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Interreg Ref: 4177
WP3
SOTA
State of the Art Report
27/04/2015
M. Messaadia2, R. D. Evans1, S. Mahdikhah2, S. Wan1,
D. Baudry2, J. X. Gao1, O. Owodunni1, S. Shah1, I. Essop1, A. Louis2, S. Keates1 and I. Cakebread1
1 University of Greenwich, Chatham, United Kingdom
2 CESI, Rouen, France
TABLE OF CONTENTS CHAP I. INTRODUCTION .................................................................................................................. 5
1 INTRODUCTION ........................................................................................................................... 5
1.1 CHALLENGES FACING MANUFACTURING .............................................................................. 5
1.2 PRODUCT LIFECYCLE MANAGEMENT ..................................................................................... 7
1.3 KNOWLEDGE MANAGEMENT .................................................................................................. 8
CHAP II. LITERATURE REVIEW ........................................................................................................... 9
2 PRODUCT LIFECYCLE MANAGEMENT .............................................................................................. 9
2.1 BACKGROUND ........................................................................................................................... 9
2.2 WHAT IS PRODUCT LIFECYCLE MANAGEMENT? ............................................................... 10
2.3 THE HISTORY OF PLM ........................................................................................................... 12
2.4 THE IMPORTANCE OF PLM ................................................................................................... 13
3 PLM DEFINITIONS ....................................................................................................................... 15
4 THE PROPOSED PLM AXES ............................................................................................................ 16
4.1 STRATEGY ................................................................................................................................ 17
4.2 ORGANIZATION ..................................................................................................................... 18
4.2.1 OEM/SUPPLIERS INTEROPERABILITY ............................................................................ 19
4.3 PROCESS ................................................................................................................................... 20
4.4 TOOLS ...................................................................................................................................... 21
CHAP III. MODELING FRAMEWORK .............................................................................................. 22
5 MODELING FRAMEWORK ....................................................................................................... 22
5.1 PROCESS PLANNING AND APPLICATION INTEGRATION ................................................... 22
5.2 INFORMATION MODELING AND APPLICATION INTEGRATION ......................................... 24
6 RECENT INDUSTRIAL STATUS OF PLM SOLUTIONS ..................................................... 25
6.1 NEEDS ANALYSIS OF PLM SYSTEM IN SMES......................................................................... 25
6.1.1 KNOWLEDGE CAPITALIZATION................................................................................... 26
6.1.2 REFERENCE MANAGEMENT ........................................................................................... 26
6.1.3 QUOTE ............................................................................................................................ 26
6.1.4 ARCHIVE MANAGEMENT ............................................................................................... 26
6.1.5 INTEROPERABILITY ......................................................................................................... 26
6.1.6 NEEDS OF DESIGN ......................................................................................................... 26
6.1.7 ASSEMBLY PLANNING AND PRODUCT DEVELOPMENT ............................................. 27
7 A STUDY OF THE INFORMATION MODELING FRAMEWORK ................................... 27
7.1 COMPARISON OF DATA MODELS ......................................................................................... 27
7.2 DEVELOPED MODEL OF PPRO .............................................................................................. 31
CHAP IV. KNOWLEDGE MANAGEMENT ............................................................................................... 34
8 WHAT IS KNOWLEDGE? ................................................................................................................. 34
8.1 TYPES OF KNOWLEDGE ......................................................................................................... 36
9 THE MANAGEMENT OF KNOWLEDGE .......................................................................................... 37
9.1 THE IMPORTANCE OF MANAGING KNOWLEDGE ............................................................. 37
9.2 THE PROCESS OF MANAGING KNOWLEDGE – COLLECTION AND SHARING ............... 38
9.3 BARRIERS TO KNOWLEDGE MANAGEMENT ....................................................................... 39
10 KNOWLEDGE MANAGEMENT IN SUPPLY CHAINS .................................................................. 41
11 SUPPLY CHAIN MANAGEMENT .................................................................................................. 43
11.1 BACKGROUND ........................................................................................................................ 43
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11.2 THE IMPORTANCE OF SCM ................................................................................................... 44
CHAP V. MAINTENANCE ....................................................................................................................... 47
12 INTRODUCTION .......................................................................................................................... 47
12.1 THE REQUIREMENT FOR MAINTENANCE PROCESS PLANNING ....................................... 47
13 MAINTENANCE PROCESS MANAGEMENT ................................................................................ 49
14 MAINTENANCE KNOWLEDGE REPRESENTATION .................................................................... 50
15 MAINTENANCE PROCESS MODELLING ..................................................................................... 51
15.1 IDEFØ ...................................................................................................................................... 51
15.2 UML AND SYSML ................................................................................................................... 53
15.3 PRODUCT LIFECYCLE MANAGEMENT .................................................................................. 54
15.3.1 MEGA SUITE .................................................................................................................... 54
15.4 BPMN ...................................................................................................................................... 55
15.5 PETRI NET ................................................................................................................................ 57
15.6 DESIGN ROADMAP ................................................................................................................. 59
CHAP VI. DISCUSSION......................................................................................................................... 62
16 CONCLUSION ......................................................................................................................... 65
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Acronyms
PPRO: model of Product Process Resources Organization
STEEP: Social, Technological, Economical, Environmental and Political factors
STEP: Standard for the Exchange of Product model data
SWOT: Evaluation of Strengths, Weaknesses, Opportunities, and Threats
SysML: Systems Modeling Language
WFMS: Workflow Management Systems
BOL: Beginning-of-Life
BoM Bill of Materials
CAD: Computer Aided Design
CAE: Computer Aided Engineering
CAM: Computer Aided Manufacturing
CR: Changes Request
CRM: Customer Relationship Management
CSM: Component Supplier Management
DSS: Decision-Support System
ECO: Engineering Change Order
ECR: Engineering Change Request
EOL: End-of-Life
ERP: Enterprise Resource Planning
HRM: Human Resource Management
ICT: Information and Communications Technology
IT: Information Technology
KM: Knowledge Management
KMS: Knowledge Management Systems
MES: Manufacturing Execution Systems
MOL: Middle-of-Life
NIST: National Institute of Standards and Technology
OEM: Original Equipment Manufacturer
PD: Product Development
PDM: Product Data Management
PEID: Product Embedded Information Devices
PLM: Product Lifecycle Management
PM: Project Management
SCM: Supply Chain Management
SME: Small to Medium Enterprises
SPM: Supply and Planning Management
WWW: World Wide Web
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SOTA State of the Art Report
CHAP I. INTRODUCTION
1 INTRODUCTION
The integration of the supplier in the product value chain is not a new challenge. Various research
and projects focused on this issue seeking more effective ways to improve the integration and
exchange between Original Equipment Manufacturer (OEM) and suppliers during development.
The WP3 BENEFITS project aims to sustain and develop the industrial fabric based on the modeling
of different business processes and interactions during innovative product development phases. The
objective of this document is to make an inventory on the challenges of knowledge management and
the process of cooperation between supplier and OEM.
The analysis of the state of the art gives a feedback related to new uses of digital engineering for the
design, and highlights the key factors for the competitiveness and innovation of enterprises, especially
SMEs. To address these issues, it is necessary for companies to upgrade their IT systems and
processes in order to take into account the management of the product life cycle (PLM).
This report reviews the literature and the work on:
The product management throughout the lifecycle (PLM);
The product modeling and collaborative process;
knowledge management; and
The benefits of extended supply chains and maintenance.
1.1 Challenges Facing Manufacturing
As predicted by Gunasekaran et al. [1] , organisations in the 21st century have to overcome
challenges presented on a number of fronts. These include:
Meeting the more complex requirements of customers who demand low-cost, but high quality
solutions to address their own specific problems or opportunities;
Managing the explosion of data which has been facilitated through the increasing use of the
internet and technological advances, such as 3D design data capabilities;
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Improving the lifecycle of products on a routine basis by focussing upon cost, quality, time and
the impact on the environment; and
Establishing effective communication channels with employees and external partners anywhere in
the world [2] .
All of these factors dictate that engineering and manufacturing companies need to develop flexible
and responsive work processes to ensure their competitiveness in this new global and integrated
enterprise environment [1] . The response from industry has seen an increasing focus on lean, green
and agile manufacturing practices, which aim to:
Eliminate waste and the use of substances harmful to the environment;
Reduce Product Development (PD) and manufacturing time and cost;
Improve quality and collaboration; and
Ideally, “get it right first time” [3] .
Furthermore, considerable attention has been paid to PLM, which has been a clear focus for
corporate investment and which has resulted in a change in value chain characteristics, as reflected in
Figure 1. Kiritsis, Bufardi and Xirouchakis [4] commented that this focus on the total management of
product lifecycles resulted from worldwide economic conditions demanding that organisations make
process changes to remain competitive.
Figure 1. Change of Value Chain Characteristics [4]
It was suggested that previous practices concentrating upon product cost, quality or time to market
were no longer sufficient to retain competitive advantage. The focus had to be redirected towards
innovation with clearly differentiated product offerings being the outcome. The total management of
the product lifecycle was deemed to be essential to foster innovation and meet customer
expectations.
Against this background, it is also recognised [5] that the development and enhancement of the
supply chain through extended enterprises is a vital issue for organisations and, furthermore, for the
successful operation of such enterprises, effective knowledge sharing is paramount. This remains a
significant challenge for manufacturing and provides a focal point for this research. Choy, Tan and
Chan [6] stated that the involvement of the extended supply chain in initial product development
processes influences outcomes to a significant degree. They suggested that the knowledge of
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suppliers should be utilised to ensure the competitive advantage of new products; this complemented
the view of Barney [7] , with regard to the value of customer input to supply chain processes [8] .
The importance of Supply Chain Management (SCM) was confirmed by Myers [9] , who stated that
“strong, cross-national supply chain relationships can create innovative environments that provide
competitive advantages for member companies. Inter-organisational learning and adaptation to
volatile environments can facilitate symbiotic relationships between partners”.
Successful PLM based upon effective supply chain management, therefore, stands as a major challenge
for manufacturing organisations and fundamental to this task is the efficient management and sharing
of organisational knowledge between stakeholders both within and beyond enterprise boundaries
[10] .
While many organisations, such as Wal-Mart [11] , Dell [12] , Toyota Motor Corporation, Boeing
and Lockheed Martin [9] have made significant improvements in knowledge management with their
suppliers, much still remains to be achieved and it is evident that the internet offers the potential to
connect small to medium sized companies more effectively with other companies within supply
chains, with all partners benefiting more from the exchange of information and knowledge [13] .
Over the last decade, the rapid development of web-based and wireless mobile telecommunication
technologies and product identification technologies, based on Product Embedded Information
Devices (PEID’s), has changed our stereotype of the product lifecycle and these technologies provide
an added dimension to allow greater visibility of product information throughout entire product
lifecycles [14] . New technologies now offer the prospect of significantly enhancing capabilities in the
areas of knowledge capture, management and sharing within supply chains in the context of PLM.
1.2 Product Lifecycle Management
Among the major changes in products development, we have the life time and time-to-market of new
products. The objective of reducing the development cycle is not always achieved, for example, when
Airbus announced delays to the A380 program in 2006. Also, during (2013) (Toyota, Nissan, Honda
and Mazda) recall more than 3.39 million cars, because of a potentially faulty passenger airbag.
Among the reasons of these failures, we find the lack of coordination in the relationship between the
OEM and suppliers. This relationship is characterized by the involvement of supplier in the life cycle
of the product until its integration into this cycle. This integration of suppliers in the value chain of
the product is not a new problem; many studies have addressed this issue and seek to find the best
way to achieve this integration. In this issue we find works that deal with aspects of interoperability
[15] , data exchange [16] and standards through the product life cycle [17] , and those who deal
with the organizational aspect between the OEM and its suppliers [18] through the development of
various forms of cooperation with different degrees of integration. The goal is to advance suppliers
to higher levels of autonomy and to win in a rank.
This increase in the relationship was driven by the expansion of the concept of collaboration and
PLM (product management throughout its life cycle) concept that is becoming increasingly important
in the strategies developed by companies [19] .
This increase of the relationship was driven by the expansion of the concept of collaboration and the
PLM concept, which is becoming increasingly important in the strategies developed by companies
[20] .
Also, the evolution of computer technologies, CADs and ICT tools, especially PLM tools helped the
evolution of cooperation between OEM and suppliers.
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Thereby, the classic work has been a great evolution, like, few years ago, in automotive sector [21] .
This evolution is characterized by the deployment of business networks between suppliers and OEM.
These networks are characterized by a "vertical cooperation"; this cooperation resulted in the
integration of equipment suppliers through simultaneous process of cars development: in the
planning / design and phases of education and achievement.
1.3 Knowledge Management
Since the 1990s and given the ever more challenging economic conditions experienced by companies
today, there has been increasing recognition that one of the most valuable resources owned by
organisations is employee knowledge. Prior to the 1990s, Porter and Millar [22] suggested that the
key to a company being successful was in the information it possessed. Nowadays, it is believed that
knowledge is the key to corporate survival; companies need to maintain and make use of both their
internal and external employee, partner and organisational knowledge [8] . Nonaka [23] predicted,
in 1991, that successful companies will be those that create, capture and make best use of their
knowledge and then apply it to new innovative product development. The need to manage and
develop this knowledge effectively has increasingly been acknowledged in order that organisations
may respond to the challenges presented in today’s dynamic and complex business environment and
to overcome economic uncertainties [24] .
Businesses have recognised that organisational knowledge has an essential role to play in responding
to competitive pressures and, for an increasing number of companies, opportunities to establish
competitive advantage lie in their ability to enhance ideas and intellectual know-how. By putting their
valuable knowledge assets to more effective use, organisations can benefit from product
development breakthroughs and improved processes and practices.
In today’s globally competitive marketplace, the issue of knowledge management is an ever-more
important topic on the corporate agenda [25] . To survive, companies must utilise their internal and
external knowledge, including from external parties in their supply chain; being able to capture this
external knowledge will not only create a competitive advantage for the company, but allow it to
develop the skills and expertise of its employees.
The creation of a corporate culture, which allows its employees to share their experiences, has
become a crucial goal for organisations. Corporate leaders are now seeking innovative methods to
capture, manage and share their employees’ knowledge around the business.
Pillai and Min [19] state that knowledge and information play a critical role in the successful
management of an organisation’s supply chain. Ross [27] states that although supply chain members
are independent entities, they still need to form inter-firm and intra-firm alliances, which make them
mutually dependable on each other to meet common goals. All parties in a supply chain need to
work together collaboratively to solve problems and develop innovative products to meet the
demands of customers. Consequently, knowledge sharing should become an integral activity within
any supply chain.
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CHAP II. LITERATURE REVIEW
2 PRODUCT LIFECYCLE MANAGEMENT
2.1 Background
The last two decades have been characterised by major developments in enterprise globalisation and
technological advancement, particularly highlighted by the birth of the Internet and the World Wide
Web (www). This has resulted in many opportunities being created for businesses and individuals,
but also many problems and challenges. In an increasingly competitive global market place,
manufacturing companies in particular have needed to develop flexible and responsive work
processes to ensure their competitive advantage [1] .
The response from industry has seen an increasing focus on lean, green and agile manufacturing
practices, which aim to:
Eliminate waste and the use of substances harmful to the environment;
Reduce product development and manufacturing time and cost;
Improve quality and collaboration; and
Ideally, “get it right first time” [3] .
Furthermore, as organisations continually strive to maintain and develop their competitive edge,
successful product development and the ability to introduce innovation and new product platforms
quickly have been identified as fundamental to corporate success [28] . It is no longer sufficient to
simply re-engineer existing product offerings if organisations wish to survive and thrive. Especially in
relation to the engineering and high technology sectors, effective knowledge sharing and management
is paramount [29] . Failure to achieve this can ultimately restrict access to key information, fail to
address product defects and reduce opportunities for innovation.
