage) and for realising these [141].

Regarding the passive GPS trackers most of the systems do not allow wireless data

transmission at the base, so that it requires manually tasks. This leads to a lack of real-time

correction features, and therefore, for vehicle tracking, the delivery time cannot be precisely

calculated.

Active GPS tracking systems using GPS/GLONASS have fairly high-power consumption,

limiting battery life if the assets don’t have their own power sources. These systems typically

do not work indoors without adding specialised hardware. Other methods for calculating the

location, such as cell tower triangulation/trilateration are much less accurate. [159]


One major implementation challenge for location technologies is the protection of data. Data

leaks have to be prevented as they could lead to thievery, threats and possible blackmail

activity.

The adoption of one standard would facilitate the system arise. Another challenge is the

decreasing of costs. Implementation costs must be quickly compensated for by the

advantages of the technology.

The reliability, robustness and accuracy of the localisation both inside and outside are decisive

for the implementation of the technology. Especially for indoor location the technology has to

be very accurate (accuracy <1m is necessary), otherwise the technology is redundant [73].

Visual Computing

Visual Computing is an image- and model-based information technology that turns information

into images and is able to extract information from images. For this purpose, the technology

combines computer graphics and computer vision. In computer graphics, images or multi-

dimensional models are created, while computer vision enables a computer to see its

environment by means of a camera. [125]

Visual computing includes the sub-technologies Augmented Reality and Virtual Reality.

Augmented Reality (AR) uses contextualised digital information to enhance the viewer’s real-

world view by means of computer vision [125]. Applications of AR in industrial context aim to

help the operator, i.e. by projecting operation instructions for logistics, manufacturing or

maintenance [234]. Virtual reality offers a digital recreation of a real life setting by means of

computer graphics [125]. For this purpose, virtual reality generates simulations of three-

dimensional images.


The technology Visual Computing was evaluated based on expert workshops and literature



manufacturing sector the applicability of the technology is limited to moderate, while the


62

process sector sees a moderate and the logistics sector even a broad applicability of visual

computing.

Figure 3-24: Technology Evaluation Visual Computing

Figure 3-25 shows the implications of Visual Computing on the supply chain performance for


Figure 3-25: Implications of Visual Computing on SC Performance

The implications of Visual Computing on the supply chain performance are characterised by:

Agility: The implication on agility will be rather limited, but the technology is able to

increase the agility in fields where trained and specialised workforce for very specific

tasks is needed [187; 293].

Costs: The costs might decrease in a limited range. It may include cost savings due to

faster prototyping, enhanced design and training, risk-free exploration, increased

efficiencies and decreased personnel costs. It will certainly have a positive implication

regarding the costs for maintenance and labour instruction fields. Otherwise, it is

expensive to procure and own devices (high investment costs). [278]

Transparency: Visual computing will provide visibility in the warehouse and for other

processes in the logistics sector. In the process industry sector for example,

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


63

Augmented Reality will enable engineers to control assets in real-time from an onshore

location using a head-up display [245].

Responsiveness: Due to the possibility of monitoring processes and instructions and

getting data and information in real-time, faster and more efficient processes (e.g.

maintenance) will be possible. Using real-time expert support, there will be positive

implications on the responsiveness.

Reliability: The application of visual computing will decrease human errors due to the

visualisation of processes. Complex assembly tasks can be performed while taking

into account a model which is overlapped with the actual device to be assembled, thus

providing more reliability on manufacturing process as a whole, regardless of the

station and workers ability on the specific task needed.

Sustainability: Due to the possibility of monitoring safety instructions, visual computing

will have a positive implication on sustainability regarding the worker’s safety.

Furthermore, the need for materials as well as the waste can be decreased as quality

assurance and automation will be more important for employees. In the process

industry, the application of AR will help to monitor pipeline infrastructure and detect

leaks before they occur, which will have a positive implication on the sustainability, as

well.


Application examples of Augmented Reality (AR)

Application 1: Augmented Reality Aided Manufacturing (ARAM)

Description: ARAM represents an application of augmented reality in the field of manufacturing

processes, and includes augmented reality aided systems for all activities connected with

realisation of product manufacturing (e.g. machine tools, transport and store devices,

measuring, testing and diagnose of parts and assembled product) [257].

Example: AR SOP Guide and Instructor work with digital standard operating procedures,

gesture inputs, computer vision and voice commands. The work of the operators is

facilitated due to 3D modeled, step-by-step instructions, automated task guidance and

visual assistance on smart glasses and tablets. [80]

Application 2: Vision Picking technology

Description: Vision picking is based on the use of 2D barcodes for interaction with the

warehouse environment. By means of a head-mounted display all important information are

illustrated at the exact location and the proper time. The technology consists of the head-

mounted display, a control software and an integrated hardware and camera. Thus, the

integration into an existing warehouse management system is easily possible.

Example: DHL uses the vision picking technology. Each worker can see the digital

picking list in their field of vision using the head-mounted display. Due to indoor

navigation capabilities and efficient route planning, the operators are able to see the

best route for the next working step which leads to reduction of the walking time. Using


64

automated barcode scanning, the image recognition software is able to control if the

operator has arrived at the right location, and the operator can be guided quickly to the

relevant item on the shelf. [87]

Application 3: Augmented reality in retail

AR can produce significant experiences for online shoppers by providing product information

that enables them to evaluate the targeted product. AR entertains users and enables them to

personalize information in a 3D virtual model, and they enjoy interacting with virtual objects

more than they do handling or looking at physical objects. [270]

75% of the world’s most valuable brands have created some form of virtual or augmented

reality experience for customers or employees, or are themselves innovating and developing

these technologies [81]. 66% of customers are interested in buying items via VR. 63% of

consumers said they are expecting VR to change the way they shop [81].

Example 1: Using the Motor Village Digital Store it is possible, independently and without

any waiting, to digitally configure any car of the Fiat brands. The Motor Village Digital

Store offers a new experience in the Virtual Reality in 3 dimensions, due to the

application of the Oculus technology. [244]

Example 2: In New York, Tommy Hilfiger’s Fifth Avenue store has installed Samsung

Gear VR headsets, immersing shoppers in a virtual journey to view and shop the label’s

fall fashion show [28].

Example 3: IKEA’s catalogue app uses AR to allow customers to virtually place IKEA

products in their home and get a realistic idea of whether a piece of furniture will fit in

their living room or bedroom. This saves the customer time in measuring the dimensions

of a piece of furniture and calculating the amount of space it could potentially take up.

[326]

Application examples of Virtual Reality (VR)

Application 1: Virtual Reality headsets

Example 1: Oculus VR developed and manufactured a Virtual Reality headset called

Oculus Rift which was first released in March 2016. Oculus Rift has a stereoscopic

OLED (Organic Light Emitting Diode) display, integrated headphones providing 3D

audio effects and rotational and positional tracking sensors. [309]

Example 2: The HoloLens combines AR, VR and live video enhancing and modifying

the real world. The user can interact with the environment by means of eye, voice and

hand gestures. [309]

Application 2: Training applications in process industry

VR technology is primarily used for training applications in a variety of process industries. It

offers the potential to expose personnel to simulated hazardous situations in a safe, highly

visual, and interactive way. Customised simulations of chemical plant layouts, dynamic

process operations, and comprehensive virtual environments can be set up to allow users to

move within the virtual plants, make operational decisions, and investigate processes at a


65

glance. Trainees see the consequences of correct and incorrect decisions immediately, giving

them the opportunity to directly learn from their successes and mistakes.

Application example of computer vision

Application: Computer Vision Inventory Management & Execution

Computer Vision Inventory Management & Execution can be defined as the identification of

characteristics of items such as brand, labels, logos, price tags, as well as shelf condition by

means of computer vision [91].

Example: Qopius develops computer vision-based AI for measuring shelf performance,

tracking products and improving retail store execution using deep learning and fine-

grained image recognition. The AI engine identifies properties of items (e.g. brand,

labels, logos, price tags, shelf condition). The use of computer vision AI can enable real-

time inventory management at Stock-keeping unit level. [91]


Technological Gaps:

For visual computing, there are technological gaps due to the lack of maturity in this area

related to optics and 3D capabilities [278]. One major technology gap is the latency in drawing

new content. Each system must have a threshold latency resulting from the frame rate of the

content to be drawn, the refresh rate of the display, and the input lag due to the interaction

with which the new content drawing was started. Furthermore, augmented and virtual reality

technologies have the unique problem of requiring spherical sound design by default. [215]

Another technological gap is that different VR headsets have different content formats today,

so that, for instance, the content running on Oculus Rift cannot run on HTC Vive without

modifications [215].

Augmented reality in its current form is a predominately mobile-focused technology.

Experiments like Google Glass and Microsoft’s HoloLens are still years away from mass

production. One technology gaps of these technologies is the short battery life.

Another technology gap that needs to be solved is the risk of hackers who may gain access

to a user’s device [26].


Generally, creating virtual content requires expertise and the right technology. The technology

needs to be more accurate and reliable and the quality and the resolution of the images have

to be improved. Augmented and virtual reality require a consolidation body that allows

developers to easily create content for multiple platforms [215].

Another major implementation challenge is the user acceptance. Therefore, the training of the

users must be improved by creating mirror real life situations and the comfort of the worn

equipment must be increased, so that there is no limitation for the movements of the users.

Companies looking to take advantage of visual computing must be aware of any potential

safety risks (e.g. the risk of distraction) for the users of the technology [143].


66

Regarding the technology gap of different content formats, a standardisation of VR and AR

technologies is necessary to avoid modifications when different headsets are used.

Additive Manufacturing

Additive manufacturing (AM) is a genuinely disruptive technology which supports

personalisation and minimises waste as well as enables the more efficient use of resources

[43].

Through a transformative approach to industrial production, AM enables the creation of lighter,

stronger parts and systems. It uses data computer-aided-design (CAD) software to direct

hardware to deposit material in precise geometric shapes, on a layer-by-layer basis. AM

distinguishes itself from traditional methods by adding materials in order to produce/create an

object, contrasting with the removal of material on traditional methods. [137]

Additive manufacturing includes 3D and 4D printing. 3D printing is a methodology using three-

dimensional CAD data sets for producing 3D haptic physical models [282]. 3D printers can

print different materials such as metal, wax, plastics, and ceramics. 3D printing can create

parts of an assembly, final products, or parts for maintenance, repair and overall (MRO)

operations [74]. There are different 3D printing processes like material extrusion, powder bed

fusion, directed energy deposition and binder jetting.

Material extrusion is an additive manufacturing process in which material is selectively

dispensed through a nozzle or orifice. The raw material is typically a thermoplastic filament

wound onto a spool which is melted during extrusion. [314]

Powder bed fusion is a process by which thermal energy fuses selective regions of a powder

bed. The thermal energy melts the powder material, which then changes to a solid phase as

it cools. [32]

In the directed energy deposition process, focused thermal energy melts and fuses materials

as the material is being deposited. In most cases, a laser is the source of the energy, and the

material is a metal powder. [338]

Binder jetting is a process by which a liquid bonding agent is selectively deposited through

inkjet print head nozzles to join powder materials in a powder bed [77].

4D printing is the process by which a 3D printed object is transformed into another structure

through the influence of external energy such as temperature, light or other environmental

stimuli. Thus, the fourth dimension which is added compared to 3D printing is the time. [266]


The technology Additive Manufacturing was evaluated based on expert workshops and

literature review. As shown in Figure 3-26, the TRL Categories range from D (Basic research,

for 4D printing) to B (Market-ready, for 3D printing) based on the level of application examples,

the technological gaps and implementation challenges. For the manufacturing sector the

applicability of the technology is moderate to broad, while the process sector sees a moderate

and the logistics sector even a broad applicability of additive manufacturing.


67

Figure 3-26: Technology Evaluation Additive Manufacturing

Figure 3-27 shows the implications of Additive Manufacturing on the supply chain performance


Figure 3-27: Implications of Additive Manufacturing on SC Performance

The implications of Additive Manufacturing on the supply chain performance are characterised

by:

Agility: Additive Manufacturing will have a broad positive implication on the agility of

the supply chain, for all three sectors, due to the possibility to create multiple different

products at the same time and a customisation near the end customer [267]. Therefore,

very fast adaptation to changes in customer demand is possible. 4D Printing even

allows to create self-modifying outputs that are able to adapt independently to

changing environments [142].

Costs: Additive manufacturing will cut costs associated with holding inventory and

shipping due to products stored digitally and printing on demand [60; 161]. Additive

manufacturing will also optimise asset maintenance [136]. However, additive

manufacturing technologies are expensive currently due to high material costs,

especially for the process supply chain sector.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


68

Transparency: Additive manufacturing will not have any implications on the supply

chain transparency or traceability.

Responsiveness: Due to the capacity for rapid prototyping, companies will be able to

develop and validate their designs faster, allowing enterprises to better react to

emerging market opportunities [136] and reducing the delivery time [34].

Reliability: The error-correcting for self-repair or self-assembly materials will have a

direct and positive implication on the reduction of wastes, defects and loss of products

[142]. Additive manufacturing limits downtime due to reduced lead times and supply

chain enhancements. In the logistics and manufacturing sectors, companies can

switch to a ‘build-to-order’ model and decrease their inventory levels offering a higher

reliability. Despite promising these possibilities, additive manufacturing is still emerging

and cannot currently be considered as a reliable technology [74].

Sustainability: Additive manufacturing has the potential to significantly reduce resource

and energy demands as well as process-related CO2 emissions [138]. The additive

manufacturing process deposits only the required amount of material in a layer-by-

layer fashion before the final object is created. The amount of residual material or

waste remaining after the process is significantly less than in subtractive manufacturing

[42].


Application examples of 3D printing

Example (material extrusion): FDM Technology works with specialised 3D printers and

production-grade thermoplastics to build strong, durable and dimensionally stable parts

with the best accuracy and repeatability of any 3D printing technology [328].

Example (powder bed fusion): EOS 3D Printer. 3D Printing of Metal Parts on an

Industrial Scale. Additive manufacturing system for the industrial production of high-

quality large metal parts. [109]

Example (directed energy deposition): DMG MORI AM in milling quality. The

combination of laser deposition welding with a powder nozzle and milling is a generative

machining method. It enables faster production of complex geometrics and individual

3D-parts. [98]

Example (binder jetting): The HP Jet Fusion 500/300 Series 3D Printers are one

example for binder/material jetting [163].

Application: B2B 3D printing services

B2B 3D printing services can enable new logistics services especially in aftermarket supply

chains (the warehousing and distribution of spare parts). Instead of managing multiple

warehouses stacked with spare parts that are often rarely ordered, logistics providers can set

up a global 3D printing infrastructure coupled with a software database of digital models. Spare

parts can then be printed only on-demand at the nearest 3D printing facility (e.g., a hub or


69

airport) and be delivered to the right location. This would reduce lead times and cut inventory

costs.

Example: 3YOURMIND helps its customers to connect every part of the workflow and

increase their additive manufacturing production by using the startup’s software

platforms for rationalising industrial 3D printing [301].

Application example of 4D Printing

Example: Kinematics by Nervous System is a system for 4D printing producing complex,

foldable shapes from articulated modules. The system offers the possibility of

transforming any three-dimensional shape into a flexible structure by means of 3D

printing combining computational geometry techniques with rigid body physics and

customisation. [252]


Technological Gaps:

Main technology gaps of additive manufacturing are the limitations regarding the availability

and costs of 3D printing material, the printing time, especially when producing big parts, the

automation in the production process and the quality of the printings. The printing of complex

parts is associated with limitations in the reliability and the skills of the personnel. Due to these

technological gaps, mass customisation is still in its initial phase. [74]


A major challenge for additive manufacturing is creating a single object from multiple materials.

Thus, the availability of matching materials will be a critical success factor for industrial

applicability [88] as the certification of 3D printing materials needs to meet stringent

performance and safety standards [136]. In order to be usable for mass production, the

technology needs to be improved.

Another implementation challenge is the raising concerns over the proper use of the

intellectual property of digital CAD data from the printing model as the authors might be

attacked by hackers and suffer copyright infringement [89; 136].

For 4D printing, a major challenge the design of structures that can transform from one shape

to another. This requires complex material programmability, precise multi-material printing and

highly specific joint designs for folding, curling and twisting, on the hardware side. For the

software side, there are even bigger implementation challenges regarding sophisticated

simulation, topology transformation and material optimisation for efficient structures. [206;

337]

Energy Infrastructure

The task of energy infrastructure is the reliable across-the-board energy coverage with the

aim of optimising the energy consumption [144].

Smart and neural grids combine the generation, storage and consumption of electric energy.

Storage wise, Energy Electrification Storage Systems (ESS) and Battery Energy Storage


70

Systems (BESS) play major roles, being capable of providing energy storage solutions for

thermal, electro-mechanical of electro-chemical electric energies. [12]

Smart grid is a modern electric power grid infrastructure that aims at higher efficiency,

reliability and safety by integrating renewable and alternative energy sources. Automated

control and modern communications technologies and reliable real-time information are key

elements for reliable supply of power to the end-users. [296]

The neural grid platform is an autonomous grid that uses artificial intelligence, connectivity,

clouds, robotics, and sensing technologies across grid and non-grid energy assets. The Neural

Grid supports omnipresent automation, self-healing, customer retention, and the integration

of dispersed markets for transactive energy. [105]


The technology Energy Infrastructure was evaluated based on expert workshops and literature

research. As shown in Figure 3-28, the TRL Category is D (Basic research) based on the level

of application examples, the technological gaps and implementation challenges. For the

manufacturing sector, the applicability of the technology is broad, while the process sector

sees a moderate applicability. In the logistics sector there is no applicability of energy

infrastructure.

Figure 3-28: Technology Evaluation Energy Infrastructure

Figure 3-29 shows the implications of Energy Infrastructure on the supply chain performance



71

Figure 3-29: Implications of Energy Infrastructure on SC Performance

The implications of Energy Infrastructure on the supply chain performance are characterised

by:

Agility: From the manufacturing sector’s point of view, there will be a positive

implication on the agility as smart and neural grids will provide improved energy supply

to the factories [322].

The process sector focuses on threats as smart grids industries can shut down energy

demanding process once costs reach a limit. This will decrease the agility of processes

as it would be partially controlled by external parameters. For example, if energy prices

are too low, the process is expected to be slower. [297]

There are no visible implications on agility regarding energy infrastructure, from the

logistics sector point-of-view.

Costs: Smart and neural grids will decrease energy consumption, thus diminishing

energy costs [297; 322].

Transparency: There will be no implication on the supply chain transparency caused

by smart and neural grids.

Responsiveness: From the manufacturing and logistics sectors’ point of view, there

will be no implication on the supply chain responsiveness due to smart and neural grid.

For the process industry sector, there is a limited positive implication. As energy will

be locally stored (even in a certain limit), it will enable production on modular plants.

Reliability: From the process and logistics sectors’ point of view the smart energy

infrastructure will not impact the reliability of the processes. The components and

different devices used within the process will have a more harmonized use and this

can impact their lifecycles despite not being an implication as such for the processes.

Regarding the manufacturing sector, the reliability will have a slightly positive

implication, particularly concerning energy supply, if the quality of solutions is

improved.

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


72

Sustainability: Smart and neural grids will enable an improved use of energy within the

overall process due to renewable energy sources, thus improving the sustainability of

the SC. The use of smart grid solutions can help making distribution grids more flexible

and dealing better with renewable energy sources. Furthermore, they can enable

active consumers and energy communities to participate in the energy markets. [130]


Application example of Smart Grid and Battery Energy Storage Systems (BESS)

Example: Battery Swapping Station represents the idea to extend the drive range by

exchanging the battery instead of recharging. The system is completed with software for

intelligent battery charging schemes to avoid peak loads on the electricity grid and to

locate the closest charging spot. Furthermore, the car batteries could also be used to

sell energy back to the grid, creating a new source of revenues for the company and its

customers. [12; 119; 180; 300]


Technological Gaps:

Due to the greater complexity caused by smart grids, there will be new risks. An ICT-driven

system now depends not only on the performance of the electricity grid, but also on the

telecommunications networks to transmit real-time supply and demand data. Given the

unstable control algorithms and systems, this may lead to unexpected system failures until the

technology is mature. Furthermore, the use of proprietary communication protocols threatens

to restrict the communication between systems and the applications of the smart grid. [251]

The building of ICTs into the energy infrastructure system and the use of signals from users

leads to the possibility of nefarious activities like hacking or manipulation for political or criminal

reasons. This can result in significant privacy, security and reliability risks. [231]


For smart and neural grids, the modelling of power consumption with the help of data mining

and the definition of pricing schemes are decisive challenges. The optimisation algorithms

have to be as accurate as possible. Since the pricing will vary depending on supply, building

the right model will prove to be a challenge. For this purpose, data availability, including

especially closed-loop real-time demand response, will be an important factor. [329]

To optimise the process of smart and neural grids, it will be necessary to store the energy at

a large scale [239]. Current batteries may not have the capacity, so that this will be another

implementation challenge.

The integration of distributed control systems, owned and operated by different actors,

requires technical standards to enable communication between the systems. Protocols and

standards developed for this issue must be revised and adopted. [181]


73

The realisation of neural grids will only be possible with the latest, still emerging technologies

such as 5G, low cost sensors, unmanned drones and robots, artificial intelligence and cyber

security solutions [108].

Alternative Propulsion Systems

In contrast with conventional engines, alternative propulsion systems do not rely solely on

petroleum-based fuels. Thus, this category includes the sub-technologies propulsion by

advanced biofuels and electro mobility.

Advanced biofuels are produced by technologies and processes that convert a broad

spectrum of plant, waste and cellulose molecules into hydrocarbon molecules [10]. Some of

these biofuels can be used to power vehicles.

Electro mobility is the application of electric drive technologies, in-vehicle information and

communication technologies and related infrastructures to enable the electric drive of vehicles

[134]. Electric vehicles are introduced to minimise CO2 emissions especially in urban areas.


The technology Alternative Propulsion Systems was evaluated based on expert workshops

and literature review. As shown in Figure 3-30, the TRL Category is B (Market-ready) based


For the manufacturing sector the applicability of the technology is moderate to broad, while

the process sector sees no applicability. In the logistics sector there is a limited applicability of

alternative propulsion systems.

Figure 3-30: Technology Evaluation Alternative Propulsion Systems

Figure 3-31 shows the implications of Alternative Propulsion Systems on the supply chain



74

Figure 3-31: Implications of Alternative Propulsion Systems on SC Performance

The implications of Alternative Propulsion Systems on the supply chain performance are

characterised by:

Agility: Alternative propulsion systems might have slightly positive implications on the

SC agility when combined with autonomous vehicles technologies and/or the use of

transport that requires this alternative propulsion systems’ sources (e.g. autonomous

electric trucks can run faster than a regular truck when fully loaded) [324]. However,

the alternative propulsion systems’ technology has no implication on the agility by itself,

on any of the supply chain sectors directly.

Costs: Vehicles powered by electro mobility will provide operating cost savings due to

lower consumption costs. However, the investment costs (e.g. costs of the batteries

and charging) are higher than those of conventional vehicles at current prices.

Transparency: When the electric vehicles are combined with other technologies such

as IoT, Cloud Computer Based Systems or Sensors, more data interfaces will be

available. This, in turn, will provide broader network and could lead to increased

transparency. Despite these facts, alternative propulsion systems on themselves have

no implication on the supply chain transparency.

Responsiveness: Special utility concepts and loading restrictions limit flexibility in the

short and medium term. Despite these points, similar performance as today can be

expected in the long term.

Reliability: Reliability diminishes in the short term as systems and technologies are not

performing well. In the long term, electric vehicles will require lower maintenance and

will be less susceptible to interference when compared to conventional drives, which

strengthens reliability.

Sustainability: Natural gas burns cleaner than conventional energy sources and emits

less environmentally harmful carbon dioxide (CO2) [352]. Battery electric vehicles emit

no tailpipe emissions during operation, which is becoming increasingly important in

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


75

congested urban areas. However, in the electricity and battery production and disposal

process, there are some concerns regarding the sustainability, such as human toxicity,

greenhouse gas emissions and ozone layer depletion [38].


Application examples of propulsion by Advanced Biofuels

Application 1: Compressed Natural Gas Vehicles (CNGVs)

CNGVs are vehicles fueled by compressed natural gas (CNG).

Example: Since the start of their cross-brand campaign in the northern spring of 2017,

the Volkswagen campaign alliance has succeeded in significantly enhancing the status

of CNG (Compressed Natural Gas) for individual mobility in Germany. [254]

Application 2: Truck powered by liquefied natural gas (LNG)

The use of liquefied natural gas (LNG) allows to lower emissions by “tank to wheel”.

Example: Volvo offers a gas-powered truck with a powertrain based on Volvo diesel

engine technology. The gas-powered Volvo FH LNG comes with a 420 or 460 HP

(Horse-Power) engine that delivers performance and productivity on par with a regular

Volvo FH with the same power rating. [353]

Application 3: Bi-fuel vehicles

Bi-fuel vehicles are those that use biofuels and conventional, oil-based, fuels as sources of

energy for their propulsion systems.

Example: Brazilian flex-fuel vehicles’ program began in the early 2000’s with the

introduction of the gasoline-ethanol based propulsion engine. The second-generation

ethanol (SGE) was a development from this first introduction of alternative fuel sources,

which took off in the late 2000’s with the National Laboratory of Bioethanol

(CTBE/CNPEM). Flex-fueled vehicles represented 21% of the industrial GDP by 2011,

as well as retaining a net revenue of US$ 121.3 billion, clearly seen as a successful

implementation of alternative energy propulsion systems based on biofuels. [194; 303;

304]

Application examples of propulsion by Electro Mobility

Application 1: Plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles are fueled by gasoline/biofuel blends and electricity from the

grid.

Example: Available models include the Chevrolet Volt in U.S. markets [61] (which is the

Opel Ampera in EU markets), and the Toyota Prius Plug-in Hybrid [339].

Application 2: Battery electric vehicles (BEVs)

Battery electric vehicles are fueled by electricity from the grid [366].

Example: Examples for BEVs include the Renault Zoe and the Nissan Leaf [366].


76

Example 2: The Deutsche Post DHL Group currently runs about 5000 StreetScooter and

10000 e-bikes. DHL plans to replace its entire mail and parcel delivery fleet in the mid-

term with electric vehicles that are charged with electricity generated from renewable

energy sources. [83]

Application 3: Fuel cell electric vehicles (FCEVs)

Fuel cell electric vehicles (FCEV) are also vehicles with an electric engine. In contrast to

battery electric vehicles, the energy is generated on board with fuel cells by hydrogen from

natural gas.

Example 1: Semitruck Tesla or Nikola One Hydrogen are two trucks that are expected

to be launched to the market soon (in 2019). For example, Nikola One uses a hydrogen

fuel cell, combined with electric motors with a power of around 1,000 CV and a lithium-

ion battery of 320 kWh. It has autonomy between 800 and 1,600 kilometers. It also

promises acceleration from 0 to 100 km per hour in 30 seconds. [378]

Example 2: PINC is a U.S.-based logistics and asset management specialist that uses

unmanned aerial vehicles (UAVs), or drones, powered by fuel cells. The UAVs can be

used to track and monitor assets and inventory at yards and other industrial and supply

chain sites. [246]


Technological Gaps:

The major technology gaps, especially for vehicles powered by electro mobility, are the range

and durability of the propulsion (e.g. battery) [103]. The degradation of power and energy

storage capacity that occurs over time are part of these technological gaps.

Other technological gaps regarding alternative propulsion systems are limited make-model

availability and limited refueling infrastructure [103]. The workshop and service network are

also insufficient.

A special technology gap for advanced biofuels is the limitation of feedstock supply, depending

on the biofuel source [250]. There are also supply issues with core elements (e.g. nickel,

lithium and cobalt) for lithium-ion batteries for electric vehicles [184].


To overcome the technological gaps, some of the implementation challenges will be system

standards for the charging and fast charging mechanisms, and energy storage with high

energy density. The decrease of investment costs for the technology are another challenge

that could be addressed by advances in compression and storage technology. [103]

To evaluate the technology, a total CO2 assessment, including manufacturing and disposal of

vehicles, will be necessary.

The technologies and processes for creating advanced biofuels also have to be improved in

order to be more reliable, efficient and cost-effective [250].


77

Renewable Energy Technologies for Production and Storage

EU has set objectives for 2030 to have at least 27% of renewables in its mix and reduce

Greenhouse Gas emissions by 40% compared to 1990 levels [116]. Thus, the energy system

relies more heavily on renewable energy resources to minimise emissions. There are several

technologies such as flywheel energy storage, hydrogen production and storage and

advanced biofuels for energy production and storage that play a growing role in these efforts.

Flywheel energy storage (FES) transforms electrical energy into mechanical energy storing it

in the form of rotational kinetic energy [224].

Hydrogen production is the family of industrial methods for generating hydrogen and hydrogen

storage technologies with the help of renewable-based electrolysis. This allows to transport

and store energy from renewables efficiently over long periods of time. At a large scale,

hydrogen can be stored in underground salt caverns in pure or methanised form. [152]

Last but not least, advanced biofuels are produced by technologies and processes that convert

a broad spectrum of plant, waste and cellulose molecules into hydrocarbon molecules [10].

Biofuels can be classified into first generation biofuels produced primarily from foods crops

(e.g. grains, sugar cane and vegetable oils) and second generation biofuels produced from

cellulosic energy crops (e.g. Miscanthus and SRC willow, agricultural forestry residues or

woody biomass) [240; 303; 304]. Third-generation biofuels are produced from algae. By 2050

biofuels will provide 27% of total transport fuel minimising around 2.1 gigatonne CO2

emissions per year when produced sustainably [182].


The technology Renewable Energy Technologies for Production and Storage was evaluated

based on expert workshops and literature review. As shown in Figure 3-32, the TRL Category

is A (Market presence) based on the level of application examples, the technological gaps and

implementation challenges. For the manufacturing sector the applicability of the technology is

broad, while the process sector sees a moderate applicability. In the logistics sector there is

only a limited applicability of renewable energy technologies.

Figure 3-32: Technology Evaluation Renewable Energy Technologies for Production and Storage

Figure 3-33 shows the implications of Renewable Energy Technologies for Production and

Storage on the supply chain performance for the three sectors (Manufacturing, Process and

Logistics).


78

Figure 3-33: Implications of Renewable Energy Technologies on SC Performance

The implications of Renewable Energy Technologies on the supply chain performance are

characterised by:

Agility: Energy storage systems aim at storing electrical energy in order to reduce the

time for the supply of energy and the fluctuations of supply and demand in the grid,

which has a positive implication on the agility [119].

Costs: Regarding a longer time horizon, renewable energy technologies will allow

general cost reductions, which will undoubtedly reflect on SC costs [183; 346].

Transparency: Renewable energy technologies will have no implication on the

transparency. The criterion is not relevant for this technology.

Responsiveness: Due to an on-demand energy supply, the responsiveness will be

higher [119].

Reliability: The greater independence of industry from the electricity grid will make the

system more reliable. The fast response characteristics of flywheels for example, will

make them suitable for grid frequency balancing [17].

Sustainability: Renewable energy technologies will decrease carbon footprint and have

significant effect on neutral solutions production with regards to CO2 levels associated

with production processes. The ability to convert surplus electricity into a green fuel for

energy storage, and then use this emission-free fuel for power transportation and

industry creates real value to the existing infrastructure [41].


Application example of flywheel energy storage

Example: Utility Hawaiian Electric has launched a pilot project using Amber Kinetics’

flywheel energy storage technology. The project will test the technology’s capability of

supporting the grid and allowing further integration of renewable generation. [165]

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


79

Application example of hydrogen production and storage technology

Example: CSIRO is planning a pilot project for testing a technology capable of

processing a 100% pure stream of hydrogen from gasified ammonia using a metal

membrane [255].

Application example of advanced biofuels

Example: Arnold-Blume Bioenergie is a German company that runs a biogas plant.

Currently, 560 kW of electrical power and 640 kW of heat are being fed into the supply

grid by two combined heat and power plants (CHPs), designed for a total capacity of

1,454 kW. [310]


Technological Gaps:

For the flywheel energy storage, the major technology gap is the fast discharge time

restraining growth of the technology. Furthermore, the use of flywheel accumulators can cause

risk of explosion. [224]

The main gap for hydrogen production and storage technology are high prices. The technology

will have to achieve ‘fossil parity’ to be successful. This is the point where hydrogen energy

production will be on-parity with the price of the electricity accessed from the grid [41].

For advanced biofuels, the costs are a decisive technological gap too, since they consume

high energy as part of their production processes [47].


Creating economic and sustainable production processes for the hydrogen and biofuels will

be major implementation challenges for this technology. Therefore, business scalability will be

decisive. [41]

The coordination for effective integration of increasing volumes of biofuels into the

transportation fuels market will require large infrastructure investments. The willingness of

investors to finance these investments will depend to a large extent on the perceived

willingness of consumers to adopt changing technologies and fuels. [294]

Smart Materials

Smart Materials are magnetically or electrically controllable materials with outstanding

mechanical properties. These materials play an increasingly important role in the development

of innovative, versatile and efficient products. The aim of smart material research is to

systematically open up the application potential of different smart materials for customers

across all industries in order to generate better, lighter and more powerful or more energy-

efficient products with a wide range of new functions [127].

Smart materials can be classified into property changing and energy exchanging smart

materials [237].


80

Property changing smart materials change one or more of their properties (chemical, electrical,

magnetic, mechanical, or thermal) as a direct reaction to a change in external stimuli in the

environment [237].

Energy exchanging smart materials transform energy from one form to another. The energy

input on a material changes the energy state of the material composition, but not the material

itself. The material stays the same, while the energy changes. [237]


The technology Smart Materials was evaluated based on expert workshops and literature

review. As shown in Figure 3-34, the TRL Categories range from C (Applied research) to B

(Market-ready) based on the level of application examples, the technological gaps and



only a limited applicability of smart materials.

Figure 3-34: Technology Evaluation Smart Materials

Figure 3-35 shows the implications of Smart Materials on the supply chain performance for


Figure 3-35: Implications of Smart Materials on SC Performance

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


81

The implications of Smart Materials on the supply chain performance are characterised by:

Agility: Due to their characteristics and their applications on supply chain

manufacturing process (e.g. smart colouring), smart materials have a slightly positive

implication on the agility from the manufacturing sector’s point of view [203]. For the

process sector, smart materials will lead to the detection of potential dysfunctionality

or threats in advance, thus improving supply chain agility.

Costs: Smart materials are cost-effective solutions since the low-maintenance design

reduces operating costs. On the other hand, as a new emerging technology it is

considered to be expensive [220].

Transparency: Smart materials will not have an implication on the supply chain

transparency except when combined with other technologies (e.g. RFID, Real-Time

Tracking System).

Responsiveness: Due to their inherent characteristics smart materials have a slightly

positive implication on the agility from the manufacturing sector’s point of view.

Reliability: As smart materials and its production processes offer a range of benefits

including superior strength and toughness, enhanced durability and increased

resistance to abrasion, corrosion, chemicals and fatigue, the technology will have a

positive implication on the supply chain reliability [237].

Sustainability: Smart materials are a potential alternative way of generating electrical

energy by converting mechanical energy, such as footsteps and other effortless

mechanical movements [351; 360]. The technology will also lead to an improved

response to extreme events (e.g. natural disasters and fire), and reuse and recycling

capacities [237]. Thus, it will have positive implications on the sustainability due to its

ability for self-healing and the avoiding of pollution [237].


Application examples of property changing smart materials

Thermochromics (i.e. change of colour due to thermal energy), phototropics (i.e. change of

colour due to light) or shape memory are examples for this kind of smart materials [237].

Application examples of energy exchanging smart materials

Light-emitting materials, thermoelectrics or photovoltaics are examples for this kind of smart

materials [237].

Application examples of smart materials in general

Application 1: Smart Labelling

Smart labels are able to detect changes in temperature, freshness, oxygen concentration and

carbon dioxide concentration [226].

Example: P&G’s Auto-ID Centre at the MIT. Most smart labels are based on RFID

technology and consist of a thin consumable label with a microchip and an antenna that

connect to form a transponder. Chipless tagging which is often based on advanced


82

magnetic material technologies is also currently being researched as it is considered

more cost-effective. [226]

Application 2: Smart Materials in automotive sector

Smart materials can be used in the automotive sector for shock absorbers, air bags or anti-

lock braking systems (ABS). Micro-Electro-Mechanical-Systems (MEMS) and electrochromic

materials are utilised as sensors in the automotive sector. MEMS devices are used for the

deployment of air bags or anti-lock braking systems (ABS). Electrochromic materials are used

in automatic light and heat control (e.g. self-dimming mirrors and rear windows). [226]

Application 3: Smart packaging

Smart packaging can be divided into two main categories: active and intelligent packaging. In

active packaging, the packs are becoming active in response to a particular triggering event.

Examples are metal containers based on self-heating or self-cooling. In intelligent packaging,

the package informs the consumer or operator by communicating visually in response to

changes regarding its external or internal conditions. An example for intelligent packaging is

a time-temperature integration information for shelf-life sensitive products. [226]


Technological Gaps:

Major technological gaps concern the complexity of the materials. As there are no in-depth

design tools for smart materials, they require experts who are experienced in smart materials.