Specifically in the case of PD, undertaken by businesses seeking to preserve competitive advantage, a
number of key issues have been identified in PLM literature. Fundamental to these is the acquisition
of intimate customer knowledge, particularly in relation to more demanding expectations and needs
which are ever more personalised. Organisations, in seeking to satisfy these requirements,
increasingly have to:
Innovate
Reduce cost while maintaining or improving quality
Increase the speed of product development
Develop partnerships within extended supply chains
Collaborate globally [[13] , [30] [31] ]
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Advancements in Information Technology (IT) have contributed to greater product complexity, but
also offer the prospect of enhanced facilitation of product development and innovation to meet
today’s challenges. Behavioural changes in corporate culture and strategic direction, however, are
also necessary and PLM has emerged as one strategic approach to create, manage and use corporate
intellectual capital, from a product’s initial conception to its retirement [32] .
Product lifecycle management has been identified [33] as offering the potential to use information
and knowledge more effectively and to innovate more quickly; it has even been suggested that PLM
“is not an option … it is a competitive necessity” for organisations seeking to establish competitive
advantage in today’s global business environment [32] .
2.2 What is Product Lifecycle Management?
Product lifecycle management may be considered as a “business solution for integrating people,
information and processes across the extended enterprise through a common body of knowledge”
which allows for the creation of a sustainable corporate strategy [32] . Often erroneously considered
to be a tool in its own right, PLM is a range of diverse tools, some intricate (e.g. digital design
software) and some basic (e.g. a simple notebook to record data), which have the goal of assisting
with the management of knowledge throughout the lifecycle of a product. Consequently, PLM may be
considered a strategy which enhances organisational learning and the acquisition and storage of
knowledge for future re-use and sharing.
In the modern-day business environment, PLM may be seen to underpin corporate ability to meet
customers’ increasingly bespoke demands in a sustainable and competitive manner. PLM offers
companies the capability of a framework to capture, store, retrieve, represent and re-utilise product
and process knowledge in order to compete more effectively in today’s knowledge-intensive PD
environment [30] .
Figure 2. Major Components of a Typical PLM System, employing Web-Based Communication Tools [30]
PLM, however, is not solely a technological framework (see Figure 2); it is seen to offer social and
cultural dimensions which also contribute to the strategy and competitive advantage of enterprises.
The formalised and facilitated processes offered by PLM encourage the sharing of knowledge
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between colleagues and partners to establish a competitive platform which cannot be replicated by
others.
PLM systems have gained acceptance for managing all information relating to products throughout
their full lifecycle, from conceptualisation through operations to disposal. The PLM philosophy and
systems, therefore, aim to provide support to an even broader range of engineering and business
activities than merely product development. PLM was initially conceived as an academic concept to
address the management of data–information–knowledge during product lifecycles, but subsequently
gained acceptance in industry to provide support to a wider gamut of business and engineering
practices [[31] [34] ].
PLM is an integrated, information-driven strategy which accelerates the innovation and launch of
products and services and provides a central knowledge base to store all product-related knowledge,
data, and processes. PLM extends from initial idea generation and concept description through
business analysis and product design to technical implementation, product testing, market launch,
service, maintenance, product improvement and ultimate decommissioning. PLM aims to gather and
make accessible data, information and knowledge during all stages of the product lifecycle.
Researchers [[13] [35] [36] ] identify significant benefits on offer to businesses through the
implementation of PLM and these include the potential to:
Increase profitable growth as a result of:
Improved alignment of product features to customer requirements;
More efficient collaborative design practices; and
Increased knowledge capture with enhanced portfolio planning.
Reduce manufacturing costs due to:
Better process planning;
The avoidance of faults early in the design process;
A better understanding of future maintenance, service and warranty costs; and
Virtual rather than physical prototyping.
In the broader context of extended enterprises, PLM allows partners to innovate, produce, develop,
support and retire products as if part of a single entity. As a business strategy, it allows for the
capture of best practices and lessons learned, while accumulating a store of intellectual capital for
systematic and repeatable re-use. As an information technology strategy, PLM provides a framework
for enhanced real-time collaboration and data sharing among geographically distributed teams [[31]
[37] [39] ].
It is important to re-iterate, however, that PLM is a process and not a technology tool. Vendors offer
a range of PLM software (see Figure 3. ), but appropriate strategies must be in place to implement
PLM. Participants in the process must understand the vision and the practices and recognise that PLM
builds on a diverse range of other management packages, such as:
Customer Relationship Management (CRM) Systems …customer records;
Enterprise Resource Planning (ERP) Systems … financial records;
Supply Chain Management (SCM) Systems … supplier support;
Human Resource Management (HRM) Systems … employee records;
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Project Management (PM) Systems … project scheduling, tracking, and resource management;
and
Product Data Management (PDM) Systems … product data and workflows [40] .
Figure 3. PLM and Typical Business Software [41]
While PLM offers enterprises and supply chains significant potential to meet commercial and
operational challenges and presents the promise of integrating and making available all information
produced throughout all phases of a product’s life cycle [33] , the demands placed on the
organisations implanting a PLM strategy are complex and not always be the same.
Medium to Large enterprises typically have the resources to meet the cost and personnel demands
of implementing such a strategy, but Small to Medium Enterprises (SME’s) often have special needs
and limited resources. The concept of PLM can offer complete solutions designed specifically for
SME’s which allow them to respond better to their customer’s needs. However, SME’s need a PLM
solution incorporating industry best practices while benefitting from easy and prompt
implementation. Fully integrated PLM solutions are needed to ensure that SME’s are able to maximize
their innovation strategy with easy scalability to meet future needs [13] .
2.3 The History of PLM
The 1980’s saw the development of increasingly elaborate IT tools and software to assist the
engineering process. Early in the decade, the introduction of Computer Aided Design (CAD) systems
heralded a new stage in product development practices. CAD systems enabled engineers to create
geometric product models on computers and an increasing range of software features were added as
CAD became more complex and powerful, establishing the era of Computer Aided Design,
Engineering and Manufacturing (CAD/CAE/CAM). Significantly, such systems also facilitated the
storage and retrieval of design data for subsequent re-use.
Concurrent with the development of these tools was the birth of Product Data Management (PDM)
software which was increasingly necessary to control and manage efficiently the growing amounts of
product data being generated and held for future use. Central data repositories were core to such
systems and the need for strict control of data entry and modification was paramount [30] .
Progressively the scope of PDM expanded and additional functionality was incorporated to allow for
the management of corporate change, enterprise documentation, projects and workflow planning.
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Alongside the development of PDM, other business functional areas had also started to embrace the
power of IT to enhance processes and address specific product lifecycle issues; in particular, software
was introduced for enterprise resource planning, customer relationship management and supply
chain management; however, all of these systems were running in parallel with each other and not
integrated within PDM software, which had been designed to use geometric engineering data.
Finally, towards the end of the 1990’s, and following upon the emergence of web-based technologies,
which presented opportunities for more accessible, more powerful and lower cost IT solutions for
extended teams, the concept of PLM arrived to present the potential to consolidate and use diverse
product-related data, information and knowledge from across businesses. PLM sought to extend its
reach into all corporate areas, including external supply chains, and bring all business and product
development processes under one roof. More importantly, PLM focused on knowledge and
presented the potential to manage it in a more structured and organised manner during product
lifecycles [42] . In turn, it allowed for this knowledge to be re-used in order to acquire a greater
insight into customers and their needs, to enhance product innovation and improve corporate
operational efficiency.
2.4 The Importance of PLM
The scope of PLM is wide-ranging and, for this reason, there is extensive and diverse literature on
the topic [43] . As might be anticipated, the importance of PLM and its potential benefits when
implemented are broadly recognised, but issues for further research and development are still being
identified in different contexts.
Dutta and Ameri [23] recognised the potential of PLM to deliver competitive advantage to
organisations through the embedding of a sustainable and dynamic cultural. However, they
questioned whether PLM had matured sufficiently and asked whether the scope, functionality and
business implications of PLM were well understood by users and vendors alike. They highlighted the
role of PLM as a Knowledge Management system underpinning various product lifecycle processes,
but stated that each process posed different technical challenges, which should be addressed
differently. They proposed four areas for further specific research consideration in terms of the
management of knowledge: Knowledge Representation, Knowledge Capture, Knowledge Generation
and Knowledge Dissemination.
The value of PLM when responding to rapidly evolving commercial environments was also recognised
by Gecevska et al, [13] who proposed a novel business approach for enhancing design coordination
in SME’s via the use of PLM.
SME’s were also a focus for research for Duigou et al. [41] who asserted that the better integration
of PLM into SME’s was necessary in order to improve performance in extended enterprises. They
reported that PLM is underdeveloped in SME’s due to difficulties in implementing new information
systems and, after completion of an IS audit, a framework was proposed to address this issue.
Sudarsan [33] confirmed the increased utilisation of PLM systems, but reported that problems
existed in terms of the sharing of content due to interoperability issues, which acted as a barrier to
further adoption. The need for better integration of systems such as CRM, CSM, ERP, MES, PDM and
SPM was also discussed. It was concluded that improved semantic, linguistic and operability standards
were necessary in order to support PLM and, specifically, to facilitate the sharing of content.
Cantamessa, Montagna, Neirotti [44] referred to PLM as an important enabler of corporate activity.
They stated that PLM systems have to support more unpredictable and knowledge-intensive
processes and often have to interpret tacit knowledge, cover extended periods and encourage
collaboration within dispersed teams and extended supply chains, as PLM generally seeks to integrate:
Design Activities; Knowledge Systems; Project and Workflow Management; and Supply Chain
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Management. They concluded that more effective IT support systems were required, if the potential
benefits on offer through PLM are to be truly delivered.
While recognising the value of PLM in terms of the work of supply chains and dispersed teams,
Sharma [45] suggested that systems still fail to support strategic decision-making. While a number of
significant challenges such as Intellectual Property Rights, Culture, Technology and human behaviour
may exist within extended enterprises, it was concluded that PLM implementation would ultimately
be of benefit to all stakeholders. An integrated PLM framework was presented, as shown in Figure 4.
Figure 4. Integrated PLM Framework [45]
Demoly et al. [46] conducted a comprehensive review of extant literature to conclude that the
benefits of PLM strategies were not being delivered as only a limited range of product lifecycle
information was being accessed. They reported that traditional PLM systems still failed to offer
effective and integrated designs to meet the needs of product life cycles and suggested that they
suffered from a lack of interoperability.
They noted that more-recently developed systems had begun to make use of web-based technologies
and the semantic web to enhance information exchange in extended chains. Further, they recognised
that in the work of others it had been suggested that there is a greater need to extract and manage
data and information contained within traditional files through the development of enhanced tools
capable of managing complex and related information during various life cycle phases.
Jun, Kiritsis and Xirouchakis [43] also referred to emerging technologies which offered the potential
for improved PLM. They reported that product embedded information device technology offered the
potential to track and trace product information through the whole lifecycle. It was suggested that all
stakeholders could access and manage relevant product information throughout lifecycles and such
technologies were seen to be particularly beneficial in terms of PLM phases after product delivery to
customers.
This view was presented under the heading of ‘closed-loop PLM’, which was a focus for the PLM and
Information tracking using Smart Embedded systems (PROMISE) project. The project’s multi-national
consortium had concluded that, while existing systems cope effectively with information flow in the
Beginning-of-Life (BOL) phase of the product lifecycle, “in and from the Middle-of-Life (MOL) phase
to the final End-of-Life (EOL) phase” systems cope less adequately; indeed, they stated that “the
information flow breaks down after the delivery of the product to the customer”. This project
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sought “to create value by transforming information to knowledge at all phases of the product
lifecycle and thus improve product and service quality, efficiency and sustainability” [47] .
Finally, Kiritsis [14] again highlighted the advent of “a new generation of products called smart or
intelligent products” which offered the prospect of gathering data which could be “analysed and
transformed to information to knowledge”. It was suggested that such technology when employed
could become another step in the optimisation of the PLM process.
3 PLM DEFINITIONS
Appeared in the late 90s, the acronym "PLM" Product Lifecycle Management concept has succeeded
the "PDM" ("product Data Management", in order to draw the product information in the industry
[48] . A journey on the Internet (Google) of "Product Life Cycle Management" identifies more than
9.200.000 various links and information overload ... This deserves some clarification.
The literal translation of the Product Life-Cycle Management is the management of a product
throughout its life cycle. This life cycle includes the initial customer requirements from concept
design including the manufacturing design (industrialization product / process), operational life and
end of life (recycling) [49] .
PLM is an integrated approach including a consistent set of methods, models and IT tools for
managing product information, engineering processes and applications along the different phases of
the product lifecycle. PLM addresses not only one company but a globally distributed,
interdisciplinary collaboration between producers, suppliers, partners and customers [50] .
(IBM) defines PLM as “…a strategic approach to creating and managing a company's product-related
intellectual capital, from its initial conception to retirement”
For the analyst [67], PLM is defined as: “a strategic business approach that applies a consistent set of
business solutions in support of the collaborative creation, management, dissemination, and use of
product definition information across the extended enterprise from concept to end of life –
integrating people, processes, business systems, and information.”
For the PLMIG1 (PLM Interst Group), PLM includes research, management of customer
requirements, product development CAD, CAM, simulation, rapid prototyping and virtual
concurrent engineering, product / process design, sourcing of components, machining digital control,
collaboration via the web with customers and suppliers. PDM is the IT Platform for PLM, the terms
'PLM System' and 'PDM System' mean the same thing, and are interchangeable
Despite the conferences / Journals [52] , [53] , [54] , books [49] ; [55] websites (PLM Interst Group)
meetings, and especially industrial needs, there is a really need to map the PLM. Especially, when it
goes through the literature and see the words often associated to PLM (Table 1).
Terms related to PLM Autors
Collaborative Mode CIMData
(Miller, 2003)
(PLM Interst Group)
(Abramovici, 2005)
Strategic approach (Abramovici et al., 2004)
(Saaksvuori, 2008)
1 http://www.plmig.com/
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(CIMData), (IBM)
(Amman, 2002)
Requirement management (PLM Interst Group)
PLM Process (CIMData)
(Saaksvuori, 2008)
(Fathi, 2007)
(Schuh, 2006)
(Feldhusen, 2008)
(PLM Interst Group)
PLM Architecture (IT tools) (CIMData)
(Saaksvuori, 2008)
(Fathi, 2007)
(Feldhusen, 2008)
(Miller, 2003)
(Abramovici, 2005)
Integrated Business approach (CIMData)
(Miller, 2003)
(Abramovici, 2005)
Integrated management (Feldhusen, 2008)
Product structure (Feldhusen, 2008)
(PLM Interst Group)
Concurrent Engineering (PLM Interst Group)
Engineering process management (Abramovici, 2005)
Table 1. Terms listed in PLM definitions, adapted from [56]
Beyond these terms listed in different definitions, we find a multitude of acronyms and other topics
associated to PLM. The combination of all these terms/topics and acronyms is mainly due to the vast
field that PLM is trying to cover. Today, PLM aims to address several concerns, via tools and
resources often based on standards such as:
Design Tools / Manufacturing / simulation of product data (CAD, document management ...)
Means of collaboration, management and sharing product data.
Standards and practices for the unification of data formats, languages, sharing and services.
Following the various definitions and areas related to PLM, we noted that we could combine these
terms along defined axes by grouping keywords/terms according to their areas. Our initial analysis
leads on drawing four pillars (levels) grouping terms often associated with PLM (Table 1). These
pillars are: the strategic level (Integred Business approach, Portfolio Management, Virtual Enterprise,
...), level (definition) process (Requirements Management, Corporate Management, ...), the
organizational level (collaborative mode, concurrent engineering, ... ) and finally the tools
implemented (ICT Architecture, product Structure, ...).
We firstly define these four axes and then present the different terms / areas often related to PLM.