Especially for achieving a long lifetime duration, the design process must be performed

carefully. [361]

Furthermore, the implementation of smart materials is limited due to issues of recyclability,

dependence on rare elements and the lack of efficient manufacturing processes [115].

Other main gaps for smart materials are the fear of risk, lack of cognition and the high costs

of the technology [237].


To overcome the gaps and create social acceptance, smart materials have to be introduced

to people in order to demonstrate the possibilities and advantages of the technology [237].

Smart materials are interdisciplinary as they are characterised by the coupling of two or more

physical domains (e.g. mechanical, electrical, chemical, thermal and optical). Therefore,

expertise in multiple disciplines is necessary for the conception and design of new solutions.

[213]

There are specific challenges for each individual smart material in order to make maximum

use of it. For example, the application of shape-memory alloys differs from conventional

actuation technologies used in mechanisms because of their unique properties and behaviour.

Shape-memory alloys must be incorporated very early in the design process to eliminate

issues resulting from drop-in of shape-memory alloys into traditional designs. Moreover, due

to their thermally controlled nature, component and system design should be engineered

carefully to permit operation over wide ranges of usage conditions and environments. [362]


83

Nanotechnology

Nanotechnology describes modification of material on an atomic, molecular, and

supramolecular level. It also includes the development of functional systems on a molecular

level and design objects from the bottom up to create sophisticated products. With

nanotechnology it is possible to manipulate properties at a very small level which opens up a

wide variety of application. [102]

The category Nanotechnology includes Bionic and Nanolithography technologies. Bionics

stands for “learning from nature”. The benefit of this new field of research is the use of solutions

that have been optimised due to the process of natural selection over generations [106].

Nanolithography is the science of etching, writing or printing to modify a material surface with

structures under 100nm (nanometres) [291]. Nanolithography transfers a geometric pattern

from a pre-fabricated photomask to a photoresist layer applied to a thin film material or the

bulk of substrate using lights, charged ions, or electron beams [375]. Subsequently, a series

of processes is applied for chemically engraving the transferred pattern into the target material

[375].


The technology Nanotechnology was evaluated based on expert workshops and literature

review. As shown in Figure 3-36, the TRL Ceategories range from C (Applied research) to B

(Market-ready) based on the level of application examples, the technological gaps and



only a limited applicability of nanotechnology.

Figure 3-36: Technology Evaluation Nanotechnology

Figure 3-37 shows the implications of Nanotechnology on the supply chain performance for



84

Figure 3-37: Implications of Nanotechnology on SC Performance

The implications of Nanotechnology on the supply chain performance are characterised by:

Agility: The manufacturing sector will see a huge development when it comes to

nanotechnology. After all, nanotechnology can create lighter, sturdier, and safer

materials that can withstand great pressures and weights – which will lead to improved

performances [76]. Nanotechnology creates surfaces which don’t require to be cleaned

as many times as traditional materials. The process and logistics sectors don’t expect

an implication on the agility.

Costs: Nanotechnology will decrease costs in a limited range due to less maintenance

and more efficiency. Nanotechnology-enabled lubricants and engine oils will reduce

wear and tear, which can extend the lifetimes of moving parts in all areas from power

tools to industrial machinery [249].

Transparency: The nanotechnology itself will have no implication on the supply chain

transparency. However, if it is combined with IoT and Communication Infrastructure, it

may lead to a higher level of transparency and traceability in the supply chain.

Responsiveness: Due to new optimised catalytic processes, the implication on the

responsiveness will be positive for the process sector. Therefore, there will be more

efficiency and faster processes in the process industry [35; 227]. For the manufacturing

sector, there will be no implications on the responsiveness.

Reliability: Nanotechnology will reinforce the reliability of the outcomes due to

mechanisms that evaluate the surface and provide information regarding temperature

and toxic levels.

Sustainability: Nanotechnology will lead to better engine efficiency, reduced

environmental impact and improved electronic systems [193; 261].

-2

-1

0

1

2



Strongly positive

Positive

Neutral

Negative

Stronglynegative


85


Application examples of Bionic

Example 1: There is paint based on shell crab that does not allow the proliferation of

microorganisms. It can be used to paint places where it is desired to reduce the rate of

bacterial proliferation.

Example 2: The use of nano sensors that shows whether the merchandise has been

exposed to oxygen, or if the temperature has changed beyond the limits, are other

examples of Bionic.

Application examples of Nanolithography

Example 1: In single step electrochemical nanolithography (ENL), metal films are

electrochemically processed at the nanoscale without the need to apply a mask using

atomic force microscope (AFM). Electrochemical surface reactions can be precisely

located by a conductive AFM tip using the applied nano range voltage pulse potential.

ENL by AFM makes it possible to control the length scale of nanopatterns more precisely

and in real-time for the formed nanostructure. [37]

Example 2: Self-healing coatings are regarded as an alternative for efficient anti-

corrosion protection with a low need for cathodic protection. Such coatings typically

consist of micro- or nano-capsules that include film-formers and repair the coating

damage when the coating is scratched. [308]

Example 3: Nanofibres production has been achieved by use of the electrospinning

technology to produce three dimensional, ultra-fine fibres with diameters in the range of

a few nanometers (more typically 100 nm to 1 micron) and lengths up to kilometers [261].

Example 4: The Nano Spray Dryer B-90 uses a vibrating mesh technology for the

generation of nano particles by spraying [49].

Applications of Nanotechnology in general

Application 1: Nanotechnology in food packaging

Nanotechnology can be used to revolutionise food packaging and safety by promising longer

shelf life, safer packaging, better traceability of food products and healthier food. There are

some examples of immobilised enzymes which can be used as antimicrobial components,

oxygen scavengers or nanosensors. [46]

Example: Researchers at the Technische Universität München (TUM) have developed

a gas sensor which can be integrated into food packaging to gauge freshness [281].


Technological Gaps:

A major technological gap is the limited production. Due to the limited speed of production in

3D printing, the mass production of nanotechnology is a considerable challenge [33].


86

Another main gap is the lack of knowledge in the area of nanotechnology. Universities and

other researchers are still learning how atoms can fit together to firm larger structures. In this

context, another problem is that there are no specific nanotechnology funding available. [33]


Regarding the technological gaps, the main implementation challenges are scaling up the

production of nanotechnology and the need for universities and researchers with

multidisciplinary skills. They also have to understand the complete processes of

nanotechnology in order to structure and organise the nanotechnologies in such a way that

they retain their properties when assembled into a device. An evaluation of the toxicity and

environmental risks of nanomaterials will also be essential. [334]


87

4 Conclusion and summary

This report has presented the most important enabling technologies that emerged through the

technology scouting. Moreover, application examples and gap analyses with the relevant

technology gaps and implementation challenges for each technology were also provided.

The technologies were evaluated regarding their influence on the future supply chain based

on the applicability for the industry sectors and the implication on the supply chain

performance. Although a relatively high number of technologies were studied and to some

extent numerically processed, the nature of this study remains largely qualitative due to the

level of interpretation our experts deployed in the assessment tasks.

The six technologies with the highest applicability and the most positive implications on the

supply chain performance over all three industry sectors are the following:

Internet of Things (positive implications on the supply chain performance, especially

on agility, costs, transparency and responsiveness)

Distributed Ledger / Blockchain (positive implications, especially on costs,

transparency and reliability)

Artificial Intelligence (positive implications, especially on agility, costs and

responsiveness)

Data Science (positive implications, especially on agility, transparency and

responsiveness)

Identification Technologies (positive implications, especially on agility, transparency,

responsiveness and reliability)

Additive Manufacturing (positive implications, especially on agility, responsiveness

and sustainability)

Furthermore, the following technologies have a high significance, especially for the following

sectors:

Manufacturing and Logistics sector

o Autonomous Transport Systems (positive implications, especially on agility,

costs and transparency)

Process and Logistics sector

o Cloud Based Computer Systems (positive implications, especially on costs,

transparency and responsiveness)

Manufacturing and Process sector

o Communication Infrastructure (positive implications on supply chain

performance for all criteria)

Therefore, the main focus for future research regarding the implications of technologies for

future supply chains should be on these nine enabling technologies. It is important to

emphasise that some technologies can be complementary, since they imply that combinations

and systems of technologies provide a higher level and a more powerful enabler. This implies

that the implications of technologies can be more powerful if they are evaluated as an


88

overarching system. Therefore, the work in the next tasks of the project will also enable a more

comprehensive and complementary analysis of groups of technologies applied to specific

supply chain models and dimensions (e.g. operations, processes, structure).

The gap analyses of the technologies revealed some frequent technology gaps and

implementation challenges. These relate to the standardisation of the respective technology

and, for a wide range of technologies, data storage, availability and accessibility and further

processing of these data (e.g. in form of algorithms).

In fact, System Integration is one of the most important implementation challenges which have

been identified in all the nine most important technologies that will enable cross-company,

universal data-integration networks with different subsystems, including hardware, software

and communications, that need to be integrated [44; 135]. System Integration should be

carried out both horizontally and vertically ensuring different types of collaboration at different

levels of supply chain. Vertical integration focuses on integrating processes across the entire

organisation, via the networking of smart production systems, smart products and smart

logistics while horizontal integration encompasses networking along the entire supply chain,

from suppliers and business partners to customers, in order to achieve a seamless

cooperation between companies.

Due to the increased data exchange and connectivity, there is growing requirement for

systems protection with the aid of cyber security [44]. Therefore, appropriate secure and

reliable levels of protection regarding the identity and access management of machines,

networks, clouds and users have to be ensured with advanced systems such as firewalls, DNS

filtering, malware protection and antivirus software [44; 62; 132].

Figure 4-1 shows the nine most significant technologies, their interdependencies, and their

most important implementation challenges Cyber Security Systems and System Integration.

Figure 4-1: Focus technologies for future research

Cyber Security Systems

and

System Integration

Autonomous

Transport Systems

Cloud Based

Computer Systems

Internet of Things

Distributed Ledger/

Blockchain

Artificial

Intelligence

Data

Science

Communication

Infrastructure

Identification

Technologies

Additive

Manufacturing


89

Apart from these nine technologies, the process industry will also focus on renewable energy

technologies and the logistics sector on location technologies as important enabling

technologies.

It is expected that with respect to supply chain models, some technologies only influence

operations without influencing the structure of the supply chain or revenue mechanism, while

others affect core production and distribution capacity and require a redesign of the network

itself. In fact, business impact and supply chain impact is not the same.

On the basis of the SC future scenarios and the identified enabling technologies in this report,

a mapping of the enabling technologies necessary to implement each specific SC scenario

will be performed in order to define the research priorities for the strategic research agenda.


90

References

[1] 5GACIA (2018), 5G for Connected Industries and Automation.

[2] AB&R (2016), “Warehouse Inventory Management System”, available at:

http://www.abr.com/warehouse-inventory-management-system/ (accessed 2 July 2018).

[3] ABB (2018), “YuMi®. Creating an automated future together”, available at:

https://new.abb.com/products/robotics/it/robot-industriali/yumi (accessed 28 June 2018).

[4] ABI Research (2018), “Smart Footwear to Provide Unique Data Opportunities for

Enterprises and Healthcare Providers”, available at: https://www.prnewswire.com/news-

releases/smart-footwear-to-provide-unique-data-opportunities-for-enterprises-and-

healthcare-providers-300591076.html (accessed 10 July 2018).

[5] Accenture (2016), Editing the uneditable Blockchain: Why distributed ledger technology

must adapt to an imperfect world.

[6] Accenture (2018), “GRTgaz. Establishing an agile, scalable IT infrastructure with

accenture cloud platform”, available at: https://www.accenture.com/us-en/success-

grtgaz-establish-it-infrastructure-accenture-cloud-platform (accessed 28 June 2018).

[7] Accenture (2018), “Predictive Oil and Gas Analytics at Woodside. Woodside’s Shaun

Gregory explains how predictive analytics will transform their LNG business”, available

at: https://www.accenture.com/us-en/insight-perspectives-energy-woodside-predicts-the-

future (accessed 29 June 2018).

[8] Acieta (2018), “Five reasons to use robotics in manufacturing”, available at:

https://www.acieta.com/why-robotic-automation/robotics-manufacturing/ (accessed 28

June 2018).

[9] Adams, R.L. (2017), “10 Powerful Examples Of Artificial Intelligence In Use Today”,

available at: https://www.forbes.com/sites/robertadams/2017/01/10/10-powerful-

examples-of-artificial-intelligence-in-use-today/#26584f2f420d (accessed 3 July 2018).

[10] Advanced Biofuels Association (2018), “Biofuels”, available at:

http://advancedbiofuelsassociation.com/section.php?sid=2 (accessed 2 July 2018).

[11] Agorasti Toka, Eirini Aivazidou, Konstantinos Arvanitopoulos-Darginis and Antonios

Antoniou (2013), “Cloud Computing in Supply Chain Management: An Overview”, in

Graham, D., Folinas, D. and Manikas, I. (Eds.), E-logistics and e-supply chain

management: Applications for evolving business, IGI Global (701 E. Chocolate Avenue

Hershey Pennsylvania 17033 USA), Hershey, Pa, pp. 218–231.

[12] AIG (2018), Lithium-ion Battery Energy Storage Systems: The risks and how to

manage them.

[13] Akpakwu, G.A., Silva, B.J., Hancke, G.P. and Abu-Mahfouz, A.M. (2018), “A Survey on

5G Networks for the Internet of Things: Communication Technologies and Challenges”,

IEEE Access, Vol. 6, pp. 3619–3647.


91

[14] ALICE - Alliance for Logistics Innovation trough Collaboration in Europe (2015),

Information Systems for Interconnected Logistics: Research & Innovation Roadmap.

[15] Alsadi, S. (2015), “UPS Leading the Prescriptive Analytics Race”, available at:

https://digit.hbs.org/submission/ups-leading-the-prescriptive-analytics-race/ (accessed 29

June 2018).

[16] Amazon (2016), “amazon Prime Air”, available at: https://www.amazon.com/Amazon-

Prime-Air/b?node=8037720011 (accessed 28 June 2018).

[17] Amiryar, M. and Pullen, K. (2017), “A Review of Flywheel Energy Storage System

Technologies and Their Applications”, Applied Sciences, Vol. 7 No. 3, p. 286.

[18] Angeles, R. (2015), “Green Sustainability Using Radio Frequency Identification.

Technology-Organization-Environment Perspective Using Two Case Studies”,

International Journal of Humanities and Social Sciences, Vol. 9 No. 11, pp. 3876–3884.

[19] AnyLogic (2018), “Oil and Gas Simulation Software”, available at:

https://www.anylogic.com/oil-and-gas/ (accessed 29 June 2018).

[20] Apache (2018), “Apache Cassandra”, available at: http://cassandra.apache.org/

(accessed 29 June 2018).

[21] Apache (2018), “Apache Hadoop”, available at: http://hadoop.apache.org/ (accessed

29 June 2018).

[22] Apache (2018), “Apache Spark”, available at: https://spark.apache.org/ (accessed 29

June 2018).

[23] Apics for Business (2018), “SCOR Metrics”, available at: https://www.apics.org/apics-

for-business/benchmarking/scormark-process/scor-metrics (accessed 28 June 2018).

[24] Apprenda (2018), “IaaS, PaaS, SaaS (Explained and Compared)”, available at:

https://apprenda.com/library/paas/iaas-paas-saas-explained-compared/ (accessed 28

June 2018).

[25] Arias, O., Wurm, J., Hoang, K. and Jin, Y. (2015), “Privacy and Security in Internet of

Things and Wearable Devices”, IEEE Transactions on Multi-Scale Computing Systems,

Vol. 1 No. 2, pp. 99–109.

[26] ARPost (2018), “Cyber Security Challenges of Virtual and Augmented Reality”,

available at: https://arpost.co/2018/03/28/cyber-security-challenges-virtual-reality-

augmented-reality/ (accessed 2 July 2018).

[27] Arriba-Pérez, F. de, Caeiro-Rodríguez, M. and Santos-Gago, J.M. (2016), “Collection

and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-

User Scenarios”, Sensors (Basel, Switzerland), Vol. 16 No. 9.

[28] Arthur, R. (2015), “Hands-On With Tommy Hilfiger's In-Store Virtual Reality Catwalk

Experience”, available at: https://www.forbes.com/sites/rachelarthur/2015/10/25/hands-

on-with-tommy-hilfigers-in-store-virtual-reality-catwalk-experience/#4da8e3f23ee4

(accessed 3 July 2018).


92

[29] Asorey, H. (2018), “How AI Is Transforming Forecasting for the Better”, available at:

https://www.salesforce.com/quotable/articles/how-AI-is-transforming-forecasting-for-the-

better/ (accessed 5 July 2018).

[30] Ballarino, A., Brondi, C., Brusaferri, A. and Chizzoli, G. (2017), “The CPS and LCA

Modelling: An Integrated Approach in the Environmental Sustainability Perspective”, in

Camarinha-Matos, L.M., Afsarmanesh, H. and Fornasiero, R. (Eds.), Collaboration in a

data-rich world: 18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE

2017, Vincenza, Italy, September 18-20, 2017 proceedings, IFIP advances in information

and communication technology, Vol. 506, Springer, Cham, pp. 543–552.

[31] Barton, D. and Court, D. (2012), Making Advanced Analytics Work For You: A practical

guide to capitalizing on big data. Harvard Business Review (ed.).

[32] Baturynska, I., Semeniuta, O. and Martinsen, K. (2018), “Optimization of Process

Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine

Learning and Finite Element Method: A Conceptual Framework”, Procedia CIRP, Vol. 67,

pp. 227–232.

[33] BDO LLP (2014), Technologies in Manufacturing: Nanotechnology.

[34] Berkers, F. and Jansen, N. (2017), Inspire Project D 2.1: Assessment of Relevant

Technologies.

[35] Berndorff, J. (2017), “Nanotechnology enables new insights into chemical reactions”,

available at: https://www.psi.ch/media/nanotechnology-enables-new-insights-into-

chemical-reactions (accessed 2 July 2018).

[36] Best, J. (2014), “The race to 5G. Inside the fight for the future of mobile as we know it”,

available at: https://www.techrepublic.com/article/does-the-world-really-need-5g/


[37] Bhattacharyya, B. (2015), “Nanofeatures on Metals and Semiconductors for

Nanotechnology Applications”, in Electrochemical Micromachining for Nanofabrication,

MEMS and Nanotechnology, Elsevier, pp. 241–258.

[38] Bicer, Y. and Dincer, I. (2017), “Comparative life cycle assessment of hydrogen,

methanol and electric vehicles from well to wheel”, International Journal of Hydrogen

Energy, Vol. 42 No. 6, pp. 3767–3777.

[39] Bisht, A. (2016), “What is GLONASS And How It Is Different From GPS”, available at:

https://beebom.com/what-is-glonass-and-how-it-is-different-from-gps/ (accessed 4 July

2018).

[40] BlueYonder (2018), “Supply Chain Solution. Demand Forecast & Replenishment”


[41] Boeker, P. (2017), “What is the Future of Hydrogen Technology? [Interview]”, available

at: http://blog.ballard.com/future-of-hydrogen-technology (accessed 2 July 2018).


93

[42] Bonnín Roca, J., Vaishnav, P., Morgan, M.G., Mendonça, J. and Fuchs, E. (2017),

“When risks cannot be seen: Regulating uncertainty in emerging technologies”, Research

Policy, Vol. 46 No. 7, pp. 1215–1233.

[43] Boon, W. and van Wee, B. (2017), “Influence of 3D printing on transport: a theory and

experts judgment based conceptual model”, Transport Reviews.

[44] Boston Consulting Group (2018), “Embracing Industry 4.0. And Rediscovering

Growth”, available at: https://www.bcg.com/capabilities/operations/embracing-industry-

4.0-rediscovering-growth.aspx (accessed 3 July 2018).

[45] Boudjou, M. (2018), “RFID Technology Improves Product Quality and Process

Efficiency for Diverse Pharmaceutical Applications”, available at:

https://www.pharmpro.com/article/2012/04/rfid-technology-improves-product-quality-and-

process-efficiency-diverse (accessed 2 July 2018).

[46] Bowles, M. and Lu, J. (2013), “Potential Application of Nanotechnology in Agricultural

Products Logistics Management”, Journal of Convergence Information Technology,

Vol. 8 No. 2, pp. 812–819.

[47] Bowyer, J., Howe, J., Levins, R.A., Groot, H., Fernholz, K., Pepke, E. and Henderson,

C. (2018), Third Generation Biofuels: Implications for Wood-Derived Fuels.

[48] Brühl, V. (2017), “Bitcoins, Blockchain und Distributed Ledgers”, Wirtschaftsdienst,

Vol. 97 No. 2, pp. 135–142.

[49] Büchi (2018), “Nano Spray Dryer B-90 HP . Small particles, small samples, high

yields”, available at: https://www.buchi.com/en/products/spray-drying-and-

encapsulation/nano-spray-dryer-b-90-hp (accessed 2 July 2018).

[50] Bugdahn, P. (2017), “How Autonomous Trucks Will Change the Trucking Industry”,

available at: https://www.geotab.com/blog/autonomous-trucks/ (accessed 28 June 2018).

[51] Buyya, R. and Gill, S.S. (2017), A Taxonomy and Future Directions for Sustainable

Cloud Computing: 360 Degree View.

[52] Buyya, R. and Gill, S.S. (2018), “Sustainable Cloud Computing: Foundations and

Future Directions”, Business Technology & Digital Transformation Strategies, Vol. 21

No. 6, pp. 1–9.

[53] Byrd, D. (2018), “Deep Offshore. An AUV to inspect pipelines”, available at:

https://www.ep.total.com/en/areas/deep-offshore/auv-optimize-pipeline-inspections


[54] Cao, L. (2017), “Data Science”, ACM Computing Surveys, Vol. 50 No. 3, pp. 1–42.

[55] Capgemini (2015), Wearable Devices and their Applicability in the Life Insurance

Industry: Introducing wearables into the life insurance customer journey.

[56] Cerasis (2017), The Most Impactful Supply Chain & Logistics Trends in 2017. Cerasis

(ed.).


94

[57] Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C. and Campo, E. (2012), “Smart

wearable systems: current status and future challenges”, Artificial intelligence in

medicine, Vol. 56 No. 3, pp. 137–156.

[58] Chan, S., Chu, J., Nadarajah, S. and Osterrieder, J. (2017), “A Statistical Analysis of

Cryptocurrencies”, Journal of Risk and Financial Management, Vol. 10 No. 2, p. 12.

[59] Chen, M., Wan, J., Gonzalez, S., Liao, X. and Leung, V.C.M. (2014), “A Survey of

Recent Developments in Home M2M Networks”, IEEE Communications Surveys &

Tutorials, Vol. 16 No. 1, pp. 98–114.

[60] Chen, Z. (2016), Research on the impact of 3d printing on the international supply

chain. Hindawi (ed.).

[61] Chevrolet, “Chevrolet 2018 Volt. Plug-in-Hybrid”.

[62] Cisco, “What Is Cybersecurity?”, available at:

https://www.cisco.com/c/en/us/products/security/what-is-cybersecurity.html (accessed 24

July 2018).

[63] Cision (2016), “Smart Clothing and Body Sensors. Market Analysis and Forecasts”,

available at: https://www.prnewswire.com/news-releases/smart-clothing-and-body-

sensors-market-analysis-and-forecasts-300360342.html (accessed 29 June 2018).

[64] Civitas (2015), Intelligent Transport Systems and traffic management in urban areas.

[65] Clausen, U., Holloh, K.D. and Kadow, M. (2014), “Visions of the future: transportation

and logistics 2030, examining the potential for the development of road and rail

transportation to 2030”.

[66] CloudFactory (2017), “5 Exciting Machine Learning Use Cases in Business”, available

at: https://www.iotforall.com/5-exciting-machine-learning-use-cases-business/ (accessed

29 June 2018).

[67] Cocco, L., Pinna, A. and Marchesi, M. (2017), “Banking on Blockchain: Costs Savings

Thanks to the Blockchain Technology”, Future Internet, Vol. 9 No. 3, p. 25.

[68] Cohen, E.E. (2018), Blockchain Interoperability: The Main Issues and How

ISO/TC 307 Is Considering Addressing Them.

[69] Columbus, L. (2014), “Where Cloud Computing Is Improving Supply Chain

Performance. Lessons Learned From SCM World”, available at:

https://www.forbes.com/sites/louiscolumbus/2014/02/12/where-cloud-computing-is-

improving-supply-chain-performance-lessons-learned-from-scm-world/#5948bbb467d3


[70] Commission on Engineering and Technical Systems (1995), Unit manufacturing

processes: Issues and opportunities in research, National Academy Pr, Washington, DC.

[71] Csiro (2016), “Advanced Manufacturing A Roadmap for unlocking future growth

opportunities for Australia”.


95

[72] Daimler (2018), “Autonomer Pionier. Mercedes-Benz Future Truck 2025”, available at:

https://www.daimler.com/innovation/autonomes-fahren/mercedes-benz-future-truck.html


[73] Dalkilic, F., Cabuk, U.C., Arikan, E. and Gurkan, A. (2017), “An analysis of the

positioning accuracy of iBeacon technology in indoor environments”, International

Conference on Computer Science and Engineering (UBMK), pp. 549–553.

[74] Dalmarco, G. and Barros, A.C. (2018), “Adoption of Industry 4.0 Technologies in

Supply Chains”, in Moreira, A.C., Ferreira, L.M.D.F. and Zimmermann, R.A. (Eds.),

Innovation and Supply Chain Management, Vol. 28, Springer International Publishing,

Cham, pp. 303–319.

[75] Davenport, T.H. and D.J. Patil (2012), “Data Scientist. The Sexiest Job of the 21st

Century”, Harvard business review, Vol. 90 No. 10, pp. 70–76.

[76] Dean Group (2017), “Nanotechnology in the Manufacturing Industry”, available at:

https://www.deangroup-int.co.uk/nanotechnology-in-the-manufacturing-industry/


[77] Deckers, J., Vleugels, J. and Kruth, J.-P. (2014), “Additive Manufacturing of Ceramics.

A Review”, Journal of Ceramic Science and Technology, Vol. 05 No. 04, pp. 245–260.

[78] Deka, G.C. (2016), “Big Data Predictive and Prescriptive Analytics”, in Management

Association, I.R. (Ed.), Big Data, IGI Global, pp. 30–55.

[79] Deloitte (2017), 2017 retail, wholesale, and distribution industry outlook. Deloitte (ed.).

[80] Deloitte (2017), Augmented/Virtual Reality: Next Big Thing of Digital Environment.

[81] Deloitte (2017), How much is that virtual doggie in the virtual window?: Virtual and

augmented reality – a guide for Australian retailers.

[82] Deloitte (2017), The 2017 MHI Annual Industry Report Next-Generation Supply

Chains: Digital, On-Demand and Always-On. Deloitte (ed.).

[83] Deutsche Post DHL Group (2018), “Electro mobility”, available at:

https://www.dpdhl.com/en/media-relations/specials/electro-mobility.html (accessed 2 July

2018).

[84] DHL (2015), “DHL successfully tests Augmented Reality application in warehouse”,

available at:

http://www.dhl.com/en/press/releases/releases_2015/logistics/dhl_successfully_tests_au

gmented_reality_application_in_warehouse.html (accessed 29 June 2018).

[85] DHL (2018), “DHL and Accenture unlock the power of blockchain in logistics”,

available at: https://www.dpdhl.com/en/media-relations/press-releases/2018/dhl-and-

accenture-unlock-the-power-of-blockchain-in-logistics.html (accessed 29 June 2018).

[86] DHL Customer Solutions & Innovation (2013), Low-cost sensor technology: A DHL

perspective on implications and use cases for the logistics industry. DHL Customer

Solution & Innovation (ed.).


96

[87] DHL Customer Solutions & Innovation (2014), Augmented reality in logistics: Changing

the way we see logistics - a DHL perspective. DHL Customer Solution & Innovation (ed.).

[88] DHL Customer Solutions & Innovation (2016), 3D Printing and the Future of Supply

Chains: A DHL perspective on the state of 3D printing and implications for logistics.

[89] DHL Customer Solutions & Innovation (2016), Logistics Trend Radar: Delivering

insight today. Creating value tomorrow! DHL Customer Solution & Innovation (ed.).

[90] DHL Customer Solutions & Innovation (2017), The future of life sciences and

healthcare logistics: A DHL perspective on key trends and technologies. DHL Customer

Solution & Innovation (ed.).

[91] DHL Customer Solutions & Innovation (2018), Artificial Intelligence in Logistics.

[92] DHL Customer Solutions & Innovation (2018), Blockchain in Logistics: Perspectives on

the upcoming impact of blockchain technology and use cases for the logistics industry.

[93] Dickson, B. (2017), “Artificial Intelligence has to deal with its transparency problems”,

available at: https://thenextweb.com/artificial-intelligence/2017/04/23/artificial-

intelligence-has-to-deal-with-its-transparency-problems/ (accessed 29 June 2018).

[94] Dickson, B. (2018), “The limits and challenges of deep learning”, available at:

https://bdtechtalks.com/2018/02/27/limits-challenges-deep-learning-gary-marcus/


[95] Dictionary of Military and Associated Terms (2005), “Automated Identification

Technology”, available at:

https://www.thefreedictionary.com/automated+identification+technology (accessed 17

July 2018).

[96] Dieterich, V., Ivanovic, M., Meier, T., Utz, M. and Sandner, P. (2017), Application of

Blockchain Technology in the Manufacturing Industry.

[97] Dixit, G. (2017), “Internet of Things in the Oil and Gas Industry”, available at:

https://www.spe.org/en/twa/twa-article-detail/?art=2744 (accessed 28 June 2018).

[98] DMG Mori (2018), “Lasertec 65 3D. Additive Manufacturing in Milling Quality”,

available at: http://media.dmgmori.com/media/epaper/lasertec_65_3D_uk/index.html#0


[99] Doorey, D.J. (2011), “The Transparent Supply Chain: from Resistance to

Implementation at Nike and Levi-Strauss”, Journal of Business Ethics, Vol. 103 No. 4, pp.

587–603.

[100] Dorfman, P. (2018), “3 Advances Changing the Future of Artificial Intelligence in

Manufacturing”, available at: https://www.autodesk.com/redshift/future-of-artificial-

intelligence/ (accessed 29 June 2018).

[101] DotEcon Ltd and Axon Partners Group (2018), Study on Implications of 5G

Deployment on Future Business Models.


97

[102] Dwivedi, A. and Dwivedi, A. (2012), “Emerging Trends in Nano Technology for Modern

Industries”, International Journal of Engineering and Innovative Technology (IJEIT),

Vol. 2 No. 6, pp. 4–13.

[103] Egbue, O. and Long, S. (2012), “Barriers to widespread adoption of electric vehicles:

An analysis of consumer attitudes and perceptions”, Energy Policy, Vol. 48, pp. 717–729.

[104] Egels-Zandén, N., Hulthén, K. and Wulff, G. (2015), “Trade-offs in supply chain

transparency: the case of Nudie Jeans Co”, Journal of Cleaner Production, Vol. 107, pp.

95–104.

[105] Elberg, R. (2018), “From smart grid to neural grid. Well, how do I get there?”, available

at: https://www.utilitydive.com/news/from-smart-grid-to-neural-grid-well-how-do-i-get-

there/518619/ (accessed 2 July 2018).

[106] Emmelmann, C., Sander, P., Kranz, J. and Wycisk, E. (2011), “Laser Additive

Manufacturing and Bionics: Redefining Lightweight Design”, Physics Procedia, Vol. 12,

pp. 364–368.

[107] Endsley, C. (2015), “Looking Into the Feasibility of Self-Driving Trains”, available at:

https://gtgtechnologygroup.com/technology-and-self-driving-trains/ (accessed 28 June

2018).

[108] Engerati (2018), “5 technologies enabling the neural grid”, available at:

https://www.engerati.com/smart-infrastructure/article/smart-grids/5-technologies-

enabling-neural-grid (accessed 10 July 2018).

[109] EOS (2018), “EOS M 400. 3D Printing of Metal Parts on an Industrial Scale”, available

at: https://www.eos.info/systems_solutions/metal/systems_equipment/eos_m_400


[110] Etherum (2018), “Etherum. Blockchain App Platform”, available at:

https://www.ethereum.org/ (accessed 29 June 2018).

[111] European Commission (2014), “Factories 4.0: The Future of European Manufacturing”,

available at: https://ec.europa.eu/digital-single-market/en/blog/factories-40-future-

european-manufacturing (accessed 31 October 2017).

[112] European Commission (2016), Deliverable 2.1: Analysis of Technology Readiness.

[113] European Commission (2017), Digital Transformation Scoreboard 2017.

[114] European Commission (2017), Horizon 2020 Work Programme 2018-2020: 19.

General Annexes.

[115] European Commission (2017), Smart materials, systems and structures for energy

harvesting.

[116] European Commission (2018), “2030 Energy Strategy”, available at:

https://ec.europa.eu/energy/en/topics/energy-strategy-and-energy-union/2030-energy-

strategy (accessed 5 July 2018).


98

[117] Ezhilarasan, E. and Dinakaran, M., “A Review on Mobile Technologies: 3G, 4G and

5G”, pp. 369–373.

[118] Feldmann, R., Hammer, M., Somers, K. and van Niel, J. (2016), “Buried treasure.

Advanced analytics in process industries”, available at:

https://www.mckinsey.com/business-functions/operations/our-insights/buried-treasure-

advanced-analytics-in-process-industries (accessed 29 June 2018).

[119] Ferreira, H.L., Garde, R., Fulli, G., Kling, W. and Lopes, J.P. (2013), “Characterisation

of electrical energy storage technologies”, Energy, Vol. 53, pp. 288–298.

[120] Fetch Robotics (2018), “Process orders faster, increase efficiency, and lower costs.”,

available at: https://fetchrobotics.com/products-technology/virtualconveyor/ (accessed 28

June 2018).

[121] Finboot Tech (2018), “Finboot”, available at: https://finboot.com/ (accessed 29 June

2018).

[122] Flämig, H. (2016), “Autonomous Vehicles and Autonomous Driving in Freight

Transport”, in Maurer, M., Gerdes, J.C., Lenz, B. and Winner, H. (Eds.), Autonomous

Driving, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 365–385.

[123] Fotiadis, D., Glaros, C. and Likas, A. (2006), Wearable Medical Devices. Wiley

Encyclopedia of Biomedical Engineering (ed.).

[124] Fraunhofer Austria (2018), “Safety und Security Konzept zur Mensch-Roboter-

Kollaboration in der Produktion”, available at:

https://www.fraunhofer.at/de/forschung/forschungsfelder/gestaltung-von-cyber-

physikalischen-montagesystemen/Safety-und-Security-Konzept-zur-MRK.html (accessed

28 June 2018).

[125] Fraunhofer IGD (2018), “Visual Computing”, available at:

https://www.igd.fraunhofer.de/en/node/885 (accessed 2 July 2018).

[126] Fraunhofer Institute for Material Flow and Logistics (2018), “SaLsA - Safe Autonomous

Logistics and Transportation Vehicles in Outdoor Areas”, available at:

https://www.iml.fraunhofer.de/en/fields_of_activity/automation_embedded_systems/resea

rch/SaLsA.html (accessed 28 June 2018).

[127] Fraunhofer ISC (2018), “Smart Materials”, available at:

https://www.isc.fraunhofer.de/en/fields-of-activity/materials/smart-materials.html


[128] FTA - Freight Transport Association (2017), Logistics Report 2017. FTA - Freight

Transport Association (ed.).

[129] Gandomi, A. and Haider, M. (2015), “Beyond the hype: Big data concepts, methods,

and analytics”, International Journal of Information Management, Vol. 35 No. 2, pp. 137–

144.

[130] Gangale, F., Vasiljevska, J., Covrig, C.F., Mengolini, A. and Fulli, G. (2017), Smart grid

projects outlook 2017.


99

[131] Gartner (2017), “Why Blockchain’s Smart Contracts Aren’t Ready for the Business

World”, available at: https://www.gartner.com/smarterwithgartner/why-blockchains-smart-

contracts-arent-ready-for-the-business-world/ (accessed 5 July 2018).

[132] Gartner (2018), “Digital business requires cybersecurity. Manage cybersecurity risk

effectively across an evolving risk landscape”, available at:

https://www.gartner.com/en/information-technology/insights/cybersecurity (accessed 3

July 2018).

[133] Gartner IT Glossary (2018), “Business Process as a Service (BPaaS)”, available at:

https://www.gartner.com/it-glossary/business-process-as-a-service-bpaas/ (accessed 28

June 2018).

[134] Gartner IT Glossary (2018), “Electro Mobility (E-Mobility)”, available at:

https://www.gartner.com/it-glossary/electro-mobility-e-mobility/ (accessed 2 July 2018).

[135] Gartner IT Glossary (2018), “System Integration”, available at:

https://www.gartner.com/it-glossary/system-integration (accessed 24 July 2018).

[136] GE Additive (2018), “Additive Manufacturing. 3D Printing in the Oil and Gas Industry”,

available at: https://www.ge.com/additive/additive-manufacturing/industries/oil-gas


[137] GE Additive (2018), “What is Additive Manufacturing?”, available at:

https://www.ge.com/additive/additive-manufacturing (accessed 10 July 2018).