4 THE PROPOSED PLM AXES
PLM is a complex phenomenon in which several dimensions and disciplines use their contributions
[57] , “bringing together products, services, structures, activities, processes, people, skills, application
systems, data, information, methods, techniques, practices and standards” [58] .
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PLM, product lifecycle management, (management) is the act of bringing people together to
accomplish common goals. Therefore, there are at least five questions that must be taken into
account in the management of the life cycle of the product [59] :
1. When: the step where management occurs (Strategy / Process)
2. Who: people, organizations involved in PLM (Organization)
3. What: objects to manage in the PLM (Process)
4. Why: challenges, motivations and objectives of PLM (Strategy)
5. How to: the features and technologies that support PLM (Tools)
The (Figure 5. ) shows our literature review synthesis, mainly based on some PLM definitions and
terminology, and terms assigned to the PLM by different authors. These four axes will serve as a
reference in the rest of our work. Strategy is the highest level, where important decisions are taking
and in this level we define the kind of organization and processes. The organizational level describes
the shape of structure based on processes. Tools level is the implementation of processes and the
support for the organization.
Figure 5. PLM axis
4.1 Strategy
Most SMEs focus on the productionErreur ! Source du renvoi introuvable.. Operational
activities require efforts and SMEs don’t feel the need to formalize a strategy. Michael Porter [22]
defines the strategy: "The strategy is to define the general guidelines for the company to have a
sustainable competitive advantage." It defines all tasks which are related to plan, organize, execute
and control value chain processes.
Strategy is also a "whole range of objectives and means guiding organization activities for the medium
and long-term …» [62] . It therefore involves decisions in terms of activity and resource allocation.
Decisions involving the commitment of financial resources (management of product / project
portfolio), human and material (must be quantifiable). The strategy reflects the company policy that
brings together members of the enterprise around the same project.
PLM area Strategy field
Strategy Process
ToolsOrganization
Decides
Use
Defin
esIm
ple
men
t
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Dig
ital
ente
rpri
se
Exte
nded e
nte
rpri
se
Vir
tual
ente
rpri
se
CR
M
Port
folio
man
agem
ent
Defining general guidelines, measures and strategic goals (Budde, 2010)
Thinking about international regulators (environment)
Complying to legislation requirements (Silventoinen, 2009)
Controlling the primary value chain activities (Budde, 2010)
Evaluation of opportunities and risks (external: STEEP, etc.) (Budde, 2010)
Assessment of Strengths and weakness (internal: SWOT, etc.) (Budde, 2010)
Business model supporting product/service
Supporting: innovation (Subrahmaniam, 2006), voice of customer, voice of market.
customer needs management (Debaecker, 2004)
Product portfolio (Budde, 2010)
Customers integration into product development (Debaecker, 2004)
Table 2. Resume of strategy axis
According to PLM, the strategy will define the organization of enterprise, how to execute operational
processes and ensure customer satisfaction. Another part of strategy consists on the lifecycle
orientated product portfolio management which determines the project portfolio of all development
projects [56] .
In projects around PLM, two major orientations exist. The first trend is projects that require many
developments according to the company history, its complexity, or the constraints of its business.
These projects require a strong accompaniment. The second trend, the highest currently, aims to
rapidly deploy the project, while minimizing the cost.
For this, we continuously seek balance between needs, earnings and the software standard features.
Budgets restrictions grow segmenting projects into cheapest small lots, which would reduce the
overall goal [49] . Therefore, it is important to define what you want and how the project fits into
the business strategy.
4.2 Organization
The organization of an enterprise involves organizational structure but also its manufacturing
technical organization and human and relational aspects. The organization must therefore formalize
the work sharing into distinct tasks and coordination between these tasks. Structure is also a part of
the enterprise culture.
“A structure is a set of functions and relationships which determine formally the tasks and functions
that each organizational unit must accomplish and ways of collaboration between these units " [63] .
PLM area Organization field
Colla
bora
tive
work
Busi
ness
Skill
s
inte
grat
ion
Dig
ital
Engi
neeri
ng Specification of the operational organization (Budde, 2010)
Specification of the organizational structure (Budde, 2010)
Skills, motivation, turnover management
People and culture management (Budde, 2010; )
Supply chain management
Resources, tools selection/management
Decision making concerning product/Service
Formalization of work sharing
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Tasks coordination
Table 3. Resume of organizational field
In the PLM context, the organization dimension focuses on the definition and optimization of the
entire company. The success of PLM system deployment and its implementation is based on following
process defined by methods [64] . People management, motivation management and skills
management are key areas for the PLM implementation. According to [65] the process of skills is
essential to implement the organizational process and to ensure their acceptance. Collaborative
design offered by PLM enables teams to work rather than sequentially, but concurrently.
In [66] [67] , authors classified the stakeholders involved in the PLM system into eight groups:
developers, suppliers, manufacturers, transporters, distributors, customers, maintenance and
recycling.
4.2.1 OEM/Suppliers interoperability
Managing the OEM/Suppliers relationship aims to manage different levels of collaboration considering
all of the chain:
The strategic level defines the objectives of the relationship by asking questions about supplier’s
competencies of providers to associate, on their levels of integration in the project, and the
potential for collaboration between suppliers through networks, etc.
The organizational level defines the shape of structure and collaboration modes that will
achieve strategic objectives.
The operational level defines processes that implement modes of collaboration and governing
organizational structures.
The system information level defines the functions of computer tools and their communication
in order to ensure collaboration. This level also applies to all rules and procedures for system’s
use.
The implementation of such relationship requires establishing effective communication between
different enterprises through interoperability mechanisms on several levels.
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Figure 6. Interoperability through PLM axes
4.3 Process
A “Process” can be defined as a “set of interrelated or interacting activities, which transforms inputs
into outputs”. These activities require allocation of resources such as people and materials [68] .
Collaboration in PLM systems is essentially managed by business processes. Working methods or
processes cannot be fixed, in that they depend on the context of the collaboration. Several
parameters determine the activity and collaboration must be identified and integrated into the PLM
system to gradually move towards flexible process (or agile) [69] .
PLM deployment or implementation needs to follow a systematic process or methodology [64] . The
issue of process within the PLM system is a complex issue. PLM system is intended to manage
process that directly contributes to the development of products throughout their life cycle.
Processes managed by the PLM, are those purpose and / or results are used to generate elements of
the product, process handling the elements of the product, the organizational structures to
manipulate elements. This includes both the business processes, process flow management, etc. [70] .
Today, most enterprise applications (for example, ERP systems) include a WFM component, and
workflow engines have been used as the control elements. Today, workflow becomes a systems
development platform in its own right and a way to develop new business applications (Smith, 2004).
According to the operational level, the PLM process execution is supported by the Process Support
System. The Workflow Management Systems (WFMS) enables a higher level of process automation.
Especially in the context of distributing and releasing unstructured content like product specifications
in cross-functional teams, the WFMS plays an essential role through a strong link to the Product
Data Management System (PDM) [56] .
Much of the activities related to product development are to introduce changes to existing solutions
[72] . The engineering changes released are based on a process of formal modifications where
different stakeholders (manufacturing, inspection, purchasing, customers, suppliers, etc.) play specific
roles. Change Management Process maintains the integrity of the product and guarantees the
traceability of changes.
Strategy
Process
Tools
Organization
PLM Goals, organization mode
Skills, Best practices, Ontology
Standards, process synchronization, process models,
Ontology
Implementation, Data models
PLM axes Interoperability levels
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PLM area Process field
Work
flow
s m
anag
em
ent
Stan
dar
ds
Pro
cess
Modelin
g
Know
ledge
Man
agem
ent
Change management : CR, ECR, ECO
Specifications
Standards
Data mining
Capture, Dissemination, Transformation, sharing
Improving process automation [73]
Traceability of processes (Silventoinen, [73] )
Managing complex products (Silventoinen, [73] )
End of life decision making (Parlikad, [74] )
Table 4. Resume of process field
4.4 Tools
PLM tools, based on data digitalization, are used for configuration management of the digital mockup.
PDM and PLM solutions bring together the product, tooling and production line around a single
database. In the manufacturing industry CAD/CAM tools can be subsumed under this software
support category. In a database context, this functionality is similar to a scheme definition. The PDM
system stores all product relevant data according to this definition, and provides different views for
each stakeholder, be it from a marketing and engineering department. The Multi-Project Management
System and collaboration tools are instruments for the management of the product in different
phases in a collaborative environment [56] .
PLM area Tools field
Vis
ual
izat
ion
ICT
Arc
hitect
ure
Pro
duct
Configu
ration
Deci
sion-s
upport
sys
tem
Pro
cess
support
sys
tem
3D Model, CAX (CAD, CAM, …)
Requirements tools (Doors, etc.)
PDM, ERP, CRM, SCM, MES, … IT tools
Product models
Collaboration Platform
Operational support systems (Mertens [75] )
Planning and controlling systems (Mertens 2009)
Table 5. Resume of tools field
Tools are the implementation of process, based on methods according to the strategic level. As an
example, the DSS (Decision-support system) aims to provide aggregated information on the product
lifecycle to the highest level of management. This kind of IT-support provides essential information
for the control and optimization of the current innovation portfolio as well as the product portfolio.
For the planning of long-term resource capacities we have the Strategic Resource Management
component [56] .
The development of PDM to PLM systems provide tools such as "workflow" and project
management, as well as numerical models to visualize a collection of components designed with
heterogeneous software [76] .
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CHAP III. MODELING FRAMEWORK
5 MODELING FRAMEWORK
Nowadays the computers plays an important role to develop the products, typically the segments
regards to first product’s engineering specification to its physical parts. PLM is supposed to develop
mange and integrated information from the first conceptualization to the disposal points and the
volume, diversity and complexity of information that describe the product will increase too.
According to road map relate to previous section that [77] proposed it, the main issues of PLM
research in academic center is divided to following group (Figure 7. ).
Figure 7. Modeling in PLM framework
5.1 Process planning and application integration
Process is defined as a group of structured activities that result in a determined product for a specific
client. It generally involves activities from various functional departments across the organization and
has a clear customer orientation throughout business processes, product data is generated.
Therefore, the description of the enterprise processes builds the ideal foundation for PLM strategies
and is considered the suitable underlying paradigm for the proposed PLM frame work. The
identification and modeling of enterprise processes can be used as an efficient tool to capture and
share process knowledge within the organization .A business process model is a representation
expressed through the formalism specified by a modeling method. A special class of enterprise
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models is formed by more comprehensive and generic model called reference models, which may be
used as the basis for the development or evaluation of specific models.
During the last decade, different information modeling frameworks in PLM domains have been
presented by researchers. One of them has been proposed to depict different activities, information
flows and different stages that must be followed by the stockholder. Its application is in original
equipment manufacturer (OEM) and supplier companies interacting with PLM and CAD/CAM tools
[78] .
Design coordination in context of structure of the project, is related to identifying the local
objective, assessment of resources, scheduling of the tasks and identifying the criteria. In SMEs,
usually design coordination narrated in macro-level and unfortunately is not correspondent to the
complexity of the current process. Merlo et al. have proposed a business approach for improving
design coordination in SMEs through PLM system [79] .
Process planning is an important step to converting a design concept to manufactured product.
Nowadays system digital manufacturing is an important component of PLM and is one of the available
solutions for managing integration knowledge information data (KID) regard to process planning.
Many researchers focused on a computerized solution for computer aided process planning (CAPP)
but since CAPP problem is complicated and must be describe with many dependent technical and
business variable, Denkena et al. have proposed an ontology which form the basis for developing
decision support and knowledge management capability to increase the CAPP solution [81] .
Nowadays SMEs have significant cooperation to be involved as a supplier in complex multidisciplinary
engineering context. Qualitative PLM solution should be more economic for such enterprises. In
[82] , authors have proposed a holistic approach with aim of supporting integration,
Structure and synchronized multidisciplinary product data relate to flexible engineering process to
support a better customization of PLM solution [82] .
The enterprises are complex system that needs to use their process in different environment such as
marketing, supplier and humanity in a way to obtain maximum value. For this reason, Fathallah et al.
have proposed an integrated enterprises model to increase the perception of PLM view. Their model
is built with a semiformal languages regard to main principal of PLM concept .In this model further
details information offer ranged in to BOL, MOL, and EOL [52] .
Process planning activities have critical role in manufacturing environment and collaborating different
companies in product development is necessary. Siller et al. have proposed a work flow model for
process regards to collaborating planning with help of CAD, CAM tools &PLM concept. The target
audiences are OEM and supplier which interact with each other in different activists, different steps
and information flows. In this study a case study has been considered to valid the model. The specific
extended enterprise chosen for the case study is dedicated to the manufacture of molds for ceramic
tiles as OEM. In addition two other enterprise (B and C) are selected as the best members of the
supply chain providing parts for the molds, and there is a competition between them to be selected.
There are types of supplier for parts that require certain machining operations. The (Figure 8. ) is
the process model that they have proposed with BPMN [78] .
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Figure 8. Proposed BPMN work flow for collaborative process planning [78] .
Developing complex product is a great criterion in enterprises that usually face them with two
constraints. In one hand, the companies’ market pressure to develop their products which result
from competition simultaneous enterprises. In another hand with higher quality demands and
complicated undertaking result is a major problem in developing of complex technical product.
Hence an approach to detect defect process is needed to contribute measuring quality of product
and reach to high level of acceptable quality. Jacobs et al. we’re wondering if defect could be prevent
& in continue fewer defect could be expect by a proper modeling of project life cycle. Their aim was
to obtain advantage of PLM modeling regard to reducing the number of defect in to the delivered
product. Their research involved two case studies to address localization and recognition of defect
sensitive area in compel product development [83] .
PLM is introduced as business strategy for managing enterprises [84] . In [84] , authors believed that
PLM solution effects the business process deeply and require the analysis and if necessary re-
engineering of process. In their research they have focused on application of process modeling and
techniques of simulations for implementing PDM and PLM system in SMEs by means of total quality
procedures. In their study ,two AS-IS model have been introduced that the first one is based on
extracting the process knowledge from total quality management and the second one is based on
interviewing with company’s staff. By these models, a gap analysis has been carried out to determine
which parts of process could be modified and in continue improved through PDM system which will
provide manager to forecast a complete extension to the PLM paradigms [84] .
5.2 Information modeling and application integration
Although PLM has been used widely in enterprises, it is unable to manage the modeling and
simulation aspects of virtual products. In order to support interchanging and sharing information of
Design Manufacturing Proposal Quotation Planning Manufauturing
Ent.
BEn
t.C
Ent.
ASh
op
flo
or
Te
chn
ica
l De
pt
De
sign
De
pt
Pu
rch
asi
ng
De
pt
Ma
cro
Pla
nn
er
Mic
ro P
len
ne
r
Te
chn
ica
l De
pt
Sho
p f
loo
rM
icro
P
len
ne
rM
acr
o P
lan
ne
r
Design
3D
Model
Meta
Plan
Send Request
for Quotation
to SME B
Evaluate
Quotation
Recieve
Quotation
from SME B Assign
to
SMEA
Send Request
for Quotation
to SME A
Recieve
Quotation
from SME A
Assign
to
SMEB
Receive
Request
Receive
Request
Macro
Plan
Macro
Plan
Rogh
Micro
Plan
Rogh
Micro
Plan
Send
Quotation
Send
Quotation
Recieve
Contract
Final Micro
Plan
Generate
CNC code
Manufacture
Manufacture
Generate
CNC code
Final Micro
Plan
Recieve
Contract
Select
Review
Review
Review
CAD file
Progress
Planning File
Progress
Planning File
Progress
Planning File
QuotationContract
All Quotations
Quotation
Progress
Planning File
Progress
Planning File
Contract
CNC codes
CNC codes
Work with?