[138] Gebler, M., Schoot Uiterkamp, A.J.M. and Visser, C. (2014), “A global sustainability

perspective on 3D printing technologies”, Energy Policy, Vol. 74, pp. 158–167.

[139] Georgoulis, S. (2016), “The NB-IoT standard. Its benefits and test challenges”,

available at: https://cobhamwireless.com/validation/resources/blog/nb-iot-standard-

benefits-test-challenges/ (accessed 29 June 2018).

[140] Gernandt, K. (2012), “Megatrends and their impact on logistics”, TLI - Asia Pacific

White Papers Series, No. 12.

[141] Ghent University (2018), “Localization and Tracking”, available at:

https://www.ugent.be/ea/idlab/en/research/wireless-networking/localization-and-

tracking.htm (accessed 2 July 2018).

[142] Ghi, A. and Rossetti, F. (2016), “4D Printing: An Emerging Technology in

Manufacturing?”, in Caporarello, L., Cesaroni, F., Giesecke, R. and Missikoff, M. (Eds.),

Digitally Supported Innovation, Vol. 18, Springer International Publishing, Cham, pp.

171–178.

[143] GLUE Content (2018), “Challenges of Augmented Reality for Businesses”, available

at: http://gluecontent.com.au/2018/04/19/challenges-of-augmented-reality-for-

businesses/ (accessed 2 July 2018).

[144] Goldthau, A. (2014), “Rethinking the governance of energy infrastructure: Scale,

decentralization and polycentrism”, Energy Research & Social Science, Vol. 1, pp. 134–

140.


100

[145] Green, K.W., Zelbst, P.J., Sower, V.E. and Bellah, J.C. (2017), “Impact of Radio

Frequency Identification Technology on Environmental Sustainability”, Journal of

Computer Information Systems, Vol. 57 No. 3, pp. 269–277.

[146] Guo, Y., Hu, X., Hu, B., Cheng, J., Zhou, M. and Kwok, R.Y.K. (2018), “Mobile Cyber

Physical Systems: Current Challenges and Future Networking Applications”, IEEE

Access, Vol. 6, pp. 12360–12368.

[147] Hahn, G.J. and Packowski, J. (2015), “A perspective on applications of in-memory

analytics in supply chain management”, Decision Support Systems, Vol. 76, pp. 45–52.

[148] Hallward-Driemeier, M. and Nayyar, G. (2017), Trouble in the Making?: The Future of

Manufacturing-Led Development. World Bank Group (ed.).

[149] Hanuska, A., Chandramohan, B., Bellamy, L., Burke, P., Ramanathan, R. and

Balakrishnan, V., Smart Clothing Market Analysis.

[150] Hao, Y. and Helo, P. (2017), “The role of wearable devices in meeting the needs of

cloud manufacturing: A case study”, Robotics and Computer-Integrated Manufacturing,

Vol. 45, pp. 168–179.

[151] Harpley, B. (2016), “Six Challenges for NB-IoT Deployment”, available at:

https://iotuk.org.uk/six-challenges-for-nb-iot-deployment/ (accessed 29 June 2018).

[152] Hart, D., Howes, J. and Lehner, F. (2015), Scenarios for deployment of hydrogen in

contributing to meeting carbon budgets and the 2050 target. E4tech (ed.).

[153] Hassanalian, M. and Abdelkefi, A. (2017), “Classifications, applications, and design

challenges of drones: A review”, Progress in Aerospace Sciences, Vol. 91, pp. 99–131.

[154] Heard, B.R., Taiebat, M., Xu, M. and Miller, S.A. (2018), “Sustainability implications of

connected and autonomous vehicles for the food supply chain”, Resources,

Conservation and Recycling, Vol. 128, pp. 22–24.

[155] Heineke, K., Kampshoff, P., Mkrtchyan, A. and Shao, E. (2017), Self-driving car

technology: When will the robots hit the road. McKinsey & Company (ed.).

[156] Helle, P., Schamai, W. and Strobel, C. (2016), “Testing of Autonomous Systems.

Challenges and Current State-of-the-Art”, 26th Annual INCOSE International

Symposium, Edinburg, Scotland, 18-21 July 2016, available at:

file:///C:/Users/mstute/Downloads/TestingofAutonomousSystems-

ChallengesandCurrentState-of-the-Art_v7.pdf (accessed 18 July 2018).

[157] Henderson, J. (2017), “Wärtsilä successfully tests remote control ship operating

capability, directs vessel from 8,000km”, available at:

https://www.supplychaindigital.com/distribution/wartsila-successfully-tests-remote-

control-ship-operating-capability-directs-vessel (accessed 28 June 2018).

[158] Hesselbarth, J. (2017), “#2 logistics-The role of autonomous systems in connection

with logistics”, available at: https://www.janhesselbarth.com/single-post/2017/06/14/2---

The-role-of-autonomous-systems-in-connection-with-logistics (accessed 28 June 2018).


101

[159] Hoffenberg, S. (2017), “NextGen Supply Chain. Expanding the Market for Remote

Asset Tracking”, available at:

http://www.scmr.com/article/nextgen_supply_chain_expanding_the_market_for_remote_

asset_tracking (accessed 2 July 2018).

[160] Hong-ying, S., “The Application of Barcode Technology in Logistics and Warehouse

Management”, pp. 732–735.

[161] Horenberg, 2., “Applications within Logistics 4.0 A research conducted on the visions

of 3PL service providers”.

[162] Hornyak, T. (2018), “10 Robotics Challenges Range From Battery Power and Medicine

to Ethics”, available at: https://www.roboticsbusinessreview.com/robo-dev/robotics-

research-challenges-range-from-battery-power-and-medicine-to-ethics/ (accessed 28

June 2018).

[163] HP (2018), “The 3D printer revolution starts now. Meet the mighty HP Voxel”, available

at: http://www8.hp.com/us/en/printers/3d-printers.html (accessed 2 July 2018).

[164] Hsu, J. (2014), “Why big data will have a big impact on sustainability”, available at:

https://www.theguardian.com/sustainable-business/big-data-impact-sustainable-business


[165] Hutchins, M. (2018), “Pilot project for flywheel storage underway in Hawaii”, available

at: https://pv-magazine-usa.com/2018/03/14/pilot-project-for-flywheel-storage-underway-

in-hawaii/ (accessed 2 July 2018).

[166] Iapp (2017), “Lack of transparency in AI decision. Making a growing concern”,

available at: https://iapp.org/news/a/limited-transparency-behind-ai-decisions-proves-to-

be-concern-for-researchers/ (accessed 29 June 2018).

[167] IBA Global Employment Institute (2017), Artificial Intelligence and Robotics and Their

Impact on the Workplace.

[168] IBM (2017), “Maersk and IBM Unveil First Industry-Wide Cross-Border Supply Chain

Solution on Blockchain”, available at: https://www-

03.ibm.com/press/us/en/pressrelease/51712.wss (accessed 29 June 2018).

[169] IBM (2018), “How an energy company retains the knowledge of its senior experts and

passes it to new employees. Instant Expertise”, available at:

https://www.ibm.com/watson/stories/woodside/ (accessed 29 June 2018).

[170] IBM (2018), “Hyperledger. Blockchain collaboration changing the business world”,

available at: https://www.ibm.com/blockchain/hyperledger (accessed 29 June 2018).

[171] IBM Chemicals and Petroleum (2017), Digital transformation in oil and gas: How

innovative technologies modernize exploration and production.

[172] IDG Connect and Oracle (2016), The Future for Cloud-Based Supply Chain

Management Solutions: A global survey of attitudes and future plans for the adoption of

supply chain management solutions in the cloud.

[173] IGI Global (2017), “What is Technological Development”.


102

[174] Ikundi, F., Islam, R. and White, P. (2017), “Platform as a Service (PaaS) in Public

Cloud. Challenges and Mitigating Strategy”, in Deng, R., Weng, J., Ren, K. and

Yegneswaran, V. (Eds.), Security and Privacy in Communication Networks, Springer

International Publishing, Cham, pp. 296–304.

[175] Indoorway (2017), “Indoor positioning brings safety and transparency to industrial

processes”, available at: https://blog.indoorway.com/indoor-positioning-brings-safety-

and-transparency-to-industrial-processes-78e39fc2be49 (accessed 2 July 2018).

[176] Infor Manufacturing (2016), Meet the Factory of the Future, Where information

technology (IT) and operational technology (OT) converge. Infor Manufacturing (ed.).

[177] Infsoft (2018), “Indoor Navigation & Tracking in Industrial Areas”, available at:

https://www.infsoft.com/industries/industry/features (accessed 2 July 2018).

[178] Infsoft (2018), Indoor Positioning & Services.

[179] Intelligent Trading Technology (2017), “Blockchain Could Shorten Settlement Time,

Paxos Exec Says”, available at: https://intelligenttradingtechnology.com/post-trade-

processes/blog/blockchain-could-shorten-settlement-time-paxos-exec-says (accessed 5

July 2018).

[180] International Electrotechnical Commission (IEC) (2017), Electrical Energy Storage –

White paper: Project of the international electrotechnical commission (IEC) market

strategy board and the Fraunhofer institut für solare energiesysteme.

[181] International Electrotechnical Commission (IEC) (2018), “Smart Grid. Challenges”,

available at: http://www.iec.ch/smartgrid/challenges/ (accessed 10 July 2018).

[182] International Energy Agency (IEA) (2011), Biofuels for Transport Roadmap.

[183] International Energy Agency (IEA) (2014), Technology Roadmap: Energy storage.

[184] International Energy Agency (IEA) (2018), “Strong policy and falling battery costs drive

another record year for electric cars”, available at:

https://www.iea.org/newsroom/news/2018/may/strong-policy-and-falling-battery-costs-

drive-another-record-year-for-electric-ca.html (accessed 19 July 2018).

[185] International Finance Corporation (2017), Beyond Fintech: Leveraging Blockchain for

More Sustainable and Inclusive Supply Chains.

[186] Investopedia (2018), “Cryptocurrency”, available at:

https://www.investopedia.com/terms/c/cryptocurrency.asp (accessed 6 July 2018).

[187] I-Scoop (2017), “Augmented reality and virtual reality trends and use cases in IIoT”,

available at: https://www.i-scoop.eu/industry-40-virtual-reality-vr-augmented-reality-ar-

trends/ (accessed 2 July 2018).

[188] ISO - International Organization for Standardization (2016), “Robots and robotic

devices - Collaborative robots. 15066:2016”, available at:

https://www.iso.org/standard/62996.html (accessed 28 June 2018).


103

[189] Jadeja, Y. and Modi, K. (2012), “Cloud Computing. Concepts, Architecture and

Challenges”, International Conference on Computing, Electronics and Electrical

Technologies (ICCEET), available at:

https://www.researchgate.net/profile/Kirit_Modi/publication/254035330_Cloud_computing

_-_concepts_architecture_and_challenges/links/545715880cf26d5090a97114/Cloud-

computing-concepts-architecture-and-challenges.pdf (accessed 28 June 2018).

[190] Jamaludin, Z., Huong, C.Y., Abdullah, L., Nordin, M.H., Abdullah, M.F., Haron, R. and

Jalal, K.B.A. (2018), “Automated Tracking System Using RFID for Sustainable

Management of Material Handling in an Automobile Parts Manufacturer”, Vol. 10 No. 1-7,

pp. 35–40.

[191] Jara, A.J., Genoud, D. and Bocchi, Y., “Big Data for Cyber Physical Systems: An

Analysis of Challenges, Solutions and Opportunities”, pp. 376–380.

[192] Javadi, S. and Seifvand, S. (2017), “NB-IoT Applications in Smart Grid. Survey and

Research Challenges”, International Journal of Communications, No. 11, pp. 74–77.

[193] Jenck, J.F., Agterberg, F. and Droescher, M.J. (2004), “Products and processes for a

sustainable chemical industry: a review of achievements and prospects”, Green

Chemistry, Vol. 6 No. 11, p. 544.

[194] Joseph Jr., H. (2013), “Flex Fuel Vehicles in Brazil. Energy & Environment Affairs

Commission”, available at:

http://www.globalbioenergy.org/fileadmin/user_upload/gbep/docs/2013_events/GBEP_Bi

oenergy_Week_Brasilia_18-23_March_2013/4.5_JOSEPH.pdf (accessed 10 July 2018).

[195] Katal, A., Wazid, M. and Goudar, R.H. (2013), “Big data: Issues, challenges, tools and

Good practices. In: Sixth International Conference on Contemporary Computing (IC3), 8-

10 August 2013, Noida, India”, available at:

https://ieeexplore.ieee.org/document/6612229/#full-text-section (accessed 10 July 2018).

[196] Kersten, W., Seiter, M., See, B. von, Hackius, N. and Maurer, T. (2017), Trends And

Strategies In Logistics And Supply Chain Management: Digital Transformation

Opportunities. BVL International - The Supply Chain Network (ed.).

[197] Khurana, D., Koli, A., Khatter, K. and Singh, S. (2017), Natural Language Processing:

State of The Art, Current Trends and Challenges.

[198] Knight, H. (2018), “Alstom on track for autonomous train technology trial”, available at:

https://www.theengineer.co.uk/alstom-autonomous-train-technology/ (accessed 28 June

2018).

[199] Knight, W. (2018), “Google’s self-training AI turns coders into machine-learning

masters”, available at: https://www.technologyreview.com/s/609996/googles-self-training-

ai-turns-coders-into-machine-learning-masters/ (accessed 29 June 2018).

[200] KPMG (2015), Connected and Autonomous Vehicles –Opportunity The UK Economic.

KPMG (ed.).

[201] Kritikos, K. (2017), BPaaS Monitoring and Evaluation Prototypes.


104

[202] Kuang, C. (2018), “Can A.I. Be Taught to Explain Itself?”, available at:

https://www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-

itself.html?_r=0 (accessed 29 June 2018).

[203] Kumar Mishra, A. (2016), Nanomaterials for Water Remediation, Smithers Rapra,

Shrewsbury.

[204] Landen, I. (2017), “Confused about 4G 5G LTE NB-IoT? Here are the basics and key

differences”, available at: https://www.bluewireless.com/single-

post/2017/08/14/Confused-about-4G-5G-LTE-NB-IoT-Here-are-the-basics-and-key-

differences (accessed 29 June 2018).

[205] Laudal, T. (2010), “An Attempt to Determine the CSR Potential of the International

Clothing Business”, Journal of Business Ethics, Vol. 96 No. 1, pp. 63–77.

[206] Lee, A.Y., An, J. and Chua, C.K. (2017), “Two-Way 4D Printing: A Review on the

Reversibility of 3D-Printed Shape Memory Materials”, Engineering, Vol. 3 No. 5, pp. 663–

674.

[207] Lennerholt, C., van Laere, J. and Söderström, E. (2018), “Implementation Challenges

of Self Service Business Intelligence. A Literature Review”, In 51st Hawaii International

Conference on System Sciences, Hilton Waikoloa Village, Hawaii, USA, January 4-7,

2017 (Vol. 51, pp. 5055-5063), available at: https://www.diva-

portal.org/smash/get/diva2:1170841/FULLTEXT01.pdf (accessed 29 June 2018).

[208] Li, D. (2016), “Perspective for smart factory in petrochemical industry”, Computers &

Chemical Engineering, Vol. 91, pp. 136–148.

[209] Liang, X., Shetty, S., Tosh, D., Kamhoua, C., Kwiat, K. and Njilla, L., “ProvChain: A

Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced

Privacy and Availability”, pp. 468–477.

[210] Liu, B., Yu, X.L., Chen, S., Xu, X. and Zhu, L., “Blockchain Based Data Integrity

Service Framework for IoT Data”, pp. 468–475.

[211] Liu, G., Geng, Y. and Pahlavan, K. (2015), “Effects of calibration RFID tags on

performance of inertial navigation in indoor environment”, International Conference on

Computing, Networking and Communications (ICNC), pp. 945–949.

[212] Liu, Y., Peng, Y., Wang, B., Yao, S. and Liu, Z. (2017), “Review on cyber-physical

systems”, IEEE/CAA Journal of Automatica Sinica, Vol. 4 No. 1, pp. 27–40.

[213] Lopes Junior, V., Steffen, V. and Savi, M.A. (Eds.) (2016), Dynamics of Smart

Systems and Structures, Springer International Publishing, Cham.

[214] Lorenz, M., Küpper, D., Rüßmann, M. and Heidemann, A. Bause, A. (2015), Time to

Accelerate in the Race Toward Industry 4.0. Boston Consulting Group (ed.).

[215] Low, C. (2017), “4 Technical Challenges AR and VR Need To Solve In 2018”,

available at: https://datafloq.com/read/4-technical-challenges-ar-vr-need-to-solve/4148



105

[216] Lucas, M. (2017), “The difference between Bitcoin and blockchain for business”,

available at: https://www.ibm.com/blogs/blockchain/2017/05/the-difference-between-

bitcoin-and-blockchain-for-business/ (accessed 10 July 2018).

[217] Lux Research (2018), “Technology Scouting and Open Innovation”, available at:

http://www.luxresearchinc.com/solutions/technology-scouting-and-open-innovation


[218] Magazino (2018), “TORU. Pick-by-Robot for warehouses”, available at:

https://www.magazino.eu/toru-cube/?lang=en (accessed 28 June 2018).

[219] Mahmood, M.A., Seah, W.K.G. and Welch, I. (2015), “Reliability in wireless sensor

networks: A survey and challenges ahead”, Computer Networks, Vol. 79, pp. 166–187.

[220] Mangadlao, J.D., Cao, P. and Advincula, R.C. (2015), “Smart cements and cement

additives for oil and gas operations”, Journal of Petroleum Science and Engineering,

Vol. 129, pp. 63–76.

[221] Mardonova, M. and Choi, Y. (2018), “Review of Wearable Device Technology and Its

Applications to the Mining Industry”, Energies, Vol. 11 No. 3, p. 547.

[222] Maria, A. (1997), “Introduction to modeling and simulation”, Proceedings of the 1997

Winter Simulation Conference, available at:

http://acqnotes.com/Attachments/White%20Paper%20Introduction%20to%20Modeling%

20and%20Simulation%20by%20Anu%20Maria.pdf (accessed 29 June 2018).

[223] Marinacci, J. (2017), “Natural language processing bridges the gap between

computers and humans”, available at: https://www.itproportal.com/features/natural-

language-processing-bridges-the-gap-between-computers-and-humans/ (accessed 29

June 2018).