Review Manufacturing
Rejected
Accept
SME B
SME A
Yes
Yes
No
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this type of product, a product’s four-dimensional (4D) informational model, (PLIM) has been
determined which include geometry, task view, system view, and lifecycle view and process
information in all life cycle of virtual product [85] .
PPRO model is another information framework which enables the manager to process technical data
throughout the lifecycle management applied in SMEs [86] . In this model, due to the requirements,
problems and also benefits of the PLM-based solution for SMEs and OEMs, we chose PPRO models.
NIST reference model is a single product interoperability framework which is able to assess, store,
serve and reuse all the product information throughout the product’s lifecycle is proposed on the
basis of National Institute of standards and Technology [87] . The advantages of this framework are
to include most of product information regarding the PLM system and its subordinate and to support
interoperability among CAD, CAE, CAM and other interrelated systems. It provides a general
depository of product information along life cycle and became our reference model for comparing
with PPRO.
Current industrial practices which are active in management of product of data, system management
of manufacturing process, need higher efficiency, more flexibility and sensibility in management of
product information in different level of product life. [88] have proposed a theoretical approach
called product relationship management approach (PROMA) .The main objective of this approach is
to manage product relationship information of vital yet complex product to enable concurrent
product design and assembly sequence planning [88] .
6 RECENT INDUSTRIAL STATUS OF PLM
SOLUTIONS
PLM systems have an impact on all parts of an enterprise and need a structure to organization
informative flows and also require decision makers to be identified. This issue will be a problem in
SMEs and difficult to predictable. Because most of the time someone must provide Different
functions and decision makers are individuated more on the basis of capital share than on the basis of
The intrinsic complexity of PLM systems and the informal organization of small companies results
difficulty of implementing PLM in SMEs [89] .
Industrial Field Study Conclusions Process planning combines both technological and Business
considerations to select the optimal process to ensure part quality in accordance with specifications
and at minimal cost. Nevertheless, our overview of SME manufacturers shows that:
Contrary to CAD/CAM, CAPP is not implemented in SMEs.
Management of infrastructure knowledge (machining equipment and tools) is lacking.
Production process knowledge is not managed, but rather only documentation of work
performed.
Difficulties exist in identifying similar jobs (e.g., in case of significant engineering change, job is
considered entirely new), with reliance only on employee memory.
Digital information is transferred from designer to manufacturer (i.e. STEP and PDF files), but
there is no digital data collaboration for transferring feedback from manufacturer to designer.
6.1 Needs analysis of PLM system in SMEs
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In order to practice methods regards to integrated management of information and process,
analyzing the needs of the small companies in terms of PLM is the first step. (Le Duigou. J, et al)
propose an approach that will provide the manager to manage technical data all over the life cycle
management [86] Their approach includes PLM needs case in a company environment that designs
and produces families of ship. The results obtained confirm that their proposed solution meets the
needs in terms of PLM of this type of company. Their approach deals with supporting the
capitalization, the reuse and the extension of fundamental knowledge and for development and
industrialization of the product. After an audit of the company, the main needs in terms of PLM have
been recognized and several functionalities are developed to encounter the needs regards to PLM
systems.
6.1.1 Knowledge capitalization
An improvement of design, the feed backs which comes from customers and a set of experiments for
optimizing the design cannot be done for all part families without involving the designer to test each
of them separately.so it needs that when there is a modification in designing of the product, this
changes will be implement automatically for all the products of the family.
6.1.2 Reference management
Because of exiting many references product, the management of them is quite hard and efficiency.so
a system of referencing based on the functions of the product family is needed to address the new
product automatically to the specific function without opening all the previous possible references.
6.1.3 Quote
In Quotation phases and initial design, there are no accesses to detailed process. So this issue faces
the management to problems such as the lack of awareness about the amount of raw materials and
the time of manufacturing before the precise design of the product. The price of the product
depends on its functional definition.so ascertaining practically the relation between price and each
function is necessary.
6.1.4 Archive management
When a customer gives back the product, it is not always easy to find the original drawing of the
product that has been sold with the references of different parts.so needing to register the modified
dates of functional parameters and the related values is important.by this data base, the exact CAD
of past product and its parameters at the product sale date can be found easily. Furthermore a
comprehensive description of requirement to have PLM system in SMES can be addressed as
following [90] .
6.1.5 Interoperability
One particular aspect of this needs relate to have an extensible integrate system which has ability to
allow interoperability with another CAD or PDM system. Regards to this integration of CAD or
PDM, a low cost one is a major factor and in a same time it must be user friend and provide them
the ability to extend it and maintain.in another hand implementing the system must not cause any
important notification in the current system of servers and client side.
On the other hand the needs of SMEs have been studied as decrease the time of designing and
reduction risks of mistakes in management of product structure specific needs for designer have
been investigated as bellows [91] .
6.1.6 Needs of design
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Ability to export CAD data and product structure to PDM and in inverse direction and in order to
manage EBOM and CAD dependent documents is significant. In addition enable data exchange with
supplier or customers in case of asynchronous collaborative design is important.
6.1.7 Assembly planning and product development
The SME especially faces difficulties of assembling all parts made by different subcontractors, As a
result of poor integration of assembly process planning and the product development process. This
separate undertaking of assembly planning and product development resulted in much rework and
poor product quality.
7 A STUDY OF THE INFORMATION MODELING
FRAMEWORK
PLM systems are a solution to construct and share product information and are already implemented
in large companies, especially in those active in aerospace and automotive domains. In small to
medium enterprises, some issues as cost, complexity and management of PLM tools coupled with
CAD tools, appear to be difficult to implement while they can be a competitive factor in the field of
the extended enterprise. A key point emerging now is how to manage the communication related
issues, sharing the design, project management, throughout the development of a product between
OEMs and SMEs. In this study, two recent information modelling frameworks for PLM system and
data model of a free PLM tools called Aras Innovator (Aras 2013) are compared to determine their
individual applications and differences. The first framework is proposed by NIST (National Institute of
Standards and Technology) [87] and the second one [86] .
7.1 Comparison of data models
In this section, all classes and packages of PPRO have been compared with NIST framework and the
comparison is presented in table 1. In the first column, classes of NIST have been indicated. In the
second one, the significance of each of them has been mentioned briefly. Third column considers if
the corresponding class exists in PPRO or not. If this class is designed in PPRO, then it will be
introduced in the fourth column. In lack of related class, the indirect corresponding class will then be
considered in fifth one. In sixth column all classes and sub-classes of two previously mentioned
models have been compared with the structure of the PLM data model in Aras software and finally
the last column is dedicated to the usage of the related class in type of PLM system.
By using this comparison, we have identified differences between the data models. The next first
paragraph considers how these models manage the data coming from CAD tools and interoperability
with enterprise resource planning (ERP) with actual situation of resources in enterprise. The second
paragraph consider how these two models present evolution of family part and finally the rest one is
regarded to two advantages of cost indicators and class of requirements that do not exist in a same
time in both of them.
In PPRO, interoperability between PLM systems with the CAD and the ERP systems is not direct.
CAD links are stored in the object attributes of the product. In NIST framework, OAM uses the data
model structures of ISO 10303, informally known as the standard for the exchange of product model
data (STEP). Database contains detailed information related to geometry assembly and tolerance
information, structure, behavior, and kinematic of the whole product. OAM is well developed to
overcome interoperability platform between different CAD tools with facilitating information
exchange by means of appropriate OAM database.
Classes in schema of
core product model
Class’s Representation Determined in
main model of
PPRO?
Corresponding package and
Class in PPRO
Indirect
corresponding
in PPRO
Correspondin
g tables in
Aras
innovator
Application of
usage in PLM
system
Yes NO
Core Product Model highest level of generalization for all CPM classes Product
(Team works in tree view)
Design All type
Common Core
Object
base class for all the object classes Product Product All type
Common Core
Relationship
base class which all association classes are specialized Import with
CAD file
CAD
documents
All type
Core Entity base class which the classes Artifact and Feature are
specialized
Import with
CAD file
CAD
documents
All type
Entity Association relationships may be applied to entities in this class Product (structural view) Relationship
types
All type
Core Property abstract class from Function, Flow, Form - Product All type
Geometry spatial description of an artifact or feature Product CAD
documents
All type
Material material composition of an artifact or feature Material Design-part All type
Constraint relationships applied to Geometry and Material Function Design-part All type
Artifact a distinct entity in a product, whether that entity is a
component, part, subassembly or assembly
Product Design-part All type
Feature portion of the artifact’s form that has some specific function
assigned to it
Import with
CAD file
Design-part All type
Port specific kind of feature, referred to as an interface feature Import with
CAD file
Design-part All type
Specification collection of information relevant to an Artifact deriving from
customer needs and/or engineering requirements
- Item Types-
part a container for
the specific
Requirements
that the
artifact must
satisfy
Requirement specific element of the Specification of an artifact that governs
some aspect of its function, form, geometry or material
- Item Types-
part strictly
determined by
a behavioral
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model
Function what the artifact or feature is supposed to do Activity process All type
Continue of table: Comparison between corresponding class diagrams of CPM, PPRO and Aras innovator
Classes in schema of
core product model
Class’s Representation Determined in
main model of
PPRO?
Corresponding
package and Class in
PPRO
Indirect
corresponding
in PPRO
Correspondi
ng table in Aras
innovator
Application of usage
in PLM system
Yes NO
Flow is the medium that serves as the output of one or more
transfer function(s) and the input of one or more other transfer
function
Technical -of
service
Item part All type
Behavior describes how the artifact implements its function - Item -
Behavior All type
Form design solution for the problem specified by the function Item Types-
part All type
Constraint a specific shared property of a set of entities that must hold in
all cases
product Item -
Behavior All type
Entity Association a simple set membership relationship among artifacts and
features
- Item part-
relation All type
Usage a mapping from Common Core Object to Common
CoreObject
Synchronized with
ERP
- - when constraints
apply to multiple
“target” entities but
not to the generic
“source” entity
Trace structurally identical to Usage Synchronized with
ERP
- - the “target” entity
in the current
product description
depends on a
“source” entity in
another product
description
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Table 6. Comparison between corresponding class diagrams of CPM (from NIST), PPRO and Aras innovator
Process Information attribute of Artifact, contains product development process
parameters
Activity-version Product-item in a Product
Lifecycle Management
(PLM)
Rationale attribute of Core Property Activity-version - documents
decision in the
product development
process
Transfer Function specialized form of Function involving the transfer of an input
flow into an output flow
- - All type
Although OAM reference is a comprehensive database, his application is mainly for mechanical
design. In another hand, for SMEs, managing CAD data directly into PLM systems could be difficult to
implement and might not be economic. An advantage to just have CAD files links is the facility to
implement it in free software like Aras innovator (Kiritsis, 2003). Furthermore, PPRO in
manufacturing view can synchronize with the ERP of the enterprise to obtain up-to-date BoM and
processes such as the items, the operations, the work centers, and their respective attributes. These
items are proposed by classes Usage and Trace in NIST.
Another aspect of comparison between these two main models refers to the evolution of family part.
PFEM in NIST proposes evolution of product and rational of changes, whereas in PPRO a new
product family, new organization of a department or a new supplier are done by adding a specific
class regarding to new product, organization, supplier or customer. This approach does not consider
the reasons of changes and the relation between different versions, series and families. So, evolution
in NIST seems more documented.
Product cost is one of the most requested indicators to guide and help decisions for design and
industrialization in SMEs that PPRO has considered in its generic model and can be another
advantage for it. In NIST there is no structure for covering this criteria whereas the requirement and
specification are important classes that are available and do not exist in PPRO. In next section it will
be proposed a way to developed PPRO in order to take advantages of these classes.
7.2 Developed model of PPRO
In previous section, a sample table showing comparison between the classes of PPRO, Aras
Innovator and CPM of NIST has been illustrated. Although PPRO is an efficient methodology for
implementing the major needs of SMEs, there are still some classes and relations contributing to
improve its efficiency. Regarding to previous section and mentioned differences, we proposed a
developed model of PPRO with considering classes such as requirement, specifications and extended
product family (Figure 9. ).
Figure 9. Class diagram of evolution of product related to developed PPRO
In order to create a product family in PPRO, the user adds new sub-class (a family of products) on
the generic class of a product. New sub classes are inheriting the attributes of the product class. But
the relation between the previous design and new one, series and version has not been declared. In
NIST, the hierarchies between versions, series and families have been extended. In (Figure 9. ) the
method of adding these diagrams to the product has been showed. The extended class regarding the
version is different from those in activity and resources. In (Figure 9. ) a chronologically sequenced
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chain structure of versions makes series. A collection of series makes family. Family is the designation
for an entire product family.
Version derivation and series derivation are classes which present association between a version and
its predecessor version and for latter a series and its predecessor series. Family derivation which is
composed of series version and version derivation contains the previous relationship between series
and versions in the evolution of the product line. Design evolution represents the design information
and descripts changes between particular series or version and their predecessor. Family evolution
and design evolution aggregate to build evolution class.
Another difference between the two models was related to requirements and specification classes
which have been considered in NIST but are less documented in PPRO. For satisfying this item,
SysML [93] diagrams and the way for considering the requirements of product took place. SysML is
for supporting the specification, analysis, design, verification and validation of systems that include
hardware, software, data, staff, procedures and facilities. This method is a subset of UML 2 with
extensions. According to (Figure 10. ), interaction between suppliers and OEM is significant. SysML
could provide a way for companies to consider the requirements and specifications related to
customer’s need or process engineering.
Figure 10. Developed PPRO of product package related to class diagram of requirement
The application of this system modeling language in structure of data model will allow the OEM to
share the information. In figure 6 the class of requirement linked to product has been added to class
diagram of PPRO. In (Figure 11. ) class diagrams related to resource family and requirements have
been extended.
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Figure 11. Developed PPRO of Resource package related to class diagram of requirement and product family
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CHAP IV. Knowledge Management
8 WHAT IS KNOWLEDGE?
In order to make a unique contribution to the research field of knowledge management within supply
chains, it is necessary to have a clear understanding of what is meant by the term ‘knowledge’. Many
definitions exist in published literature which seeks to explain what the term knowledge management
actually means and an introduction to this literature is now given.
In 2005, Ngai and Chan [115] referred to knowledge management as a process or practice that
develops the ability of an organisation to create, capture, store, maintain and disseminate its internal
employee and organisation knowledge. O’Dell and Grayson [116] agreed, but added that the process
must also involve getting the collected knowledge to the right people at the correct time in order to
improve business functionality. In 1999, however, Liebowitz [117] explained that knowledge cannot
necessarily be managed and instead is a set of activities that companies must practice in order to
achieve competitive advantage. These activities were described as: Knowledge Creation; Knowledge
Valuation and Metrics; Knowledge Mapping and Indexing; Knowledge Transport; Storage and
Distribution; and Knowledge Sharing [118] .
However, before exploring knowledge management further, it is important to be clear about how
knowledge itself is defined. Researchers [[119] [122] ] exploring the practice and theory of KM often
distinguish between data, information and knowledge. In 2006, Keane and Mason [123] reported that
researchers and practitioners should not be too concerned with differentiating between these three
terms and others [124] [125] agree, stating that the three terms are merely synonyms of each other.
Most researchers, however, agree with Zeleny [126] who believes that an understanding of the
various components of knowledge is essential. For the purpose of this report, an explanation of these
is now provided.
Firstly, what is data? According to Wilson [53], Data relates to the representation of information
independent of its meaning or, in other words, facts and figures in their basic form. Shih et al. [122]
adds that data is the raw stimuli which is not ready to be utilised by an individual. Court [125]
explained that data is likely to be of little use to an individual or organisation if it has no meaning and,
therefore, must be transformed into information.
What is information? In 1994, Nonaka [128] referred to information as “the flow of messages” within
an organisation. Wilson [127] and Floridi [129] both acknowledged that information must also
include a level of meaning and so expanded on Nonaka’s definition by commenting that information is
data plus the meaning connected with it. Court [125] added that information can be viewed as the
combination of raw data and meaning.
Various definitions exist in extant literature to define the term ‘knowledge’. Zeleny [126] stated that
knowledge relates to an individual’s ability to make distinctions, choices and decisions about
information that they have acquired. Alavi and Leidner [130] stated that knowledge is information
contained in the mind of an individual, while Rodgers and Clarkson [131] agreed, but added that
information is everything outside of an individual’s mind. Court [125] further explained that
knowledge is recorded information i.e. outside of the mind that is then stored in the memory of
individual and can include ideas, concepts, data and techniques. Nunamaker [119] sought to clarify by
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stating that knowledge is information that is organised into meaningful patterns that can turn into
repeatable processes. They added that people can develop these patterns in knowledge through
awareness and understanding of particular topics and their implications.
Turban and Frenzel [132] stated that knowledge is organised information applicable to a specific
problem; they continued by stating that the information has been organised and analysed to make it
understandable and applicable to solving the problem. Van der Spek and Spijkervet [121] expanded
on this by adding that knowledge is the set of insights, experiences and procedures that are
considered correct and true and can, therefore, guide the thoughts, behaviours and communications
of other people. Shih et al. [122] further added that knowledge is the high value form of information
that may be useful to an individual when making decisions and prompting actions. Wiig [133] finally
added that knowledge can consist of personal perspectives or concepts, judgements and expectations
and personal know-how.
Ackoff [120] continued the discussion by adding the term ‘wisdom’ (see figure 5), which adds the
concept of good judgement to knowledge and makes use of acquired experiences on the part of
individuals and organisations. Nunamaker, Romano and Briggs [119] add that an individual must have
wisdom to make intelligent decisions and judgements about what to do with knowledge once it has
been acquired. Finally, Ackoff [120] explained that data, information and knowledge relates to the
past whereas wisdom relates to the future actions of an individual, including the design and vision for
future actions i.e. the ability of an individual to perceive and evaluate the long term consequences of
an action [134] .
Figure 12. The Data-Information-Knowledge-Wisdom Hierarchy [120]
For the purpose of summarising the different terms already discussed, the following explanation to
help differentiate between data, information and knowledge is proposed.
Data relates to facts, figures, images and sound in their rawest form. Data is typically collected through
primary research methods, including quantitative and qualitative techniques, such as survey results, face to
face interviews, tests and simulations. Once the data has been collected, it is then formatted, filtered,
summarised and converted into information. To turn data into information there must be an action on the
part of an individual or group to analyse that data and infuse it with meaning. Once the information has been
analysed and summarised, it is then distributed and applied to others or to certain processes e.g. procedures
or rules that guide individuals on how to complete tasks. Finally, through the observation and interpretation of
available information, knowledge is then acquired to influence and be used in future thinking and actions.
Research [[23] [135] [136] also shows that there is a distinction between personal knowledge and
organisational knowledge. Tsoukas and Vladimirou [137] explained that personal knowledge is the
“individual capability of a person to draw distinctions, within a domain of action, based on
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appreciation of context or theory, or both” whereas organisational knowledge is the “capability of
members within an organisation to draw distinctions in the process of carrying out their work, in
particular concrete contexts, by enacting sets of generalisations whose application depends on
historically evolved collective understandings”.
For the purpose of this study, research is carried out in the field of organisational KM. According to
Nonaka [23] organisational knowledge can be described as the information flow among the various
resources within a company; it is the knowledge that is captured through IT systems, organisational
processes, products, company rules and corporate culture. Knowledge within an organisation can
include workers’ experiences and skills, which may be stored in the minds of the employees or could
be recorded in physical documentation, which is available for all employees to view and re-use. Myers
[9] adds that organisational knowledge is “processed information that is embedded into routines and
processes that enable action”. Research [138] [141] shows that organisational knowledge and the
management of that knowledge are critical to the success and competitive advantage of an
organisation [119] .
8.1 Types of Knowledge
In 1991, Nonaka [23] proposed that knowledge can be categorised into two types: explicit and tacit.
These terms have been widely acknowledged in published literature [[128] [142] [143] and,
therefore, knowledge may be considered to possess two distinct forms; ‘explicit’ or ‘codified’
knowledge, which may be found in tangible forms such as books, drawings, tapes etc., and ‘tacit’
knowledge which may be summarised as know-how, practical knowledge and skills [24] .
Explicit knowledge is recognised as information that can be codified, such as in facts or figures, or
which has been visually represented in physical documentation or in audiovisual files, such as videos
and images [119] . Tacit knowledge is information which is stored within the minds of individuals or
employees within organisations. Tacit knowledge is perceived to be particularly difficult to capture,
codify and share/communicate with other individuals; this is due to the fact that the knowledge
resides in the minds of individuals. On the other hand, explicit knowledge is recognised to be much
easier to communicate and share because it is already represented outside the mind of the individual.
Grant [144] stated that tacit knowledge can only be learnt through personal observation and
application and can only be turned into explicit knowledge through practice, while Lubit [145] added
that tacit knowledge is best captured through social methods rather than by IT systems. Some
researchers [146] [147] , in fact, conclude that knowledge cannot be represented by IT systems.
According to Teece [148] and Modi and Mabert [149] tacit knowledge can be transformed into
explicit knowledge through self-observation and organisational routine/practice. Blair [150] argues,
however, that tacit knowledge can only be stored in the minds of ‘practicing experts’ and not every
individual holds tacit knowledge – one must learn from explicit knowledge to become an expert, viz.
an individual with tacit knowledge. Day [151] on the other hand, argued that there should not be this
categorisation of knowledge and that tacit knowledge should be simply labelled as ‘knowledge’
because explicit knowledge is simply an individual’s sense of information.
It is believed [152] [153] that a high level of tacit knowledge within organisations results in difficulties
and challenges in efficient knowledge transfer/sharing. The reason for this is that tacit knowledge is
difficult to formalise and articulate. Knowledge is very dependent on context and is personalised and,
for these reasons, it is often believed that tacit knowledge is best learned through a collaborative
process [138] Research [154] shows that tacit knowledge is genetically inexpressible and highly
personal and, for this reason, Hislop [154] commented that tacit knowledge is best learnt through
personal communication between individuals.
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9 THE MANAGEMENT OF KNOWLEDGE
9.1 The Importance of Managing Knowledge
The management of employee and organisational knowledge is becoming increasingly important for
the survival of manufacturing organisations [155] Researchers [156] believe that, by sharing formal
organisational knowledge both internally and with partners in the supply chain, companies are now
able to increase performance and ultimately be successful in global marketplaces. According to
Nunamaker [119] , empirical research [155] [157] suggests that organisations that actively share and
make use of collective knowledge with partners in the supply chain become more productive and are
more likely to survive than those that withhold knowledge within their company.
According to Shin, Holden and Schmidt [158] , knowledge management can be broken down into
four distinct categories: knowledge creation, knowledge storage, knowledge sharing and knowledge
application (shown in Figure 13. ).
Figure 13. Knowledge Management Activities [158]
Holsapple and Singh [159] separated knowledge management into five primary activities and four
supporting tasks. They stated that the process of knowledge management includes Knowledge
Acquisition, Knowledge Selection, Knowledge Generation, Internalisation and Externalisation
underpinned by the key management skills of Leadership, Coordination, Control and Measurement.
Tseng [8] illustrates these five activities within their conceptual framework (see Figure 7) published in
2009, which shows clearly how the external knowledge of a company enters into the internal
knowledge management activities of that organisation; this in-turn allows the business to acquire new
knowledge and then internalise it constructively to improve the knowledge of its employees and
enhance processes and decision making.
Knowledge Creation Knowledge Storage
Knowledge Application Knowledge Sharing
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Figure 14. Tseng’s Conceptual Framework [8]
9.2 The Process of Managing Knowledge – Collection and
Sharing
Knowledge Collection refers to the identification of knowledge and the methods used to capture
such knowledge. He, Ghobadian and Gallear [160] explained that it is the process of accessing and
absorbing knowledge through direct or indirect contact or interaction with knowledge sources.
Grant [68] added that the ability of a company to successfully capture knowledge is relative to its
organisational performance.
The task of collecting knowledge can take place internally or externally to an organisation and,
therefore, it is vital to recognise the importance of companies working closely together and
maintaining links with partners in their supply chain to gather information and market
intelligence/knowledge to make informed decisions. Once collected, the knowledge must be securely
managed so that it is made available to all employees for re-use [8] [161] .
Nonaka and Takeuchi [139] agreed that the creation and capture of knowledge depends upon
mastering the process of transforming one form of knowledge into another. Grant [142] noted that
tacit knowledge is found within individuals and its capture, therefore, usually needs direct contact at
the individual level.
Once the knowledge has been captured through various methods, including bespoke KM systems, an
organisation must be able to validate the authenticity of the knowledge and present the knowledge to
workers in the company through different resources e.g. corporate intranet, regulations, whitepapers
etc. This act of knowledge sharing calls for the knowledge to be classified and collated in a form that
makes it easy for employees to find the relevant knowledge they are seeking [8]
In 2004, Yang summarised knowledge sharing as being the dissemination of information and
knowledge within a community. It is accepted by researchers [[117] [162] that the sharing of
knowledge is crucial for any knowledge management initiative within an organisation to be successful.
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Effective knowledge sharing in the workplace is observed to improve employee learning, which in
turn improves the agility of a company and increases the quality of products designed and
manufactured by an organisation [162]
Researchers [163] [164] commonly agree that the sharing or dissemination of knowledge relates to
the transfer of knowledge between a knowledge source and a knowledge recipient (i.e. the individual
who receives the knowledge) and improves their knowledge from it.
As already mentioned, many believe that the sharing of tacit knowledge is extremely difficult. This is
one reason why Knowledge Management Systems (KMS) have emerged as a key research and
development area and continues to grow. KMS are designed to support the creation, transfer and
application of knowledge within organisations [165] ; researchers and KM practitioners continually
strive to build systems that convert individuals’ tacit knowledge into explicit knowledge [123] . Hislop
[154] and Hendriks [163] , however, disagreed with this rapidly evolving research area as they both
share the view that tacit knowledge is best learnt through personal communication and, therefore,
cannot easily be expressed in written form or shared via a computer; they added that through
personal communication, the knowledge source or ‘giver’ expands on his or her own knowledge and
adds meaning.
The external knowledge of a company includes customer, supplier and competitor expertise.
Organisations which are able to tap into that knowledge base increase significantly their chances of
succeeding in competitive markets. However, there are numerous barriers to creating a culture for
sharing between these entities. Myers and Cheung [9] add that the flow of knowledge between
external partners makes a supply chain more transparent, which gives all partners a greater insight
into customer needs and value propositions, which in turn allows partners to gain market intelligence
for better planning of new product innovation.
Successful knowledge sharing within a supply chain requires openness and a willingness to share
between two parties. Trust inter alia is a key issue with regard to sharing knowledge with supply
chain partners. Liebowitz [117] described knowledge sharing within and between organisations as a
crucial element to the success of a business. Dyer and Singh [166] acknowledged that knowledge
sharing between partners in a supply chain could generate relational incomes for both parties,
although Simatupang and Sridharan [167] stated that more often than not, companies in a supply
chain do not like to share their private information completely.
9.3 Barriers to Knowledge Management
It is recognised [162] [168] [169] that the activities of knowledge capture and sharing face numerous
barriers typically relating to either social factors or the technology adopted or a combination of
both [118] . Lockwood [170] established that businesses often cannot identify what is known within
their organisations and, consequently, best practices, expertise and knowledge and skills cannot easily
be applied and transferred. Wilson [127] and Tsoukas [171] both suggested that the process of
capturing tacit knowledge and converting it into explicit knowledge cannot be achieved. Wilson [127]
did, however, argue that tacit knowledge can be demonstrated or performed and in turn can be
learnt by an observer.
Disterer [169] and Riege [162] both recognised that organisations themselves can contribute to a
failure to collect knowledge, stating that the physical layout of an office or the hierarchical structure
of a company may be counter-productive to knowledge capturing initiatives. Disterer [169] and
Ardichvili [168] added that individuals often perceive knowledge as power that can allow them to
advance within an organisation and, therefore, are not prepared to share. Riege [162] further added
that individuals often do not have the time to contribute knowledge to others, if they are working,
for example, on busy production or assembly lines.
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Trust is a key issue being researched in the field of knowledge sharing. Employees within
organisations often will not share their tacit knowledge for fear that other individuals will take credit
for the knowledge they have previously shared [162] . In this regard, Choi et al. [172] suggested that
trust and reward mechanisms are possibly more important than technical support in isolation for
encouraging knowledge sharing. Myers and Cheung [9] add that many organisations do not share
knowledge with external partners for fear of divulgence of confidential information, including
corporate technologies, pricing schedules, customer databases and processes.
Disterer [169] , Riege [162] and Ardichvili [168] all commented further on the language and cultural
barriers experienced within extended enterprises, stating that it is extremely difficult to manage and
share knowledge with overseas partners. In addition, a lack of consistent policies across businesses
often hinders the successful management of information systems [173] . Research suggests that
knowledge sharing within internal organisational teams is often “fraught with serious problems” [119]
[174] .
Organisations which collaborate and share knowledge with supplier partners are additionally
reported to face common risks. One of these risks is the sharing of proprietary information by the
supplier with its own different group of external partners. Knowledge which is shared between the
OEM and the supplier is susceptible to being passed outside the boundaries of the supply chain.
It has been acknowledged by Myers [9] that organisations which share similar philosophies and
corporate culture have a far greater inclination to share knowledge with each other. They further
added that organisations which have made investments in supply chain partners (e.g. financial,
equipment, facilities) are more open to knowledge sharing because they have an interest in the other
party developing their knowledge.
Various barriers to KM may be identified in relation to the technologies which assist in the capture
and distribution of knowledge. Riege [162] commented on the interoperability problems associated
with the introduction of new knowledge management systems into the existing IT infrastructure of
companies. Compatibility issues with current or legacy systems may also arise when trying to extract
previously published knowledge.
It has also been pointed out [175] that some organisations do not offer sufficient training in
connection with the introduction of new KMS; this causes difficulties due to many employees being
unfamiliar with the new system installed. Additionally, companies often do not explain the benefits of
new IT systems to its employees and, consequently, users are not naturally inclined to use them. By
not planning for the introduction of a new system, organisations could find that employees do not
adopt it or contribute to it.
Bakker et al. [176] expanded on the issue of knowledge management systems within product
development, stating that other barriers may exist within global project teams. These barriers
included the linking of product developing activities with the functionality offered by the KM systems.
Additionally, employees often become frustrated if the systems do not allow them to communicate
or collaborate across boundaries. Finally, employees have also noted that different teams, even within
the same organisation, may use contrasting vocabularies or technical jargon [177] [178] .
Cultural and language barriers have also been noted [179] [181] as a key barrier to the success of
knowledge management practices within global organisations. Desouza and Evaristo [182] added to
the discussion on the cultural impact on knowledge management by contrasting the difference in
cultures between North America, Western Europe, Japan and Spain. They argued that Spanish and
Japanese employees are far more open to the sharing of knowledge, whereas employees in North
America and Western Europe were more reserved when it came to sharing their tacit knowledge
and wanted confirmation that the knowledge would be controlled and not be misused.
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Finally, a key concern facing organisations with regard to knowledge management is the matter of
employees leaving the business. Preiss [183] recognised that important tacit knowledge will be lost if
an organisation fails to capture it before an employee leaves. Similarly, if an employee stores work
locally on a personal hard drive rather than in shared folders or cloud-supported networks, where
others have been granted access, then explicit knowledge already stored may not be captured and
made available to others, including new employees.
10 KNOWLEDGE MANAGEMENT IN SUPPLY CHAINS
In 2011, Samuel et al. [184] recognised that the research fields of supply chain management and
knowledge management were two distinct research topics and explorative study into the
combination of both fields was limited. However, they did add that these two separate areas had
both received considerable research attention over the past few years.