[224] MarketWatch (2018), “Global flywheel energy storage market growth expected to be

driven by, growing demand for eco-friendly power sources”, available at:

https://www.marketwatch.com/story/global-flywheel-energy-storage-market-growth-

expected-to-be-driven-by-growing-demand-for-eco-friendly-power-sources-2018-02-19


[225] Massawe, L.V., Kinyua, J.D.M. and Vermaak, H. (2012), “Reducing false negative

reads in RFID data streams using an adaptive sliding-window approach”, Sensors

(Basel, Switzerland), Vol. 12 No. 4, pp. 4187–4212.

[226] Materials Foresight, Smart materials for the 21st Century.

[227] Matheson, R. (2013), “Nanotechnology startup targets hydrocarbons to transform oil

refining”, available at: https://www.nanowerk.com/news2/newsid=33348.php (accessed 2

July 2018).

[228] Mautz, R. (2012), Indoor Positioning Technologies: A Survey, 1. Aufl.,

Südwestdeutscher Verlag für Hochschulschriften, Saarbrücken.

[229] Mcafee, A.and Brynjolfsson, E. (2012), “Big data: the management revolution”,

Harvard business review, Vol. 90, pp. 60–68.


106

[230] McCarthy, D. (2017), “IoT and the Digitalization of Oil and Gas Production”, available

at: https://pgjonline.com/news/2017/11/iot-and-the-digitalization-of-oil-and-gas-production


[231] McDaniel, P. and McLaughlin, S. (2009), “Security and Privacy Challenges in the

Smart Grid”, IEEE Security & Privacy Magazine, Vol. 7 No. 3, pp. 75–77.

[232] McKinsey (2017), Smartening up with Artificial Intelligence (AI): What’s in it for

Germany and its Industrial Sector?

[233] Meng, Z. and Li, Z. (2016), “RFID Tag as a Sensor - A Review on the Innovative

Designs and Applications”, Measurement Science Review, Vol. 16 No. 6, pp. 305–315.

[234] Merlinoa,M. and Sproģeb, I. (2017), “The Augmented Supply Chain”, Procedia

Engineering, Vol. 178 No. 308-318.

[235] Metzger, A. (2017), Big Data Research and Innovation: A Gap Analysis from the

Perspective of the European Big Data Value PPP.

[236] Mineraud, J., Mazhelis, O., Su, X. and Tarkoma, S. (2016), “A gap analysis of Internet-

of-Things platforms”, Computer Communications, 89-90, pp. 5–16.

[237] Mohamed, A.S.Y. (2017), “Smart Materials Innovative Technologies in architecture;

Towards Innovative design paradigm”, Energy Procedia, Vol. 115, pp. 139–154.

[238] Mohamed, N., Al-Jaroodi, J., Lazarova-Molnar, S. and Jawha, I. (2017), “Middleware

Challenges for Cyber-Physical Systems”, Scalable Computing: Practice and Experience,

Vol. 18 No. 4.

[239] Mohd, A., Ortjohann, E., Schmelter, A., Hamsic, N. and Morton, D. (2008), “Challenges

in integrating distributed Energy storage systems into future smart grid”, IEEE

International Symposium on Industrial Electronics, pp. 1627–1632.

[240] Mohr, A. and Raman, S. (2013), “Lessons from first generation biofuels and

implications for the sustainability appraisal of second generation biofuels”, Energy Policy,

Vol. 63 No. 100, pp. 114–122.

[241] MongoDB (2018), “mongoDB”, available at: https://www.mongodb.com/ (accessed 29

June 2018).

[242] Morgado, A., Huq, K.M.S., Mumtaz, S. and Rodriguez, J. (2018), “A survey of 5G

technologies: regulatory, standardization and industrial perspectives”, Digital

Communications and Networks, Vol. 4 No. 2, pp. 87–97.

[243] Moser, T. (2017), “IoT Is Ready for the Process Industry”, available at:

https://automation.isa.org/2017/07/iot-industrial-process-industry/ (accessed 28 June

2018).

[244] MotorVillage (2018), “MotorVillage Digital Store”, available at:

http://www.motorvillagedigitalstore.it/ (accessed 3 July 2018).


107

[245] Mozenix (2018), “How Augmented & Virtual Reality Technology Is Impacting Oil &

Gas”, available at: https://mozenix.com/2018/04/09/augmented-virtual-reality-technology-

impacting-oil-gas/ (accessed 3 July 2018).

[246] Murray, J. (2017), “Why this U.S. logistics giant will use drones powered by fuel cells”,

available at: https://www.greenbiz.com/article/why-us-logistics-giant-will-use-drones-

powered-fuel-cells (accessed 2 July 2018).

[247] Narain, A. (2017), “Autonomous Cars are a passé. Let’s talk about Autonomous

Trains!”, available at: https://www.geospatialworld.net/blogs/autonomous-trains/


[248] National Maritime (2018), “Is Automation the Future of Shipping?”, available at:

https://www.nmdg.co.uk/news/automation-future-shipping/ (accessed 28 June 2018).

[249] National Nanotechnology Initiative, “Nanotechnology & You. Benefits and

Applications”, available at: https://www.nano.gov/you/nanotechnology-benefits (accessed

2 July 2018).

[250] National Petroleum Council (2012), Advancing Technology for America's

Transportation Future: Chapter Two - Light Duty Vehicles.

[251] Navigant (2018), From Smart Grid to Neural Grid: Industry Transformation and the Top

Five Technologies Poised to Bring the Grid into the Cloud.

[252] Nervous System (2013), “Kinematics”, available at: https://n-e-r-v-o-u-

s.com/projects/sets/kinematics/ (accessed 2 July 2018).

[253] NewGenApps, Industry 4.0: Prepare for the 4th industrial revolution.

[254] NGV Global News (2018), “Status of CNG Mobility in Germany Rising”, available at:

https://www.ngvglobal.com/blog/status-cng-mobility-germany-rising-0222 (accessed 2

July 2018).

[255] Nogrady, B. (2017), “How Australia can use hydrogen to export its solar power around

the world”, available at: https://www.theguardian.com/sustainable-

business/2017/may/19/how-australia-can-use-hydrogen-to-export-its-solar-power-

around-the-world (accessed 2 July 2018).

[256] Norwegian Society of Automatic Control (2013), Opportunities and Challenges for the

Oil and Gas Industry.

[257] Novak-Marcincin, J., Barna, J., Janak, M. and Novakova-Marcincinova, L. (2013),

“Augmented Reality Aided Manufacturing”, Procedia Computer Science, Vol. 25, pp. 23–

31.

[258] Nowak, G., Maluck, J., Stürmer, C. and Pasemann, J. (2016), “The era of digitized

trucking. Transforming the logistics value chain”, available at:

https://www.strategyand.pwc.com/reports/era-of-digitized-trucking (accessed 28 June

2018).

[259] NXP (2011), How RFID is Helping Companies Reduce their Carbon Footprints.


108

[260] O'Boyle, T. (2018), “Leveraging IoT Technology to Gain Supply Chain Visibility”,

available at: https://www.manufacturing.net/article/2018/02/leveraging-iot-technology-

gain-supply-chain-visibility (accessed 4 July 2018).

[261] Okoli, J.U., Briggs, T.A. and Major, I.E. (2013), “Application of Nanotechnology in the

Manufacturing Sector. A Review”, Nigerian Journal of Technology (NIJOTECH), Vol. 32

No. 3, pp. 379–385.

[262] Ondiflo (2018), “Blockchain Ticketing Solution. A Blockchain Platform to Automate All

Ticketing-Based Services for the Oil and Gas Industry”, available at:

https://www.ondiflo.com/ (accessed 29 June 2018).

[263] Oracle (2016), “Oracle Announces Pfizer’s Selection of Oracle Cloud for Clinical Data

Management and Trial Management across its Clinical Trial Portfolio”, available at:

https://www.oracle.com/corporate/press/pfizer-selects-oraclecloud-062016.html


[264] Osseiran, A., Sachs, J. and Puleri, M. (2015), Manufacturing reengineered: Robots,

5G and the Industrial IoT.

[265] Oussous, A., Benjelloun, F.-Z., Ait Lahcen, A. and Belfkih, S. (2017), “Big Data

technologies: A survey”, Journal of King Saud University - Computer and Information

Sciences.

[266] Papageorgiou, M. (2017), “4D Printing. A technology coming from the future”, available

at: https://www.sculpteo.com/blog/2017/10/25/4d-printing-a-technology-coming-from-the-

future/ (accessed 2 July 2018).

[267] Parlings, M. (2017), “Supply Chain Management 4.0 und Shadowed Supply Chains.

Presentation held at CeMat@Hannover Messe, 27 April 2017, Hannover”, available at:

http://publica.fraunhofer.de/documents/N-480315.html (accessed 29 June 2018).

[268] Percepto (2018), “Energy Giant Enel Selects Percepto's Sparrow Drone for Use at

Power Plant”, available at: https://www.prnewswire.com/news-releases/energy-giant-

enel-selects-perceptos-sparrow-drone-for-use-at-power-plant-300651861.html (accessed

28 June 2018).

[269] Plant Automation Technology (2018), “Types of Sensors Used in Industrial

Automation”, available at: https://www.plantautomation-technology.com/articles/types-of-

sensors-used-in-industrial-automation (accessed 5 July 2018).

[270] Poushneh, A. and Vasquez-Parraga, A.Z. (2017), “Discernible impact of augmented

reality on retail customer's experience, satisfaction and willingness to buy”, Journal of

Retailing and Consumer Services, Vol. 34, pp. 229–234.

[271] Predix (2018), “Predix Platform. The Foundation for Digital Industrial Applications”,

available at: https://www.ge.com/digital/predix-platform-foundation-digital-industrial-

applications (accessed 28 June 2018).

[272] Predix (2018), “What is a digital twin?”, available at:

https://www.ge.com/digital/predix/digital-twin (accessed 28 June 2018).


109

[273] ProGlove (2018), “1st Smart Glove for Industries”, available at:

https://www.proglove.de/ (accessed 10 July 2018).

[274] PwC - Price Waterhouse Coopers (2009), Transportation & logistics 2030 series, How

will supply chains evolve in an energy-constrained, low-carbon world? PwC - Price

Waterhouse Coopers (ed.).

[275] PwC - Price Waterhouse Coopers (2016), 2016 industrial manufacturing trends,

Manufacturers must weather the risk that comes with embracing new technologies. PwC

- Price Waterhouse Coopers (ed.).

[276] PwC - Price Waterhouse Coopers (2017), FinTech Trends Report: A deep dive into

what's driving the FinTech revolution in India. PwC - Price Waterhouse Coopers (ed.).

[277] PwC - Price Waterhouse Coopers (2017), “What are the essential eight

technologies?”, available at: http://usblogs.pwc.com/emerging-technology/a-guide-to-the-

essential-eight-emerging-technologies/ (accessed 31 October 2017).

[278] PwC Governance Insights Center (2017), The Essential Eight technologies: Board

byte: augmented and virtual reality.

[279] Ray, B. (2018), “What is M2M?”, 2018 Update, available at: https://www.link-

labs.com/blog/what-is-m2m (accessed 28 June 2018).

[280] Redcentric (2017), “What is Infrastructure as a Service (IaaS)?”, available at:

http://www.redcentricplc.com/resources/articles/what-is-iaas/ (accessed 28 June 2018).

[281] Regan, P. (2013), “With carbon nanotubes, a path to flexible, low-cost sensors.

Potential applications range from air-quality monitors to electronic skin”, available at:

https://www.tum.de/die-tum/aktuelles/pressemitteilungen/detail/article/31051/ (accessed

4 July 2018).

[282] Rengier, F. Mehndiratta, A., von Tengg-Kobligk. H. Zechmann, C. M. and

Unterhinninghofen, R. Kauczor, H.-U. and Giesel, F. L. (2010), “3D printing based on

imaging data: review of medical applications”.

[283] Reuters Staff (2017), “BP, Shell lead plan for blockchain-based platform for energy

trading”, available at: https://www.reuters.com/article/us-energy-blockchain/bp-shell-lead-

plan-for-blockchain-based-platform-for-energy-trading-idUSKBN1D612I (accessed 29

June 2017).

[284] Rifkin, J. (2014), Digital Europe: The Rise of the Internet of Things and the Economic

Transformation of the EU.

[285] Rijal, S. (2015), RFID Technology in Logistical Activities. ARCADA (ed.).

[286] Rimal, B.P. and Lumb, I. (2017), “The Rise of Cloud Computing in the Era of Emerging

Networked Society”, in Antonopoulos, N. and Gillam, L. (Eds.), Cloud Computing,

Springer International Publishing, Cham, pp. 3–25.

[287] Robotics Online Marketing Team (2017), “Robots in the Oil and Gas Industry”,

available at: https://www.robotics.org/blog-article.cfm/Robots-in-the-Oil-and-Gas-

Industry/40 (accessed 28 June 2018).


110

[288] Roland Berger (2015), The Supply Chain Excellence Study 2015.

[289] Roland Berger (2017), Roland Berger Trend Compendium 2030: Megatrend 5

Dynamic technology & innovation.

[290] Rolls-Royce (2017), Autonomous ships The next step: Autonomous shipping is the

way forward in the maritime industry with Rolls-Royce leading the way in its

development. Rolls-Royce (ed.).

[291] Rouse, M. (2007), “Nanolithography”, available at:

https://whatis.techtarget.com/definition/nanolithography (accessed 5 July 2018).

[292] Rouse, M. (2016), “Drone (unmanned aerial vehicle, UAV)”, available at:

http://internetofthingsagenda.techtarget.com/definition/drone (accessed 12 December

2017).

[293] Rovaglio, M. and Scheele, T. (2011), “Automation IT. Virtual reality improves training in

process industries”, available at: https://ww2.isa.org/standards-and-publications/isa-

publications/intech-magazine/2011/june/automation-it-virtual-reality-improves-training-in-

process-industries/ (accessed 2 July 2018).

[294] Rusco, F. (2012), Biofuels Infrastructure in the United States: Current Status and

Future Challenges.

[295] Sagiroglu, S. and Sinanc, D. (2013), “Big data: A review. International Conference on

Collaboration Technologies and Systems (CTS), 20-24 May 2013, San Diego (USA)”,

available at: https://ieeexplore.ieee.org/document/6567202/ (accessed 10 July 2018).

[296] Salvadori, F., Gehrke, C.S., Oliveira, A.C. de, Campos, M. de and Sausen, P.S.

(2013), “Smart Grid Infrastructure Using a Hybrid Network Architecture”, IEEE

Transactions on Smart Grid, Vol. 4 No. 3, pp. 1630–1639.

[297] Samad, T. and Kiliccote, S. (2012), “Smart grid technologies and applications for the

industrial sector”, Computers & Chemical Engineering, Vol. 47, pp. 76–84.

[298] Sana (2017), “Manufacturing Industry Report: Mastering Industry 4.0 With E-

Commerce”.

[299] Sandy, N. (2015), “Process Intelligence. More than BI”, available at:

https://www.rtinsights.com/process-intelligence-more-than-bi/ (accessed 28 June 2018).

[300] Sarker, M.R., Pandzic, H. and Ortega-Vazquez, M.A. (2015), “Optimal Operation and

Services Scheduling for an Electric Vehicle Battery Swapping Station”, IEEE

Transactions on Power Systems, Vol. 30 No. 2, pp. 901–910.

[301] Saunders, S. (2018), “PostNord Using 3YOURMIND 3D Software Platform to Enhance

Its Customer Offering”, available at: https://3dprint.com/209171/postnord-3yourmind-

partners/ (accessed 2 July 2018).

[302] Schätz, B., Törngren, M., Bensalem, S., Cengarle, M.V., Pfeifer, H., McDermid, J.,

Passerone, R. and Sengiovanni-Vincentelli, A. (2015), Cyber-Physical European

Roadmap & Strategy: D.6 1+2 - Integrated CPS Research Agenda and


111

Recommendations for Action, CyPHERS Project, co-funded through the European

Union’s Framework Programme FP7-ICT.

[303] Senna, P.P. and Ansanelli, S.L.d.M. (2017), “First generation or second generation

ethanol in Brazil? A comparison through economic and social environmental aspects”,

Revista Espacios, Vol. 38 No. 37, pp. 15–21.

[304] Senna, P.P. and Ansanelli, S.L.d.M. (2018), “Second Generation Ethanol.

Technological Intensity on the Integrated Cycle”, U.Porto Journal of Engineering, Vol. 4

No. 1, pp. 67–76.

[305] Sewio (2018), “Real-Time Location System in Industry”, available at:

https://www.sewio.net/rtls-in-logistics/ (accessed 2 July 2018).

[306] Shipchain (2018), “Disrupting Transport & Logistics on the Blockchain”, available at:

https://shipchain.io/ (accessed 29 June 2018).

[307] Shipowners (2017), “Unmanned and autonomous vessels. The legal implications from

a P&I perspective”, available at: https://www.shipownersclub.com/unmanned-

autonomous-vessels-legal-implications-pi-perspective/ (accessed 28 June 2018).

[308] Shrestha, B. (2016), “8 applications of nanocoatings”, available at:

https://www.nanobay.com/news/blog/221-exclusive/6149-8-applications-of-nanocoatings


[309] Sidiq, M., Lanker, T. and Makhdoomi, K. (2017), “Augmented Reality VS Virtual

Reality”, International Journal of Computer Science and Mobile Computing, Vol. 6 No. 6,

pp. 324–327.

[310] Siemens (2012), Arnold-Blume Bioenergie GmbH : Aus einem Guss.

[311] Siemens (2015), “The Future of Oil and Gas. Going Offshore Online”, available at:

https://www.siemens.com/innovation/en/home/pictures-of-the-future/energy-and-

efficiency/the-future-of-oil-and-gas-comos.html (accessed 29 June 2018).

[312] Siemens (2016), “Simulation. Providing a Path to Process Optimization”, available at:

https://www.siemens.com/innovation/en/home/pictures-of-the-future/industry-and-

automation/digital-factory-simulation-in-the-process-industry.html (accessed 29 June

2018).

[313] Siemens (2017), “Prototype Robot Solves Problems without Programming”, available

at: https://www.siemens.com/innovation/en/home/pictures-of-the-future/industry-and-

automation/the-future-of-manufacturing-autonomous-assembly.html (accessed 28 June

2018).

[314] Silbernagel, C., “Additive Manufacturing 101-3. What is material extrusion?”, available

at: http://canadamakes.ca/what-is-material-extrusion/ (accessed 10 July 2018).

[315] Singh, N. (2017), “#3 Smart Clothing Technologies that Could be the Next Big Thing in

Wearables”, available at: https://www.entrepreneur.com/article/290483 (accessed 29

June 2018).


112

[316] Skorobogatjko, A., Romanovs, A., Kunicina, A. (2014), “State of the Art in the

Healthcare Cyber-physical Systems, Information Technology and Management Science”.