With regard to the research area of KM within supply chains, Samuel et al. [184] acknowledged that
researchers typically focused on one of two streams: some explore the different types of knowledge
learning which might take place in a supply chain and others focus more specifically on the techniques
employed to manage knowledge with a particular focus on supply chain management.
Despite it being recognised [185] [187] that knowledge sharing is a key to success in extended
enterprises and a key contributor to enhanced competitiveness, few empirical studies exist that
demonstrate how knowledge is actually captured and shared in global supply chain networks.
Furthermore, little research has been carried out in establishing how tacit knowledge is transformed
into explicit knowledge within extended supply chains [184] . Instead, published research into
knowledge management within supply chains typically concentrates on exploring the impact of key
attributes of a supply chain partnership, including organisational trust, commitment and shared
meaning [149] [188] [190] .
Kim et al. [191] and Cheung and Fu [192] noted that in the commercial world, research into
knowledge exchange has become increasingly important for organisations. However, they further
acknowledged that few companies have successfully exploited the knowledge that their supply chain
partners possess. This is an important challenge as Walter et al. [193] explained that potentially
hidden knowledge and best practice most likely resides within supply chain networks. Mabert and
Venkataramanan [194] stated that the sharing of knowledge in supply chains should help improve the
overall performance of the members involved. However, research [195] suggested that it may be a
long time before companies sufficiently evolve to become fully collaborative in end-to-end supply
chain knowledge sharing.
Research [196] [199] has shown that successful new product development relies heavily on
customers and suppliers collaborating more closely together, sharing information and knowledge
among their independent product development teams. It is also acknowledged [[191] [200] that the
closer the relationship between supply chain partners, the more successful the knowledge exchange
becomes. Research [198] [201] [202] adds that involving the supplier from the outset will result in a
faster development process for each party. However, there still appears to be a lack of published
literature which examines how to integrate third party suppliers into a product development process
and how to work more productively together with supply chain partners.
Companies in various industries, including automotive, aerospace and chemical, have acknowledged
the benefits of knowledge sharing in supply chains and have consequently established initiatives to
better facilitate the sharing of knowledge. For example, the European chemical industry estimates
savings of 2% of total industry sales through increased collaboration, including knowledge sharing,
between supply chain partners [9] . Finally, it has been noted [9] that common supply chain failures,
including incorrect stock levels, excess inventories, new product failure rates, wasted time in
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engineering and R&D, could all be improved through the better sharing of knowledge between supply
chain partners.
According to Peterson, Handfield and Ragatz [203] , common issues which arise from integrating
suppliers into product development activities, include:
Tier structure;
Level of responsibility for design;
Setting of responsibilities in the requirement setting process;
Not knowing when to involve suppliers in the process;
Inter-company communication methods;
Intellectual property rights; and
Alignment of organisational objectives [204] [206] .
Rivikin [207] , Weill and Vitale [208] added that even if knowledge exchange is adopted by companies
within supply chains, there may still be obstacles which prevent the exchange, such as corporate
search engine capabilities and the compatibility of partner companies IT infrastructures, which may
use different tools, including different PLM systems.
Park and Ungson [209] noted that the opportunistic characteristics of a company may jeopardise
supply chain relationships, if one company gains more knowledge than the other in a shorter amount
of time; this suggests that for knowledge sharing in supply chains to be successful, both companies
must experience mutual gain. Kim et al. [191] recognised that if one supply chain partner gains more
knowledge too quickly, then the other may start to withhold knowledge and this will reduce the
overall effectiveness of knowledge exchange in both companies, which will in the end defeat the
overall goal of becoming more collaborative in knowledge sharing. Hsiao, Tsai and Lee [210] agreed,
stating that even if a company chooses to partake in supply chain knowledge exchange, they may still
be unwilling to share certain valuable information.
According to Coates [211] , Kim et al. [191] and Samuel et al. [184] , by creating a knowledge
management network whereby all entities within a supply chain can collectively share internal
knowledge within external partners, companies can survive and develop their own skills and
knowledge [119] . Furthermore, businesses can benefit from the knowledge residing in other parts of
the company and across the whole supply chain, including in other partner companies [188] [212] .
Nowadays, organisations must make full use of the knowledge that they possess in their employees
to compete and thrive in the global marketplace.
Chakrabarti and Hauschild [213] noted that involving suppliers from the outset of new product
development projects will add information and expertise regarding new ideas, methods and
technologies, which will help solve and identify problems. The dynamics of the supply chain will also
improve, allowing for a better working relationship [214] . Finally, it is worth noting that involving
suppliers in the product lifecycle will improve communication channels between the supplier and
customer and reduce any possible delays [202] [215] . However, further research [202] [216]
suggests that both parties must establish precisely what knowledge is to be shared in order to allow
both parties to jointly benefit from the collaboration.
According to Wasti and Liker [217] cited in Peterson, Handfield and Ragatz [203] , there are three
critical conditions which organisations must consider before engaging in collaborative efforts with
suppliers: (1) the extent to which the supplier influences decision-making in the organisation; (2) the
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amount of control the buyer retains over the design of products; and (3) the frequency of design-
related communication.
Samuel et al. [184] recognised that the information and knowledge stored within supply chain
partners is disparate and there is a need to develop knowledge-based tools, which capture and store
collective knowledge and make it available to the extended enterprise [218] . Goh [219] concurred
that knowledge within organisations adds value to its products, processes and employees and that
new KM-based tools will help assist businesses in exploring, innovating, disseminating and automating
corporate knowledge [184] .
Samuel et al. [110] further commented that current research into supply chain management is
particularly focussed on the structural issues of the supply chain partners, including governance
structures and the structure of the corporate network. Gammelgard [220] introduced the theory
that knowledge management and learning from others in the supply chain can introduce new
innovation to all supply chain partners and stated that the collective knowledge and experience of all
members may be the most significant source of value creation.
Pillai and Min [26] stated that knowledge regarding a participant within a company’s supply chain can
be called ‘relationship knowledge’. Relationship knowledge includes explicit knowledge on end
customers, suppliers, distributors and channel intermediaries; an organisation which possesses such
knowledge can make informed decisions on which company to work with, which company has the
necessary resources to complete a job and can also answer cultural questions, such as ‘does the
company have a knowledge sharing culture’... therefore, should we work with that company?
Mentzer et al. [221] introduced a second term called ‘Demand Knowledge’, which relates to
knowledge regarding specific supply chain activities, such as: sourcing of parts from suppliers;
production of components or complete products; marketing of finished products; and transportation
of products or services.
Cokins [222] cited in Pillai and Min [26] defined one final term called ‘cost knowledge’, which relates
to a companies’ knowledge on costs, including labour, materials; inventory; and distribution. An
informed knowledge of costs enables organisations to better negotiate deals with other members in
their supply chains.
Finally, research by Barabasi [223] and Scott [224] confirmed that organisations can now use social
network identification technologies to analyse internal and external collaborations within a supply
chain; this allows organisations to gain a greater understanding of supply chain members. Newman
[225] , Wagner and Leydesdorff [226] further proposed methods for illustrating collaboration
networks within supply chains.
11 SUPPLY CHAIN MANAGEMENT
11.1 Background
Supply Chain Management may be defined as the practice of managing the whole process of activities
of supply networks from suppliers through manufacturers, retailers/wholesalers to customers and
end users [227] . SCM may also be seen as helping organizations to manage the flow of information,
money and products beyond the physical boundaries of the organization [122] .
First coined in the early 1980’s, the term ‘Supply Chain Management’ did not become commonly
used until later in the 1990’s when ERP systems had been increasingly employed within organisations.
The concept of SCM expanded from the basic practice of controlling material flows to the wider-
ranging activities of financial, material, process and technical management [228] [230] .
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The goal of SCM may be considered to be an effective flow of goods, services and information
between extended enterprise partners, while the span of functional activities extends from demand
creation through to ultimate order fulfilment and beyond. Mabert and Venkataramanan [194]
described a Supply Chain as a “network of facilities and activities that performs the functions of
product development, procurement of material from suppliers, the movement of materials between
facilities, the manufacturing of products, the distribution of finished goods to customers and
aftermarket support for sustainment”.
The importance of SCM grew out of the reported successes arising from implementation in major
organisations such as Wal-Mart, Proctor & Gamble, Dell, Hewlett-Packard and Toyota in Japan,
where research suggested that the involvement of suppliers in NPD processes contributed to the
‘Japanese advantage’ [201] [231] [232] .
Ragatz, Handfield & Scannell [233] , when reporting on the involvement of suppliers in NPD, stated
that Open Innovation (“the use of external as well as internal ideas”), recognised that good ideas are
not exclusive to single firms. More broadly, they stated that “there is intuitive appeal to the idea that
using the knowledge and expertise of suppliers to complement internal capabilities can help reduce
concept-to-customer cycle time, costs, quality problems, and improve the overall design effort.” The
work of cross-functional teams was increasingly becoming established across corporate boundaries.
Furthermore, Gassmann et al. [234] identified that the working together of customers, suppliers,
universities and others was changing the buyer-supplier relationship and suggested that, more
recently, there was an extension of open innovation into low-tech industries, into SME’s and from
products into services [175] .
SCM continues to evolve in a significant manner as companies seek to respond to the more
demanding conditions of globalised markets. It attracts significant research interest and there is
greater focus on the management of information and knowledge within the context of SCM.
11.2 The importance of SCM
The experience of Japanese motor vehicle manufacturers was an early indicator of the importance of
SCM. In the context of product development, studies revealed that new Japanese vehicles were
developed and launched more quickly, possessed more innovative features and at a lower cost in
terms of development manpower and time. Clark [201] suggested that this implied that the additional
knowledge and skills of suppliers could enhance efficiency, increase output and ultimately deliver
better products [235] .
Especially in the area of product development, the potential on offer via the integration of suppliers
into enterprise practices is seen as compelling. Ragatz, Handfield and Scannell [233] identified clear
benefits relating to purchased material cost, quality and development time. Furthermore, their
research also suggested benefits in terms of access to and the application of technology which
resulted in improved decision-making and designs. Thomas [175] , while identifying how suppliers’
expertise could complement internal capabilities, also emphasised the importance of customer
integration into NPD activities to improve customer satisfaction and increase sales.
Traditionally, SCM has been seen as particularly significant for NPD and corporate decision making,
but increasingly its value in terms of knowledge management is also recognised. Effective SCM has
been reported to improve collaboration between customers and suppliers; in turn, this can allow for
the creation and sharing of new knowledge and ultimately enhanced learning [[236] [237] Similarly, a
knowledge development culture has been shown to enhance the performance of supply chains [238] ;
facilitated knowledge transfer between partners can result in enhanced supply chain flexibility [239] .
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The combination of effective and integrated SCM and PLM systems may be considered as two
fundamental elements for corporate success and enhanced knowledge management deriving from
their employment offers the potential to deliver considerable enterprise competitive advantage.
As in the case of PLM, Supply Chain Management has received considerable and wide-ranging
academic attention. Research has focussed on a number of areas and these have included:
Information flow within supply chains, particularly in relation to demand and its impact on
material requirements [230] ;
Learning in supply chains [240] ;
Issues relating to the integration of partners in supply chains and the effect on performance [241]
cited in [184] ;
Collaboration within supply chains and the issues which influence good practice [242] ;
The advantages and benefits resulting from collaboration in supply chains [243] ;
Factors which impede effective supply chain collaboration, including organisational, operational
and behavioural issues [[244] [245] ;
Collaborative Planning, Forecasting and Replenishment (CPFR), a web-based concept to co-
ordinate activities between supply chain partners [244] ;
The use of technology, particularly relating to information exchange, in supply chains [246] ;
The integration of both suppliers and customers into the NPD process [233] [247] ;
Open innovation [248] ; and
Knowledge management.
As the integration of suppliers and customers continues and more open innovation is pursued, the
issue of knowledge management within supply chains has increasingly become an important area for
research; the following represent a selection of research publications relating to various aspects of
knowledge management:
Strategic decision making in supply chains was investigated by Raisinghani and Meade [249] who
developed a model to support knowledge management.
The sharing of knowledge within supply chains was considered by Huang and Lin [250] and Douligeris
and Tilipakis [251] , who identified the potential of semantic web techniques.
The use of IT-based solutions to support the management of knowledge in supply chains was also a
focus of research by Corso et al. [252] .
The capturing and sharing of knowledge, particularly in relation to SME’s working in supply chains,
was researched by Fletcher and Polychronakis [253] , who proposed a framework to enhance the
sharing of knowledge and skills.
The problems experienced specifically in global supply networks were explored by Myers and
Cheung [9] , who highlighted that cross-cultural differences could introduce a further factor to inhibit
knowledge sharing.
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Hult, Ketchen and Slater [190] studied the performance of supply chains and considered how
outcomes are influenced by knowledge development processes. They highlighted the importance of
knowledge acquisition and retention in supply chain processes.
Halley et al. [254] explored the effective integration of supply chain management and knowledge
management, while much research has been conducted into the social aspects of knowledge
exchange, where the key factors of trust, co-operation and effective communication have been
identified as critical for knowledge creation, sharing and learning [255] .
Finally, the vital relationship between supply chain and knowledge management is made apparent
through the recurring use in the literature of terms such as ‘knowledge supply chain’ [256] and
‘knowledge supply networks’ [258] which emphasises the importance of understanding the process
of knowledge exchange between stakeholders in extended enterprises.
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CHAP V. Maintenance
12 INTRODUCTION
12.1 The Requirement for Maintenance Process Planning
There are a number of researchers whom explore maintenance issues in manufacturing systems. For
example, some academics research focuses on fault diagnosis methodology of solder-joint on prevent
faults and the reduction of maintenance costs (Liu et al., 2014). Geramifard et al. (2013) proposed an
approach, based on hidden Markov model, to improve the fault detection and diagnosis accuracy on
synchronous motor faults, which is useful for fault prediction. Boutros and Liang (2011) improved
fault detection, diagnosis speed and accuracy of bearing and cutting tools, while some are researching
the maintenance planning or scheduling between different machines or with production together
(Rivera-Gómez et al., 2013, Elyas et al., 2013, Go et al., 2013, Celen and Djurdjanovic, 2012,
Nourelfath and Châtelet, 2012).
A simulation method on manpower resource utilization to improve maintenance performance has
been studied by Datta et al. (2013). The impact of spare parts inventory on making maintenance and
replacement decisions has been studied by Nguyen et al. (2013), while Tran and Yang (2012)
developed a condition-based maintenance management system for rotary machining. Others have
focused on the selection of maintenance policies, for example, through modelling the impact of
different maintenance policies on machine failures, and maintenance cost and maintenance actions
(preventive maintenance, minimal repair and overhaul) can be selected accordingly (Liu et al., 2013,
Chang and Lo, 2011); the preventive maintenance policy is applied according to the inspected failure
stage of a process (Wang et al., 2014); the determination of preventive maintenance intervals (Xia et
al., 2012); future failures and remaining useful life (RUL) is calculated so that predictive maintenance
can be achieved (Voisin et al., 2010).
Kazaz and Sloan (2013) researched the impact of process deterioration on production and
maintenance policies, while Zitrou et al. (2013) explored the parameter uncertainty on maintenance
costs to help decision makers select the most affective maintenance policies. Njike et al. (2011) dealt
with the maintenance and production planning issue considering the quality of products
manufactured. Salonen and Deleryd (2011) researched the cost of poor maintenance so to improve
maintenance performance through establishing the viewpoint to identify deficiencies in maintenance
performance.
Research relating to maintenance management has also been conducted in aerospace and
aeronautics, which is known as Maintenance, Repair and Overhaul (MRO). Zhu et al. (2012)
developed a Product Service System (PSS) framework to support MRO services; Papakostas et al.
(2010) introduced a short-term maintenance planning methodology for airline operators. The
relationship between failure mechanisms and outsourced maintenance functions has been studied,
indicating some serious failures which are associated with maintenance outsourcing functions
(Quinlan et al., 2013). Kiridena (2012) developed a framework that plans and schedules aircraft
maintenance tasks. The challenges and opportunities for the repair work of aircraft composite
materials have been studied by Katnam et al. (2013). Datta et al. (2013) dealt with manpower
resource allocation strategy determination for the aircraft maintenance line, while Reményi and
Staudacher (2014) identified and implemented a scheduling rule on aircraft engine maintenance work.
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Within all maintenance related research, most are focused on the supportive input or the output
phase of maintenance operations, as can be seen in Fig.1, however, if we divide the whole
maintenance phase into three separate stages: before maintenance action, during maintenance action,
and after maintenance action. In the first phase, aspects that determine how to select maintenance
policy, how to schedule preventive maintenance have been researched in the literature, such as the
identification and diagnosis modelling, maintenance scheduling or planning, failure prediction or
resource scheduling, modelling method and approaches support this part; while after maintenance
actions, performance should be evaluated, so the key performance indicators such as cost, efficiency,
possibility, robustness. During maintenance action, knowledge-based methods have been developed
for maintenance solution generation, for example – (Potes Ruiz et al., 2013, Potes Ruiz et al., 2014,
Guo et al., 2013) developed a framework to generate new knowledge from past experiences to
improve maintenance activity decision making, but the maintenance solution is limited to the action
level, for example, in (Potes Ruiz et al., 2014), overhead crane’s failure mode has been researched,
through the analysis of removal control’s fault, ‘Urgent corrective’ type of maintenance is
recommended and the maintenance action of ‘replacement of the defective part’ is the final solution
that was generated; in (Potes Ruiz et al., 2013) complex machine’s failure mode and its solutions are
studied, similarly, the maintenance actions such as ‘Actuator replacement’, ‘Vibration Analysis’ or
‘Filter replacement’ are generated knowledge; Guo et al. (2013) developed a knowledge integration
framework to support maintenance decision making, but did not provide the process of how to make
decisions. The importance of maintenance procedure has been recognized, but few works are related
to the generation and optimisation of maintenance workflows (Ren et al., 2011). So, in the middle
phase of the whole maintenance process, actual maintenance procedure guidance is missing.
According to the definition, maintenance is a process that contains activities, resources and
techniques that retains a component in or restore it to the required function (Söderholm et al.,
2007). Thus it is the activities/procedures/steps included in the maintenance task that ensure the
machine’s performance. However, limited literature is focused on maintenance process making.
Figure 15. The overview of the current research and the gap in maintenance field
Maintenance can be divided into corrective, preventive or predictive maintenance, according to the
maintenance time and failure time (if before failure, preventive maintenance, if failure can be
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predicted, predictive maintenance is conducted to prevent the occurrence of failures; if failure
occurs, then corrective maintenance or repair is done to fix the problems) (Potes Ruiz et al., 2014).
No matter which type of maintenance, they have an operation process that achieves the maintenance
tasks, for example, Dai and Zhang (2009) made the process for maintaining the torque converter slip,
as shown in Fig.2.
Figure 16. The process for maintaining the torque converter slip
It can be seen, therefore, that this is an urgent issue to deal with in maintenance process planning –
to make the workflow including maintenance procedures, steps, together with techniques needed,
fundamentals, resources, manpower, so that to improve maintenance accuracy and efficiency and
reduce maintenance error and cost.
13 MAINTENANCE PROCESS MANAGEMENT
The maintenance process starts from the preparation for maintenance action and ends with product
testing and certification; the process for maintenance is as follows:
Phase 1: Disassembly and cleaning
Phase 2: Inspection and spare parts provision
Phase 3: Repair
Phase 4: Reassembly
Phase 5: Test and certification
This process is quite different to the maintenance processes described by Söderholm et al. (2007);
this is an overview of the whole in order to carry out maintenance and feedback, maintenance
planning phase is similar with this papers maintenance process planning, however, they described the
input and output of maintenance planning, but they did not provide information on how to generate
the execution process for engineers; Ramudhin et al. (2008) divided the maintenance process into 12
phases, whereby the process starts from customers sending products back to the service centre and
ends with the products sent back to the customers; in comparison, Reményi and Staudacher (2014)
described three cycles of maintenance process flow, starting from the disassembly of the product;
the commonality of the above research is that they did not provide the methodology to make the
process clear from the execution by engineers.
Remove the torque converter from transmission
Check the torque converter Disassemble the torque
converter
Wash the components inside of the converter
Check the lock-up clutch Replace the oil seal of the torque
converter
assemble the torque converter End of maintenance
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Process planning is first applied to the manufacturing process converting the design concept into a
manufactured product and the output of manufacturing process planning elaborates the machines,
setups, manufacturing operations, operation sequence, estimation of operation time, resources
required and the correct tools to manufacture a part based on an engineering drawing or a
Computer Aided Design (CAD) model (Denkena et al., 2007). Similarly, the output of the
maintenance process planning phase is the maintenance plan; this determines maintenance
operations, procedures, resources needed, time scale required etc. More importantly, the
manufacturing process plan is completed based on the parts design model, so the maintenance
process should also be made according to the machine’s product model. Other information that
supports maintenance process planning includes: information of current machine health condition
from performance monitoring phase, maintenance requirements based on stakeholders such as
maintenance aims, objectives, strategies and policies, and information of available tooling and parts
(Söderholm et al., 2007). Besides, the maintainability and the availability of the machine should be
taken into account. Furthermore, lessons learnt and experience feedback from previous maintenance
process planning and previous execution results should also be considered in the next planning.
Computer Aided Process Planning (CAPP), as a method for automating manual process planning, can
improve efficiency of planning; variant and generative CAPP are two basic approaches. In variant
CAPP, the parts have similar geometric characteristics and are grouped to have similar processes.
Each part is coded and classified into the group. Each group has a typical part for which the typical
process plan document is made and stored in the database. Every time a part’s manufacturing process
needs to be planned, the typical process plan of the typical part in the same group will be retrieved
and referenced, based on which a new process can be planned by engineers. Generative CAPP, on
the other hand, is created based on decision making logic and algorithms, as well as the part model
and manufacturing information. Other types of CAPP are derived from these two basic types, such as
the comprehensive CAPP, which is the combination of variant CAPP and generative CAPP and
intelligent CAPP, which is based on decision making and previous knowledge. In variant CAPP, a lot
of similar parts can be planned once a standard plan has been made, but the process made is limited
to the previous planned parts and process planning experts are required to modify the standard plan
according to different manufacturing environment. Generative CAPP can create process plans
automatically and quickly without referring to previous parts, but with the various and complex
manufacturing environments, decision making logic should be customized for the specific
manufacturing shop (Culler and Burd, 2007).
After maintenance plans have been created, engineers with relevant knowledge could conduct
maintenance operations based on the maintenance plan which detailed where, when and how to
maintain the machine. Besides, other supportive resources, such as tooling and materials,
experienced personnel, and maintenance documentation are also important inputs (Söderholm et al.,
2007). The results of this phase can also be input into the performance examination phase and
process planning phase to guide the monitoring scheme or modify/upgrade the next process planning.
14 MAINTENANCE KNOWLEDGE REPRESENTATION
There are three basic types of Knowledge Management (KM) and representation methods
developed, which are frame-based systems, Semantic networks, and Description Logics (DLs) &
Conceptual Graphs (CGs). Frame-based systems, such as Expert Systems, experience based systems,
lessons learnt systems, or best practice systems use concept to represent objects with common
properties, it is lack of formal semantics; in semantic networks, such as ontology based management
systems, knowledge is represented as graphical concepts and in semantic relations, but there is a lack
of logic theory which is not beneficial for decision making afterwards; while DLs and CGs integrate
the advantage of both semantic networks and reasoning logics (Potes Ruiz et al., 2014, Potes Ruiz et
al., 2013).
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Conceptual graphs, based on semantic networks and Peirce’s existential graphs, combine the visual
advantage of graphical languages and the capability of logic. The advantages that it has are 1) the
graphical representation of CGs can represent the nested information and context that are difficult
to represent in linear notations; 2) the Fuzzy Conceptual Graphs can represent the fuzzy knowledge
which does not belong to the sets of “to be or not to be”; 3) because of the combination with Fuzzy
Logic, the logic foundation provides a basis, not only for reasoning processes performed, but also for
justifying the soundness and the completeness of a reasoning procedure (Cao, 2010).
A basic conceptual graph is a bipartite graph which has two disjointed sets: the concept set and the
relation set, where they are connected by edges. Each concept set includes a pair of a concept type
and a concept referent representing the type and instance of an entity, which can be drawn as a
rectangle (the ‘*’ denotes an undefined instance). Each relation set is a relation type, representing a
relation between the two entities represented by the concept sets connected to it, which can be
drawn as an oval. Each edge is labelled by a positive integer and sometimes just for readability, drawn
as an arrow. Fig.3 shows an example for the relationship between machine, failure and solutions
graph by CGs. In order to simplify the graph, the number on the edge isn’t shown. The machine has
several components, which attribute failure mode, when there is another textual format of the
conceptual graph that representing the concept set and relation set in square bracket and round
bracket.
[machine:*] (has) [Component:*]
Figure 17. An example of relationship between machine, failure and solutions graph by CGs
15 MAINTENANCE PROCESS MODELLING
Before choosing the modelling language for the maintenance process, there is a necessity to specify
the characteristics of the requirements for making the maintenance process. Due to the introduction
of maintenance process in the previous section, feedback is required for knowledge sharing and re-
use, so the process modelling language should include a feedback function and be easy to identify the
relationship between the links. Besides, product information is involved in maintenance, which
requires a product data model to be assigned to the maintenance process model, then the modelling
of maintenance sequences becomes more important as there are different stages of decision making
and many people are involved in the process.
In the following sub-sections, a review of several modelling languages is carried out and the selection
of the language should meet the above characteristics.
15.1 IDEFØ
IDEF refers to a family of modelling methods that form functional modelling to data, simulation,
process description, object-oriented design inter alia (Mayer et al., 1992). Eventually, IDEF methods
have been defined up to IDEF14.
“IDEFØ is a method designed to model the decisions, actions, and activities of an organization or
system. IDEFØ was derived from a well-established graphical language, the Structured Analysis and
Design Technique (SADT). The United States Air Force commissioned the developers of SADT to
develop a function modelling method for analysing and communicating the functional perspective of a
system. Effective IDEFØ models help to organize the analysis of a system and to promote good
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communication between the analyst and the customer. IDEFØ is useful in establishing the scope of an
analysis, especially for functional analysis.
As a communication tool, IDEFØ enhances domain expert involvement and consensus decision-
making through simplified graphical devices. As an analysis tool, IDEFØ assists the modeller in
identifying what functions are performed, what is needed to perform those functions, what the
current system does right and what the current system does wrong. Thus, IDEFØ models are often
created as one of the first tasks of a system development effort” (Knowledge Based Systems).
IDEFØ is seen as a valuable tool to capture functional relationships in processes and the knowledge
requirement for each function, which makes it a good modelling choice for the initial stages. An IDEF
model is composed of arrowed lines and boxes to show functions and represent activities. The basic
syntax of an IDEFØ diagram can be seen in Fig. 4.
Figure 18. The basic syntax of IDEF IDEFØ diagram
Within the IDEFØ diagram, data or task(s) is the input to this function, then output data or another
task(s), from the top it is the measures to control the output, then the bottom inputs the
mechanisms – means to carry out the tasks. An IDEFØ diagram is a hierarchically structured set of
nodes. At the top level there is a single context node (as is seen the top diagram in Fig. 5) to
describe the overall purpose and viewpoint. The context node may have one or up to six child
nodes. Each child node may be described in more detail, as one or up to six nodes. Each of the
arrow lines can link to a child node in the same diagram or to an external node, by reference to the
edge of the node diagram and an associated node ID (Baxter, 2007).
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Figure 19. Decomposition Structure of IDEFØ
This hierarchical model is useful for being able to describe a high level view of the process and a
detailed function view. However, one of the limitations of IDEFØ is that the maximum number of
child nodes in any parent node is up to 6, which limits the representation of a process into six nodes.
Although multiple nodes can be used to describe the process, this on the other hand leads to
additional complexity and reduces the interpretability of a diagram. Another limitation is when
representing feedback loops between different nodes multiple internal links will cause problems in
understanding the diagram. Besides, IDEFØ shows interactions between functions, but it does not
show task ordering. Thus IDEFØ is not suitable for this project.
IDEF3 is a Process Description Capture Method for collecting and documenting processes. It
represents temporal information; precedence and causality relationships. IDEF3 is well suited to
describing enterprise processes, including manufacturing operations. However, the inclusion of
product data in the process is a key element of the proposed methodology, since it represents the
integration between product knowledge and maintenance knowledge. The maintenance operations
and procedures are closely related to the product data: different parts have different maintenance
policies, thus IDEF3 is not suitable for the project neither.
15.2 UML and SysML
UML is an acronym of Unified Modelling Language developed by Object Management Group (OMG)
and it is a Worldwide Industry-standard graphical modelling language for representing software
systems built using the Object-Oriented (OO) style (1997a). UML offers a standardised means for
visualising a system’s architectural blue prints, and the modelling language combines techniques from
data modelling, business modelling, object modelling and component modelling (Fowler, 2003).
Three elements are included in UML: actors, activities and business processes (Ambler, 2005). Actors
specify a role which is acted by a user or any other system that interacts with the subject, which may
represent roles played by human users, external hardware, or other subjects. Activities: Activity is a
major task that must take place in order to fulfill an operation contract. It represents a step in a
business process or an entire business process. And business processes: it is a collection of related,
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structured activities or tasks that produce a specific product or service for a particular customer or
set of customers.
UML has several advantages, for example, it provides significant and detailed notations and principles
for any business sectors (Debbabi et al., 2010, Zhang, 2011), i.e. a use case diagrams describes the
functional behaviour between users and the cases, a class diagram describes the static relationship
between different classes: objects, attributes and associations; a sequence diagram describe the
dynamic behaviour between different objects. Therefore, UML can manage current manufacturing
industries’ most modelling requirements from various viewpoints. Besides, since it is developed as a
standard modelling language, it can be recognised by both researchers and organisations.
Furthermore, it is also commercially supported by many tools and textbooks.
However, UML still has some disadvantages (Zhang, 2011), UML is not capable for sequential
development between systems, and it just has capability for simple sequence representation. It is not
satisfied for modelling business process, especially for complex and hierarchical processes. More
importantly, UML does not contain any feedback loop for modelling the business process, thus it is
not appropriate in this project.
SysML is a graphical modelling language developed in response to the UML for Systems Engineering
Request for Proposal (RFP). The language is used for specifying, analysing, designing, verifying and
validating systems, including software, hardware, procedures and facilities (Friedenthal et al., 2008).
SysML has defined nine basic diagrams which can be divided into four categories: Structure Diagram
(including Class Diagram and Assembly Diagram), Parameter Diagram, Requirement Diagram and
Behaviour Diagram (including Activity Diagram, Sequence Diagram, Timing Diagram, State Machine
Diagram and Use Case Diagram) to represent different aspects. Similarly with UML, it does not
contain feedback loop for business processes, and in the modelling of sequence diagram, it cannot
include product information into it. Thus both SysML and UML are not appropriate to this project.
15.3 Product Lifecycle Management
Product Lifecycle Management (PLM) is a process of managing the whole lifecycle of a product
consisting of its conception, design, manufacture, service and maintenance (CIMData, 2015).
According to CIMData, PLM is a strategy business approach rather than a technology, in which
processes are as important or even more important than data. PLM systems integrate people, data,
processes and business systems and provide product information backbone for companies and their
extended enterprise (2008). PLM tasks include the creation, mortification and exchange of product
information (Srinivasan, 2008).