[317] Smit, J., Kreutzer, S., Moeller, C. and Carlberg, M., Industry 4.0, European

Parliament's Committee on Industry, Industry, Research and Energy (ITRE). Brussels:

Policy departments A: Economic and Scientific Policy.

[318] Sokal, N. (2017), “NB-IoT. New challenges and opportunities for the Internet of Things

and the new WARP NB-IoT program”, available at: https://medium.com/iot-creators/nb-

iot-new-challenges-and-opportunities-for-the-internet-of-things-a95f78ce628d (accessed

29 June 2018).

[319] Sokovic, M. and Kopac, J. (2006), “RE (reverse engineering) as necessary phase by

rapid product development”, Journal of Materials Processing Technology, Vol. 175 No. 1-

3, pp. 398–403.

[320] Solon, O. (2015), “Cobots. Making It Safe for Humans, Robots to Work Side-by-Side”,

available at: https://www.insurancejournal.com/news/national/2015/08/25/379514.htm


[321] Sood, A. (2015), “Onshore to Offshore. How M2M is Changing the Oil & Gas World”,

available at: https://iot.telefonica.com/blog/onshore-to-offshore-how-m2m-is-changing-

the-oil-gas-world (accessed 28 June 2018).

[322] Sorrell, S. (2015), “Reducing energy demand: A review of issues, challenges and

approaches”, Renewable and Sustainable Energy Reviews, Vol. 47, pp. 74–82.

[323] Stamina (2018), “Mobile robot for part handling in the automotive industry”, available

at: https://www.basystemes.com/en/rd-projects/stamina (accessed 28 June 2018).

[324] Stanway-Williams (2018), “The Benefits Of Electric Vehicles In The Supply Chain. How

electric trucks will revolutionize the supply chain”, available at:

https://channels.theinnovationenterprise.com/articles/the-benefits-of-electric-vehicles-in-

the-supply-chain (accessed 4 July 2018).

[325] Starship Technologies (2018), “A new kind of business. The Self-Driving Delivery

Robot”, available at: https://www.starship.xyz/business/ (accessed 28 June 2018).

[326] Stinson, L. (2013), “So Smart. New Ikea App Places Virtual Furniture In Your Home”,

available at: https://www.wired.com/2013/08/a-new-ikea-app-lets-you-place-3d-furniture-

in-your-home/ (accessed 3 July 2018).

[327] Stoppa, M. and Chiolerio, A. (2014), “Wearable electronics and smart textiles: a critical

review”, Sensors (Basel, Switzerland), Vol. 14 No. 7, pp. 11957–11992.

[328] Stratasys (2018), “Stratasys F900”, available at: http://www.stratasys.com/3d-

printers/stratasys-f900 (accessed 2 July 2018).

[329] Sureka, A. and Indukuri, K.V. (2008), “Using Genetic Algorithms for Parameter

Optimization in Building Predictive Data Mining Models”, in Tang, C. (Ed.), Advanced

data mining and applications: 4th international conference, ADMA 2008, Chengdu,


113

China, October 8 - 10, 2008 ; proceedings, Lecture notes in computer science Lecture

notes in artificial intelligence, Vol. 5139, Springer, Berlin, Heidelberg, pp. 260–271.

[330] Sustainable Process Industry through Resource and Energy Efficiency (SPIRE),

SPIRE Roadmap.

[331] TexTrace (2016), RFID Brand Label is Key to Transparency and Sustainability.

[332] Thakkar, H. and Majumder, P. (2014), An overview of decadal progress in Big Data.

[333] The Courier Mail (2016), “Drones being used by Queensland Gas Company to check

wells and pipelines in a worldfirst trial”, available at:

https://www.couriermail.com.au/news/queensland/drones-being-used-by-queensland-

gas-company-to-check-wells-and-pipelines-in-a-worldfirst-trial/news-

story/5cffa1ca9a06e02f833d23e00be3b45f (accessed 2 July 2018).

[334] The Royal Society (2004), Nanoscience and nanotechnologies: Opportunities and

uncertainties.

[335] The Royal Society (2017), Machine learning: The power and promise of computers

that learn by example.

[336] Thomas, A. (2016), “The Benefits Modern Society Gets from GPS Tracking

Technology”, available at: https://trackimo.com/benefits-gps-tracking-technology/


[337] Tibbits, S., McKnelly, C., Olguin, C., Dikovsky, D. and Hirsch, S. (2014), 4D Printing

and Universal Transformation.

[338] Tofail, S.A.M., Koumoulos, E.P., Bandyopadhyay, A., Bose, S., O’Donoghue, L. and

Charitidis, C. (2018), “Additive manufacturing: scientific and technological challenges,

market uptake and opportunities”, Materials Today, Vol. 21 No. 1, pp. 22–37.

[339] Toyota (2018), “Prius Plug-in Hybrid”, available at:

https://www.toyota.de/automobile/prius-plugin/index.json (accessed 2 July 2018).

[340] Track your Truck (2018), “"Passive" GPS Tracking Devices”, available at:

https://www.trackyourtruck.com/gps-tracking-devices/passive-tracking/ (accessed 2 July

2018).

[341] Trackimo (2018), “Best GPS Tracker in the World”, available at: https://trackimo.eu/en/


[342] Tractica (2016), “Artificial Intelligence Revenue to Reach $36.8 Billion Worldwide by

2025”, available at: https://www.tractica.com/newsroom/press-releases/artificial-

intelligence-revenue-to-reach-36-8-billion-worldwide-by-2025/ (accessed 31 October

2017).

[343] Tractica (2017), Smart Clothing and Body Sensors: Connected Sports and Fitness

Apparel, Smart Footwear, Fashion Apparel, Baby and Pregnancy Monitors, Heart Rate

Monitors, Movement Sensors, and Wearable Patches: Market Analysis and Forecasts.


114

[344] Tsai, W., Bai, X. and Huang, Y. (2014), “Software-as-a-service (SaaS): perspectives

and challenges”, Science China Information Sciences, Vol. 57 No. 5, pp. 1–15.

[345] UNIDO - United Nations Industrial Development Organization (2016), Emerging trends

in global advanced manufacturing: challenges, opportunities and policy responses.

UNIDO - United Nations Industrial Development Organization (ed.).

[346] Union of Concerned Scientists (2015), “How Energy Storage Works”, available at:

https://www.ucsusa.org/clean-energy/how-energy-storage-works#.WwbLt-6FOM8


[347] United Nations (2015), “Sustainable Development Goals”, available at:

https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (accessed

28 June 2018).

[348] Van Rijmenam, M. (2016), “The Future of Big Data? Three Use Cases of Prescriptive

Analytics”, available at: https://datafloq.com/read/future-big-data-use-cases-prescriptive-

analytics/668 (accessed 29 June 2018).

[349] VDMA Future Business (2016), Trendradar für den Maschinen- und Anlagenbau.

[350] Verticals on Demand, Getting SaaSy In Pharma: How SaaS CRM Technology Offers a

Lifeline to Today’s Beleaguered Pharmaceutical Companies.

[351] Vinoy, K.J., Ananthasuresh, G.K., Pratap, R. and Krupanidhi, S.B. (2014), Micro and

Smart Devices and Systems, Springer India, New Delhi.

[352] Volkswagen AG (2018), “Optimal alternative. Emerging from the niche: Compressed

Natural Gas (CNG) is increasingly touted as a forward-looking drive technology for the

future – and for good reason. An overview”, available at:

https://www.volkswagenag.com/en/news/stories/2018/02/optimal-alternative.html


[353] Volvo Trucks (2018), “The New Gas-Powered Volvo FH LNG. High Performance, Low

Emissions”, available at: https://www.volvotrucks.com/en-en/trucks/volvo-fh-series/volvo-

fh-lng.html (accessed 2 July 2018).

[354] Von Alt, C. (2003), Autonomous Underwater Vehicles: Prepared for the Autonomous

Underwater Lagrangian Platforms and Sensors Workshop.

[355] Vosloo, S. (2018), “Alexa, When Will Natural Language Processing Revolutionize

ICT4D? Your Weekend Long Reads”, available at: https://www.ictworks.org/natural-

language-processing-revolutionize-ict4d/#.WuHUzG4vz3g (accessed 29 June 2018).

[356] Waller, M.A. and Fawcett, S.E. (2013), “Data Science, Predictive Analytics, and Big

Data: A Revolution That Will Transform Supply Chain Design and Management”, Journal

of Business Logistics, Vol. 34 No. 2, pp. 77–84.

[357] Wang, G., Gunasekaran, A., Ngai, E.W.T. and Papadopoulos, T. (2016), “Big data

analytics in logistics and supply chain management: Certain investigations for research

and applications”, International Journal of Production Economics, Vol. 176, pp. 98–110.


115

[358] Wang, J., Ma, Y., Zhang, L., Gao, R.X. and Wu, D. (2018), “Deep learning for smart

manufacturing: Methods and applications”, Journal of Manufacturing Systems.

[359] Wang, L., Gao, R. and Ragai, I. (2014), “An Integrated Cyber-Physical System for

Cloud Manufacturing”, in Ragai, I. (Ed.), Proceedings of the ASME 9th International

Manufacturing Science and Engineering Conference - 2014: Presented at ASME 2014

9th International Manufacturing Science and Engineering Conference, June 9 - 13, 2014,

Detroit, Michigan, USA; [part of the Joint International Manufacturing Conference

(MSEC-NAMRC-ICMP)], ASME, New York, NY, V001T04A029; 6 pages.

[360] Wang, L. and Yuan, F.G. (2008), “Vibration energy harvesting by magnetostrictive

material”, Smart Materials and Structures, Vol. 17 No. 4, p. 45009.

[361] Weber, A. (2010), “Smart Material Challenges”, available at:

https://www.assemblymag.com/articles/87817-smart-material-challenges (accessed 3

July 2018).

[362] Weber, A. (2010), “Smart Material Challenges”, available at:

https://www.assemblymag.com/articles/87817-smart-material-challenges (accessed 2

July 2018).

[363] Williams, A. (2017), “Materials handling. Machines on the move”, available at:

https://automotivelogistics.media/intelligence/machines-on-the-move (accessed 28 June

2018).

[364] Williams-Grut, O. (2017), “The electricity used to mine bitcoin this year is bigger than

the annual usage of 159 countries”, available at: http://uk.businessinsider.com/bitcoin-

mining-electricity-usage-2017-11?IR=T (accessed 19 July 2018).

[365] Wisskirchen, G., Biacabe, B., Bormann, U., Muntz, A., Niehaus, G., Soler, G. and von

Brauchitsch, B. (2017), Artificial Intelligence and Robotics and Their Impact on the

Workplace. International Bar Association Global Employment Institute (ed.).

[366] Wolfram, P. and Lutsey, N. (2016), Electric vehicles: Literature review of technology

costs and carbon emissions.

[367] Wollenhaupt, G. (2017), “Warehouse Operations Go Hands-Free With Wearable

Tech”, available at: https://insights.samsung.com/2017/06/23/warehouse-operations-go-

hands-free-with-wearable-tech/ (accessed 29 June 2018).

[368] World economic forum (2013), Outlook on the Logistics & Supply Chain Industry 2013.

World economic forum (ed.).

[369] World economic forum (2017), Technology and Innovation for the Future of

Production: Accelerating Value Creation. World economic forum (ed.).

[370] World economic forum (2018), “8 ways AI can help save the planet”, available at:

https://www.weforum.org/agenda/2018/01/8-ways-ai-can-help-save-the-planet/


[371] World economic forum and Accenture (2016), Digital Transformation of Industries

Logistics Industry. World economic forum (ed.).


116

[372] Wright, A. and De Filippi, P. (2015), Decentralized Blockchain Technology and the

Rise of Lex Cryptographia.

[373] Wuest, T., Weimer, D., Irgens, C. and Thoben, K.-D. (2016), “Machine learning in

manufacturing: advantages, challenges, and applications”, Production & Manufacturing

Research, Vol. 4 No. 1, pp. 23–45.

[374] Würth (2018), “IBIN®. The first intelligent bin”, available at: https://www.wuerth-

industrie.com/web/en/wuerthindustrie/cteile_management/kanban/ibin_intelligenterbehae

lter/ibin.php (accessed 28 June 2018).

[375] Yang, L. (2015), Nanotechnology-Enhanced Orthopedic Materials, Elsevier.

[376] Yeomans, G. (2014), Autonomous Vehicles: Handing over Control: Opportunities and

Risks for Insurance. Lloyd's Exposure Management (ed.).

[377] Yli-Huumo, J., Ko, D., Choi, S., Park, S. and Smolander, K. (2016), “Where Is Current

Research on Blockchain Technology? A Systematic Review”, PloS one, Vol. 11 No. 10,

e0163477.

[378] Yus, V.A. (2018), “Gas vs electricidad. ¿Cuál es mejor para el transporte de

mercancías?”, available at: https://www.highmotor.com/gas-vs-electricidad-cual-mejor-

transporte-mercancias.html (accessed 2 July 2018).

[379] Zhao, H. (2018), “Bitcoin and blockchain consume an exorbitant amount of energy.

These engineers are trying to change that”, available at:

https://www.cnbc.com/2018/02/23/bitcoin-blockchain-consumes-a-lot-of-energy-

engineers-changing-that.html (accessed 29 June 2018).

[380] Zhao, M., Kumar, A., Ristaniemi, T. and Chong, P.H.J. (2017), “Machine-to-Machine

Communication and Research Challenges: A Survey”, Wireless Personal

Communications, Vol. 97 No. 3, pp. 3569–3585.

[381] Zheng, Z., S. Xie, H.-N. Dai, and H. Wang (2017), “Blockchain Challenges and

Opportunities: A Survey”, International Journal of Electric and Hybrid Vehicles, No. 1-23.

[382] Zhu, X., Mukhopadhyay, S.K. and Kurata, H. (2012), “A review of RFID technology and

its managerial applications in different industries”, Journal of Engineering and

Technology Management, Vol. 29 No. 1, pp. 152–167.


117

Annex A: List of Acronyms

AGV Automated Guided Vehicles

AI Artificial Intelligence

AM Additive Manufacturing

AMR Autonomous Mobile Robot

AR Augmented Reality

ARAM Augmented Reality Aided Manufacturing

B2B Business to Business

BESS Battery Energy Storage Systems

BEV Battery Electric Vehicles

BPaaS Business Process as a Service

CAD Computer-Aided-Design

CNGV Compressed Natural Gas Vehicle

CO2 Carbon Dioxide

Cobot Collaborative Robot

CPS Cyber-Physical System

CRM Customer Relationship Management

FCEV Fuel Cell Electric Vehicle

FES Flywheel Energy Storage

GDP Gross Domestic Product

GLONASS Global Navigation Satellite System

GPS Global Positioning System

IaaS Infrastructure as a Service

ICT Information and communications technology

IoT Internet of Things

IT Information Technology

ITS Intelligent Transportation System

LNG Liquefied Natural Gas

LTE Long Term Evolution

M2M Machine to Machine

NB-IoT NarrowBand-IoT

NLP Natural Language Processing

OLED Organic Light Emitting Diode

PaaS Platform as a Service

PHEV Plug-in Hybrid Electric Vehicle


118

RFID Radio Frequency Identification

RTLS Real-Time Locating System

SaaS Software as a Service

SaLsA Safe Autonomous Logistics and Transport Vehicle

SC Supply Chain

SQL Structured Query Language

tps Transactions per second

TRL Technology Readiness Level

UAV Unmanned Aerial Vehicle

UWB Ultra-WideBand

VNS Vehicular Networking System

VR Virutal Reality

WLAN Wireless Local Area Network


119

Annex B: Running EU Projects of Project Partners

Technologies

EU Projects Au

ton

om

ou

s Tr

ansp

ort

Syst

ems

Ro

bo

ts

Clo

ud

Bas

ed C

om

pu

ter

Syst

ems

Inte

rnet

of

Thin

gs

Dis

trib

ute

d L

edge

r /

Blo

ckch

ain

Art

ific

ial I

ntel

ligen

ce

Dat

a Sc

ien

ce

Mo

bile

an

d W

eara

ble

Dev

ices

Co

mm

un

icat

ion

Inf

rast

ruct

ure

Iden

tifi

cati

on

Tec

hn

olo

gies

Loca

tio

n T

ech

no

logi

es

Vis

ual

Co

mp

uti

ng

Ad

dit

ive

Man

ufa

ctu

rin

g

Ener

gy I

nfra

stru

ctu

re

Alt

ern

ativ

e P

rop

uls

ion

Sys

tem

s

Ener

gy T

ech

no

logi

es

Smar

t M

ater

ials

Nan

ote

chn

olo

gy

DISRUPT x x x xBIFOCALPS x x x x x x x x x x x

Symbioptima

Movaid x x x x xPICKPLACE x

COVR xDaedalus x xINSPIRE x x x x x xMAYA x x

FiberEUse x xTransformingTransport x x

Clusters 2.0 x x x x x x xLEARN x x x xSENSE x x x x

AEROFLEX x x x x x xConnectedFactories x x x x x x x x x x x

L4MS x x xMIDIH x x x

AMable x x x xBEinCPPS x x

ScalABLE 4.0 x x x xBEACONING x x x x x x x x

Fasten x x x x x xColRobot x x x

MANU_SQUARE x x x xENSUREAL X X X XHARMONI

MONSOON X X X XINSPIREWater X X X

EPOS X X X X X X X


120

Technologies

EU Projects Aut

onom

ous

Tran

spor

t

Syst

ems

Rob

ots

Clo

ud B

ased

Com

pute

r

Syst

ems

Inte

rnet

of

Thin

gs

Dis

trib

ute

d L

edge

r /

Blo

ckch

ain

Art

ific

ial I

ntel

ligen

ce

Dat

a Sc

ienc

e

Mob

ile a

nd W

eara

ble

Dev

ices

Com

mun

icat

ion

Infr

astr

uctu

re

Iden

tifi

cati

on T

echn

olog

ies

Loca

tion

Tec

hnol

ogie

s

Vis

ual C

ompu

ting

Add

itiv

e M

anuf

actu

ring

Ener

gy In

fras

truc

ture

Alt

erna

tive

Pro

puls

ion

Syst

ems

Ener

gy T

echn

olog

ies

Smar

t M

ater

ials

Nan

otec

hnol

ogy

MEASURE X X X XCarE-Service x

BigDataGrapes xHYLIFT-EUROPE x

NEXTRUST xLoCoMaTech x

TARGET x x x xBigMedilytics xWEAR Sustain xProductive4.0 x x x x x x

SerIoT xSEMIoTICS x x

LEARN x xSELIS x x x x x

CLUSTERS 2.0 x x xSENSE x X x x x x

MOBILITY4EU x x


121

Acronim extended name call Type website sector of application Main Supply Chain topic

DISRUPT

Decentralised architectures for optimised

operations via virtualised processes and

manufacturing ecosystem collaboration

H2020-FOF-11-2016 RIA http://www.disrupt-project.eu automotive, white goods

Innovative Cloud-based Platform to support manufacturing

processes. In particular, development of new tools for

inbound logistics with disruptive events

BIFOCALPS

Boosting Innovation in Factory of the Future

Value Chain in the Alps Interreg (Alpine Space)regional

policy

http://www.alpine-

space.eu/projects/bifocalps/en/projec

t-

results/communication/communicatio

n-material

manufacturing as a wholeDefinition of policies to support application of technologies

for industrial collaboration

Symbioptima

Human-mimetic approach to the integrated

monitoring, management and optimization

of a symbiotic cluster of smart production

units

H2020-SPIRE-2015 RIA http://www.symbioptima.eu process industry Advanced optimisation tools for industrial symbiosis

Movaid H2020-FOF-06-2015 RIA http://www.movaid.eu/about/ Orthopaedic sectorNew methods for supply chain operations for customized

orthesis

PICKPLACEFlexible, safe and dependable robotic part

handling in industrial environmentsH2020-ICT-2017-1 IA

manufacturing and

logisticsInternal logistics

COVRBeing safe around collaborative and versatile

robots in shared spacesH2020-ICT-2017-1 IA http://safearoundrobots.com/ machine tools

Daedalus

Distributed control and simulAtion platform

to support an Ecosystem of DigitAL

aUtomation developerS

H2020-FOF-2016 RIAhttp://www.daedalus.iec61499.eu/ind

ex.php/en/machine tools

creation of a Digital Ecosystem that could go beyond the

current limits of manufacturing control systems and

propose an ever-growing market of innovative solutions for

the design, engineering, production and maintenance of

plants’ automation.

INSPIRETowards growth for business by flexible

processing in customer-driven value chainsH2020-SPIRE-2016 CSA http://www.inspire-eu-project.eu/

manufacturing and

process

developing new innovative business models for more

flexible and sustainable manufacturing value chains

providing at the same time a guideline to measure the

performance of this novel models under different scenarios

MAYA

Multi-disciplinary integrated simulAtion and

forecasting tools, empowered by digital

continuity and continuous real-world

synchronization, towards reduced time to

production and optimization

H2020-FoF-2015 RIA http://www.maya-euproject.com/ automotive

strategically support production-related activities during all

the phases of the factory life-cycle, from the integrated

design of the product - process - production system,

through the optimization of the running factory, till the

dismissal/reconfiguration phase

FiberEUse

Large scale demonstration of new circular

economy value-chains based on the reuse of

end-of-life fiber reinforced composites.

H2020-CIRC-2016TwoStage IA http://fibereuse.eu/ re-manufacturing supply chain for the circular economy

TransformingTransport Big Data Value in Mobility and Logistics ICT-15-2016 IA https://transformingtransport.eu/ Transport and logisticsBig Data PPP: Large Scale Pilot actions in sectors best

benefitting from data-driven innovation

Clusters 2.0 Clusters 2.0 MG-5.1-2016 RIA http://www.clusters20.eu/ Transport and logisticsNetworked and efficient Logistics Clusters, Eingereicht (1st

stage) über ETP ALICE

LEARNLogistics Emission Accounting and Reduction

NetworkMG-5.3-2016 CSA http://www.learnproject.net/ Transport and logistics

Promoting the deployment of green transport, towards Eco-

labels for logistics

SENSEAccelerating the Path Towards Physical

InternetMG-5.4-2017 CSA http://www.etp-logistics.eu/ Potential of the Physical Internet

AEROFLEXAerodynamic and Flexible Trucks for Next

Generation of Long Distance Road TransportGV-09-2017 IA https://aeroflex-project.eu/ Transport and logistics Aerodynamic and flexible trucks


122


ConnectedFactories Industrial Scenarios for Connected Factories FOF-11b-2016 CSAhttps://www.effra.eu/connectedfactor

iesManufacturing Industry Digital automation

L4MS Logistics for Manufacturing SMEs FOF-12a-2017 IA http://www.l4ms.eu/ Manufacturing Industry ICT Innovation for Manufacturing SMEs (I4MS)

MIDIHManufacturing Industry Digital Innovation

HubsFOF-12a-2017 IA http://www.midih.eu/ Manufacturing Industry

ICT Innovation for Manufacturing SMEs (I4MS) ; IDS

zusammen mit Politecnio di Milano und Innovalia

(=Koordinator von I4MS)

AMable AdditiveManufacturABLE FOF-12a-2017 IA https://www.amable.eu/ Manufacturing Industry ICT Innovation for Manufacturing SMEs (I4MS)

BEinCPPSBusiness Experiments in Cyber Physical

Production SystemsH2020-FoF-2015 IA http://www.beincpps.eu/

SMEs, manufacturing ICT Innovation for Manufacturing SMEs (I4MS)

ScalABLE 4.0Scalable automation for flexible production

systemsH2020-FOF-2016 RIA https://www.scalable40.eu/ Manufacturing

Develop an Open Scalable Production System, that

integrates factories from shop floor to high

management in real time.

BEACONING

Breaking Educational Barriers with

Contextualised, Pervasive and Gameful

Learning (BEACONING)

H2020 ICT-20-2015 IA http://beaconing.eu/Manufacturing, Process

and Logistics

Aims to support "anytime anywhere" learning by exploiting

pervasive context-aware and gamified techniques and

technologies. Future Internet Technology, mobile,

gamification, pervasive gaming, procedural content

generation, game authoring, human – computer interfaces,

learning analytics and problem based - learning.

FastenFlexible and Autonomous Manufacturing

Systems for Custom-Designed ProductsH2020-EUB-2017 RIA https://cordis.europa.eu/project/rcn/212223_en.html Manufacturing, Process

CPS, Vital processes included in the product and production

systems lifecycle. IoT, Additive Manufacturing and

Robotics, with Mass Customization, Product-Service

Systems and Sustainable Manufacturing.

ColRobotCollaborative Robotics for Assembly and

Kitting in Smart ManufacturingH2020-ICT-2015 IA https://www.colrobot.eu/ Manufacturing

Collaborative Robots, Cyber-security systems (based on

human-robot interactions)

MANU_SQUAREMANUfacturing ecoSystem of QUAlified

Resources ExchangeH2020-NMBP-2017-two-stage RIA

https://cordis.europa.eu/project/rcn/2

12854_en.htmlManufacturing

Creation of a European platform-enabled responsible

ecosystem acting as virtual marketplace. Establishes

dynamic value networks which can be arranged on-demand

to couple the needs of buyers and the availability of sellers

of manufacturing capacity.

ENSUREAL

Integrated cross-sectorial approach for

environmentally sustainable and resource-

efficient alumina production

SPIRE-07-2017 IA https://www.ensureal.com/Aluminium production,

process industry

ENSUREAL project’s main objective is to decrease

dependence on alumina and characterise all the streams of

the alumina industry in order to valorise them and make

the European aluminium industry more competitive at a

global scale. In order to do so, ENSUREAL proposes the

introduction of a new technology (Pedersen process) that

improves the process’ yield and its energy and

environmental performance. Moreover, ENSUREAL’s

consortium proposes a new value chain that takes into

account all the streams as valorisable products across the

aluminium supply chain and introduces the foundry and the

agricultural sector.


123


HARMONI

Harmonised assessment of regulatory

bottlenecks and standardisation needs for

the process industry

SPIRE-12-2017 CSA https://www.spire2030.eu/harmoni Process industry

HARMONI aims at bringing together all the relevant

stakeholders of the process industry to jointly identify,

analyse and propose solutions to the regulatory

bottlenecks and standardization needs that hamper their

innovation processes and the market uptake of their

results, necessary to move towards a more sustainable and

competitive European process industry. In order to achieve

HARMONI’s overarching goal, the consortium will develop

and apply a methodology for ensuring an effective

collaboration of the 8 sectors involved in SPIRE PPP to

elaborate the solutions to the common challenges they

face due to non-technological barriers, such as regulatory

issues or the lack of European Standards when trying to

improve their resource efficiency. In addition, HARMONI

will analyse, compare and propose recommendations to

trigger the transferability of technical solutions among and

beyond the SPIRE sectors

MONSOON

MOdel based coNtrol framework for Site-

wide OptmizatiON of data-intensive

processes

SPIRE-02-2016 RIA https://www.spire2030.eu/monsoon

Process industry, Raw

material, energy,

aluminium and plastic

domain

The MONSOON vision is to provide Process Industries with

dependable tools to help achieving improvements in the

efficient use and re-use of raw resources and energy.

MONSOON aims at establishing a data-driven methodology

supporting the exploitation of optimization potentials by

applying multi-scale model based predictive controls in

production processes. MONSOON features harmonized site-

wide dynamic models and builds upon the concept of the

cross-sectorial data lab, a collaborative environment where

high amounts of data from multiple sites are collected and

processed in a scalable way. The data lab enables

multidisciplinary collaboration of experts allowing teams to

jointly model, develop and evaluate distributed controls in

rapid and cost-effective way. Hybrid simulation and

seamless integration techniques are adopted for rapid

prototyping and deployment in real conditions. MONSOON

will be developed and evaluated in two sites from the

aluminium and plastics domains.

INSPIREWater

Innovative Solutions in the Process Industry

for next generation Resource Efficient Water

management

SPIRE-01-2016 IAhttps://www.spire2030.eu/inspirewat

er

Water management,

Process industry,

INSPIREWATER demonstrates a holistic approach for water

management in the process industry using innovative

technology solutions from European companies to increase

water and resource efficiency in the process industry.

INSPIREWATER addresses non-technical barriers as well as

technical, as innovation needs both components and

demonstrates them in the steel and chemical industry. A

flexible system for water management in industries that

can be integrated to existing systems is worked out and

demonstrated to facilitate implementation of technical

innovations.


124


EPOS

Enhanced energy and resource Efficiency and

Performance in process industry Operations

via onsite and cross-sectorial Symbiosis

SPIRE-06-2015 RIA https://www.spire2030.eu/epos

Process industry, steel,

cement, chemicals, bio-

based

Out of the community created by SPIRE covering industrial

and research actors throughout Europe, the EPOS project

brings together 6 global process industries from 6 key

relevant sectors: steel, cement, minerals, chemicals, bio-

based/life science products and engineering

With the aim of reinforcing competitiveness of the EU

industry, it is the ambition of the EPOS partners to gain

cross-sectorial knowledge and investigate cluster

opportunities using an innovative Industrial Symbiosis (IS)

platform to be developed and validated during the project.

The main objective is to enable cross-sectorial IS and

provide a wide range of technological and organisational

options for making business and operations more efficient,

more cost-effective, more competitive and more

sustainable across process sectors.

MEASUREMetrics for Sustainability Assessment in

European Process IndustriesSPIRE-04-2014 CSA https://www.spire2030.eu/measure

Process industrychemistry,

consumer goods, steel,

automotive, waste

The European project MEASURE accepted the challenge to

provide a roadmap highlighting life cycle based evaluation

approaches, which support a sustainable supply chain

management including the cooperation between

manufacturers and cross-sectorial co-product, recycling and

reuse options in practical use. The project brings together

leading European process industries in chemistry,

consumer goods, steel, automotive & waste with academic

experts on sustainability assessment, regulators and

standardisation bodies working on this ambitious goal.

CarE-Service

Circular Economy Business Models for

innovative hybrid and electric mobility

through advanced reuse and

remanufacturing technologies and services H2020-CIRC-2017TwoStage IA

https://cordis.europa.eu/project/rcn/2

16087_en.html hybrid and electric industry

new enabling technologies and service to systematically

perform innovative reuse and remanufacturing as key-

processes to provide value to customers and, at the same

time, to minimize environmental impacts.

BigDataGrapesBig Data to Enable Global Disruption of the

Grapevine-powered IndustriesH2020-ICT-2017-1 RIA http://www.bigdatagrapes.eu/

wine and natural cosmetics

industries

help companies across the grapevine-powered value chain

ride the big data wave, supporting business decisions with

real time and cross-stream analysis of very large, diverse

and multimodal data sources.

HYLIFT-EUROPE

HyLIFT-EUROPE - Large scale demonstration

of fuel cell powered material handling

vehicles

FCH-JU-2011-1 JTI-CP-FCH - http://www.hylift-europe.eu/

vehicle manufacturers,

infrastructure operators

and SME companies to

provide fully working

hydrogen powered fuel

cell material handling

solutions

demonstrate fuel cell systems in material handling vehicles

from the partners STILL and MULAG and potentially from

non-participating OEMs. STILL will purchase fuel cell

systems from suppliers according to the FCH JU purchasing

rules (“principles of economy, efficiency and

effectiveness”). MULAG will integrate fuel cell systems

from the partner DTP.

NEXTRUST

Building sustainable logistics through trusted

collaborative networks across the entire

supply chain

H2020-MG-2014_TwoStages RIA http://nextrust-project.eu/ logistics industry

develop C-ITS cloud based smart visibility software to

support the re-engineering of the networks, improving real-

time utilization of transport assets.


125


LoCoMaTechLow Cost Materials Processing Technologies

for Mass Production of Lightweight VehiclesH2020-NMBP-GV-2016 RIA

http://www.locomatech.net/ProjectAr

ea1/home

automotive and

alluminium industry

a world first low-cost HFQ® aluminium production line

(prototype), targeting reduction of energy consumption per

vehicle by 15.3-22%,

TARGET Making Regional Manufacturing Globally

Competitive and Innovative

NPA 2014-2020 is part of the

European Territorial

Cooperation Objective,

supported by the European

Regional Development Fund

(ERDF) and ERDF equivalent

funding from non EU partner

countries

Innovation http://www.targetproject.eu/about-

target/

versatile application fields

from manufacturing to

interior design, health

care, tourism, exhibition,

and cultural heritage

applications.

develop tools to enhance capacity of manufacturing

companies in the NPA region to adapt and embrace new

technologies and innovation. The toolbox developed will

consist of subsets such as digital manufacturing (robotics

and simulation). New ideas and thinking (human centred

and environmental thinking, competitiveness), business

models, and, modern Product innovation.

BigMedilytics Big Data for Medical Analytics H2020-ICT-2017-1 IA https://www.bigmedilytics.eu/ healthcare sector

BigMedilytics (Big Data for Medical Analytics) is the largest

EU-funded initiative to transform the region’s healthcare

sector by using state-of-the-art big data technologies to

achieve breakthrough productivity in the sector by reducing

cost, improving patient outcomes and delivering better

access to healthcare facilities simultaneously.

WEAR SustainWearable technologists Engage with Artists

for Responsible innovationH2020-ICT-2016-1 IA

https://wearsustain.eu/consortium-

partners/wearable industry

Lead the emergence of innovative approaches to design,

production, manufacturing and business models for

wearable technologies

Productive4.0

Electronics and ICT as enabler for digital

industry and optimized supply chain

management covering the entire product

lifecycle

H2020-ECSEL-2016-2-IA-two-

stage

ECSEL-IA -

ECSEL

Innovation

Action

https://productive40.eu/

OEMs and suppliers from

different industrial

domains, e.g.

mechanical/electrical

engineering, machinery,

semiconductor industry,

manufacturing, chemical

industry and

finance/banking

digitalized production applicable to all kinds of products.

The results such as IoT components modeling and

simulation methods as well as tool chains for cross-lifecycle

and cross-domain digitalization are suitable means for

linking all stages of a product lifecycle in a sustainable way.

SerIoT Secure and Safe Internet of Things H2020-IOT-2017 RIA https://seriot-project.eu/different industrial

domains

SerIoT technology will be installed, deployed and validated

in emerging IoT-enabled application areas (i.e. Smart

Transportation, Surveillance & Flexible

Manufacturing/Industrie 4.0 as core business areas and &

Food & Supply chain) through-out its lifetime, enabling the

conduction of pioneer R&D for the delivery of horizontal

IoT end-to-end security platform in Europe.

SEMIoTICSSmart End-to-end Massive IoT

Interoperability, Connectivity and SecurityH2020-IOT-2017 RIA https://www.semiotics-project.eu/

three diverse usage

scenarios in the areas of

renewable energy,

healthcare, and smart

sensing

develop a pattern-driven framework, built upon existing

IoT platforms, to enable and guarantee secure and

dependable actuation and semi-autonomic behaviour in

IoT/IIoT applications.

LEARNLogistics Emission Accounting and Reduction

Network

H2020-MG-2016-SingleStage-

RTD-MOVECSA http://www.learnproject.net Transport and logistics

The overall goal of LEARN is to establish co-ordinated

networks of industry, government and civil society

Stakeholders and build on existing initiatives to drive

consistent and transparent emissions measurement and

reporting across the global logistics supply chain.


126


SELISTowards a Shared European Logistics

Intelligent Information SpaceH2020-MG-2015_TwoStages RIA www.selisproject.eu Transport and logistics

SELIS is aimed at delivering a ‘platform for pan-European

logistics applications

CLUSTERS 2.0Open network of hyper connected logistics

clusters towards Physical Internet H2020-MG-2016-Two-Stages RIA http://www.clusters20.eu/ Transport and logistics

The project’s vision is to leverage the full potential of

European logistics clusters for an efficient and fully

integrated transport system in Europe making optimal use

of an open network of logistics clusters and hubs starting

with Zaragoza (PLAZA), Duisburg (Duisport), Lille (Dourges),

Bologna-Trieste (Interporto/port of Trieste), Brussels

(BruCargo), London (Heathrow), Pireaus (PCT), Trelleborg

(Port), while keeping neutral the environmental and local

impacts such as congestion, noise, land use and local

pollution levels.

SENSEAccelerating the Path Towards Physical

Internet

H2020-MG-2017-SingleStage-

RTD-MOVECSA Transport and logistics

accelerate the path towards the Physical Internet (PI), so

advanced pilot implementations of the PI concept are well

functioning and extended in industry practice by 2030, and

hence contributing to at least 30 % reduction in congestion,

emissions and energy consumption.

MOBILITY4EUAction Plan for the Future of Mobility in

Europe

H2020-MG-2015_SingleStage-

ACSA www.mobility4eu.eu Transport and logistics

An action plan for the coherent implementation of

innovative transport and mobility solutions in Europe is

thus urgently needed and should be sustained by a wide

range of societal stakeholders. The MOBILITY4EU project

will develop such a plan taking into account all modes of

transport as well as a multitude of societal drivers

encompassing health, environment and climate protection,

public safety and security, demographic change,

urbanisation and globalisation, economic development,

digitalisation and smart system integration.

RIA = Research and Innovation Action

IA = Innovation Action

CSA = Collaborative and support action

Documents

D3.1: Technology Mapping and Scouting€¦ · D3.1: Report on technology mapping and scouting 11 1 Introduction The aim of work package 3 (WP3) is to develop a strategic research