15.3.1 Mega Suite
The implementation of PLM is lack of holistic and generic including business process definition and
product information models, while different tools are used by different researchers to model
business processes (Marchetta et al., 2011), among which Maga Suite tools support much in process
modelling. The MEGA Suite provides repository-based modelling tools to support projects ranging
from process analysis to risk and control mapping to application analysis and design.
MEGA Suite consists of three main products: MEGA Process, MEGA Architecture and MEGA
Designer. MEGA Process is designed for business analysts for defining, documenting and improving
business processes, organizational structures, procedures and tasks, and for understanding their
interdependencies. It is specific for general Business Users. MEGA Architecture is designed for IT
architects. It is usually used to describe how IT operations support the associated business functions.
Together, MEGA Process and MEGA Architecture deliver to the MEGA Repository a comprehensive
view and record of how business and IT work together which is for technical users. MEGA Designer
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is designed for project managers and software architects. It provides complements for the MEGA
Process and MEGA Architecture tools with a UML-based modelling environment that supports
service oriented architecture, database architecture, and object component-based design.
Mega has been used for the creation of a reference framework model, for Product Lifecycle
Management. It has been used for the representation of an integrated business process model that
comprises several processes carried out during product’s lifecycle, including the development of new
products, the production and logistics processes. In the following model, called Business Process
Environment Diagram, there are a representation of external actor such as Customer, Supplier and
Partner enterprise and a representation of business processes such as New Product Development,
Order Delivery, Production Planning, Procurement and Manufacturing and Customer service
Management. (Marchetta et al., 2011)
Figure 20. Example of a Business Process Environment Diagram (Marchetta et al., 2011)
The advantages of MEGA suite for process modelling are that, in MEGA, processes and activities can
be decomposed without any limit. Besides simple decomposition (i.e., sub-processes), MEGA Process
provides the ability to connect process functional analysis to process organizational analysis
(procedures). In addition, advanced control flow is provided for activity flows, including decision
boxes. At the same time MEGA supports ABC, the ability to add activity-based costs to business
processes and MEGA Process features a Discrete Event simulation engine that provides a number of
simulation capabilities. Defining Activities BPMN is supported in MEGA Designer for designing IT
processes, as well as the compatibility with BPEL language. While on the other hand, MEGA does not
support UML 2.0 Activity diagrams. And MEGA tools are Microsoft centric. They all utilize the same
MEGA Modelling Desktop – a standard Windows-based application adhering to the MS Office
paradigam (1994). From the Fig.6, since there will be several input(s) and output(s) of a function, the
sequence of the activities are not very obvious, especially for complex processes, it will make the
figure difficult to read. The other important disadvantage is that, it is a commercial process modelling
tool that the company provides training and consulting, which makes the tool not appropriate for
researchers.
15.4 BPMN
The Business Process Modelling Notation (BPMN), was developed by an industrial union and
provides a standard notation for modelling business processes, which can support business analysts
and system designers (Recker, 2010). BPMN defines a Business Process Diagram (BPD) which creates
graphical models of business process operations based on a flow-charting technique (White, 2004). In
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the BPD, a variety of graphical elements are used to represent activities/events/data throughout the
process.
BPMN notations provide four categories of graphical elements according to which the BPD users can
understand the process easily. The four categories are: Flow Objects, Connecting Objects,
Swimlanes, and Artifacts (White, 2004). Flow Objects include three core elements: Event
(represented by a circle, is something that happens during the process), Activity (represented by a
rounded-corner rectangle and is a generic terminology of work that operates within the process),
and Gateway (represented by a diamond shape, and controls the divergence and convergence
encountered in the process). Flow Objects in the diagram are connected by Connecting Objects to
generate the basic structure of the business process. Connectors can be divided into Sequence Flow
(represented by a solid line with a solid arrowhead, and used to show the activity orders), Message
Flow (represented by a dashed line with an open arrowhead, and used to show the message flow
between two separate process participant), and Association (represented by a dotted line with a line
arrowhead, and used to associate data, text, and other Artifacts with flow objects). Swimlanes is used
as a mechanism to explain functional capabilities or responsibilities of different roles within the
process. The two types of BPD Swimlane objects are: Pool which represents a participant in a
process, and also act as a graphical container for partitioning activities from other Pools, and Lane
which is a sub-partition within a Pool and used to categorize and organize activities.
Figure 21. Notations of BPMN 2.0
In the BPD, Pools represent different process participants or different entities such as hospital and
patients, and separate with each other; while lanes are more used to separate activities within a
specific company function or role. Both BPMN 1.0 and BPMN 2.0 specifications defined only three
types of BPD Artifacts which are Data Object, Group and Annotation. Data Objects show how data
is generated and required by activities during the process; the grouping is used for analysis or
documentation purposes, represented by a rounded corner rectangle with dashed line; while
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annotations are used to provide additional information for the understanding of the BPD. The
following figure is one example of BPMN2.0 poster showing various notations (2015)
There are over 40 tools to support BPMN modelling (1997b). Many researches around BPMN have
been conducted. Ye et al. (Ye et al., 2008) provided a mapping from BPMN to YAWL (a workflow
language) nets to fix the gap of lack of semantics of BPMN for evaluating BPMN models.
Also, several techniques have been achieved about BPMN. BPMN can be transformed to BPEL
(Business Process Execution Language for Web Service) to be executed directly (Ouvans et al.,
2006). Some model transactions has been made between BPMN work flow diagrams and WebML
hypertext diagrams which allows fast implementation of the specified business process (Brambilla,
2006). An implementation of BPMN into redesign of a service management process in a truck
dealership in the N.E. US is described by this paper (Zur Muehlen and Ho, 2008). Fig.8 is an example
of order fulfilment using BPMN 2.0 specifications. (1997b)
Figure 22. An example Order fulfilment (1997b)
The positive aspect of BPMN is very obvious, since it supports process modelers standard notation
to map business processes. And also it is indeed a rich and expressive language to use. At same time,
negative section appears. As is showed above, there are too many notations to be used to express
business process so that if someone wants to use them they should accept conduct by expertise,
while 70% of BPMN users are self-taught, the danger of using BPMN are wider-spread. Also, the cost
to fix errors made in the conceptual specification of processes is a very costly impediment to BPM
project success. (Recker, 2010). This modelling language is not adopted in this project.
15.5 Petri Net
Petri Net originated in the dissertation of German scientist Carl Petri in the 1960s. In his
dissertation, Petri developed a novel network of asynchronous information flows and used it for
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investigating communicating Finite State Machines (FSMs). His work attracted the attention of a
number of researchers who found the modelling tool very convenient for representation and the
study of interacting parallel events and processes in discrete-event systems (Kostin and Ilushechkina,
2010). Since then, thousands of articles have been published that considered different theoretical and
numerous applications of Petri Nets.
Petri Net diagrams consist of two types of nodes, places and transitions, without any limitation on
the number of places incident to a transition and the number of transitions incidents to a place.
Places and transitions are represented by small circles and straight line segments or rectangles,
respectively. These two figures are connected by means of an arc, and the arc connects a place with
a transition or vice versa and has an arrow heading to the direction of flow. Inhibitor arcs which have
a circle instead of an arrow head represent a condition that no tokens must be contained in the
corresponding place for them to activate. (Peterson, 1981)
Figure 23. Petri Net components & example (2013)
Petri Nets have been used in several areas such as software design, workflow management, process
modeling, data analysis, concurrent programming, reliability engineering, diagnosis, discrete process
control, simulation and KPN modeling (2013).
The example below shows a simple modeling example of a bus stop. The left part of the diagram
represents the movement of the waiting passengers who want to board the bus while on the right
side of the diagram represents the movements of the bus. At the top of the diagram you have two
circles which are called places. On the left you have people waiting for the bus while on the right you
have the bus moving towards the bus stop. The transition point for the bus is to stop at the bus stop
which brings it to its next place that of waiting for people to board the bus. At this stage the waiting
people represented by the three black dots, start moving through a transition stage that of boarding
the bus one at a time until the bus stop is empty. Once this is completed the bus will go through
another transition point that of moving away from the bus stop and the black dot representing the
bus will move to the next place that of bus leaving the bus stop.
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Figure 24. Bus Stop Example
During the usage and research of a large number of researchers, the characteristics of Petri-Nets are
concluded as follows: The size of the Petri-Nets model is usually easily manageable, regardless of the
amount of tokens present. Petri Nets allow the analyst to model dynamic situations giving the user
more flexibility in type of models created. Petri Nets, used in a simulation with the help of good
software, allows the user to actually observe the processes. Importantly, the user can develop a
model, and then observe the tokens as they move throughout the model in real or simulated time.
At the same time, Petri Nets are easy to modify due to the simplicity of the system.
On the other hand, disadvantages exist. The model is ideal to see how the framework works but lack
the graphic/esthetic details that are obtained from a simple flow chart. Currently, the major
drawback of Petri Nets is the lack of readily available dedicated software package. (Birolini, 2007,
Peterson, 1981). Furthermore, the process model cannot integrate information model together
within the same diagram, which is the most important characteristics for this project. So from this
point, this modelling tool is not appropriate to this project.
15.6 Design Roadmap
The Design Roadmap (DR) method is the selected method for modelling the maintenance process,
because it meets the proposed requirements mentioned at the beginning of this chapter. DR’s were
firstly developed by Park and Cutkosky (1999) as a bipartite graph; they consist of basic node types:
tasks and features. The nodes are connected by arrowed lines representing three types of
relationship: abstraction, precedence and constraint links. Tasks are key units of a process, with at
least one feature as its input and one or more features as its output. A feature is information or
material based on which a task operates - “it can represent data (e.g. scalar, vector, list, graph, etc.),
an aggregate property (such as electrical properties, or geometry) or an artefact (e.g. a schematic
drawing)” (Park and Cutkosky, 1999).
There are three basic dependency modes determined by three types of links between tasks and
features:
Precedence links are the primary mechanisms in the DR model which represents the process and
information flow; this link is defined in order to make the incomplete input information possible,
and make the sequence, relationship and the boundaries between tasks and features more
clearly. There are two precedence relationships: strong and week. Strong links represent the
strict ordering between nodes (denoted as Dlink1(Fi,Tj) or Dlink1(Ti,Fj)); while weak links
represent feedback and feedforward between features and tasks (denoted as Dlink2(Fi,Tj) or
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Dlink2(Ti,Fj)); a side-effect link (M) represent a secondary executing effect of a task upon a
feature. The execution of the task does not determine the feature existence, but it may influence
the feature values. Fig.11 is the example of this type of links.
Figure 25. (a) Notation for feedforward and feedback links; (b) notation for side-effect links.[source in (Park and
Cutkosky, 1999)]
Constraint links always connect two feature nodes. Clink(Fi,Fj) means feature Fj is affected by
feature Fi due to some constraint relationship.
Abstraction links connect the members of a sequence of nodes to a single parent node, denoted
as ALink(parent, child). Fig.12 shows an example of a hierarchy of abstraction links. The box
node ID=R and is at level l of d, where d is the depth of the abstraction tree belonging to the
root R (l = 1 for the lowest level node, and l = d for the root node). R is also labeled ‘0’ for the
root node.
Figure 26. A multi-level hierarchy (Ti are tasks and A denote abstraction links).
DR models can deal with both simple and complex processes. The syntax of DR is easy to
understand and build. DR is particularly appropriate for manufacturing projects and suitable for this
T1 T2F1 F2 F3
M
M
T1 T2F1 F2 F3
F
F
I O OI
I IO O
(a) Dlink2(F1,T2), Dlink2(F3,T1)
(b) Dlink2(T1,F3), Dlink2(T2,F1)
T1
T1.1 T1.2
T1.1.1
T1(2/3)
A
A
...
...
A
T1(2/3)
T1(1/3)
0(3/3)
ALink(T1, T1.1),...; ALink(T1.1, T1.1.1),...; ALink(T1, T1.2)
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project, due to it being good at representing sequences and feedback loops. More importantly, it
includes the product data in this model. Besides, DR is easy to build, even in Microsoft Excel; thus,
based on the above advantages and the requirement of this project, this modelling language is
selected as the modelling tool for this project.
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CHAP VI. DISCUSSION
Research has revealed that, in today’s globally competitive markets, manufacturing companies need
to develop progressive and flexible work practices in order to maintain competitive advantage [1] . It
is suggested [28] that successful product development and the ability to innovate and introduce new
product platforms quickly are fundamental to corporate success. The PLM process is of paramount
importance against this background and effective knowledge management is vital, especially in relation
to engineering and high technology industries. An inability to access key information and knowledge
will ultimately be detrimental to organisations’ commercial success.
Developments in the field of Information Technology offer the prospect of enhanced facilitation of
knowledge exchange, innovation and product development to meet today’s challenges. Research [25]
confirms that PLM has emerged as a key strategic approach to create, manage and use corporate
intellectual capital, seeking to embrace all stages of a product’s life from idea generation through to
delivery, servicing and ultimate decommissioning. PLM systems are seen to offer the infrastructure to
exchange knowledge more effectively and, it has been suggested [99] that enterprises must adopt
their practices as PLM is no longer “an option ... it is a competitive necessity”.
The competitive environment which has evolved over the past thirty years has contributed to
companies seeking various alternative solutions to enhance operations. The 1980s saw a plethora of
bespoke solutions being adopted by different functional areas within organisations which sought to
enhance corporate offerings in increasingly competitive environments; these included CAD, CAM,
CAE, SCM, ERP and CRM Systems. Unfortunately, these were developed to meet specific needs
within the business and, in the field of engineering, they essentially focussed upon geometric drawings
and design. It was not until nearer the end of the 1990s that the concept of PLM emerged to
consolidate and integrate the diverse organisational sources of product-related data, information and
knowledge. PLM is now defined as “a business solution for integrating people, information and
processes across the extended enterprise, through a common body of knowledge” [32] ; it
represents a strategy to improve organisational learning and the management of knowledge.
PLM offers formalised and facilitated processes, which allow colleagues and those involved in
extended partnerships to develop competitive platforms which are not easily copied by others. As a
business strategy, PLM allows for the capture of best practices and lessons learned, while
accumulating a store of intellectual capital for later re-use. However, whilst its benefits are widely
recognised, its implementation is reported in the literature [41] to pose numerous difficulties,
especially in the case of the actual management of knowledge.
PLM provides a specific focus on knowledge and its management, while the additional process of
SCM facilitates the integration of diverse partners into strategic decision making processes, but how
does this combination of potent tools actually work in practice? It is confirmed from the review of
literature that this question is deserving of empirical research and the industrial investigation planned
within the scope of this current project will seek to address whether the potential on offer to
organisations is actually being realised.
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Duigou et al. [41] suggests that, while original equipment manufacturers would have the resources to
implement PLM strategies and share knowledge more effectively, small to medium sized enterprises
may find it more difficult due to limited resources and specific operational issues. The investigation
will seek to verify the current state of the art and identify outstanding requirements.
It is recognised [215] that the concept of PLM offers the prospect of focussing upon each stage of the
product lifecycle with information and knowledge being acquired during each phase; however, a
number of issues have been identified in terms of the actual sharing of that knowledge. Notable
amongst these difficulties is the lack of interoperability between the PLM systems employed [100] ;
to this end, more effective IT support systems are seen to be required if the benefits of PLM are to
be better realised [44] .
Demoly et al. [113] identified that only a limited range of product lifecycle information is being
accessed and, due to shortcomings in PLM system designs, the needs of product lifecycles were still
not being addressed. Sharma [112] concluded that PLM systems still fail to support strategic decision
making. However, others more recently have pointed towards emerging web-based technologies and
the semantic web as offering the potential to develop more effective solutions to enhance knowledge
exchange, especially in relation to dispersed and extended teams. Most recently, product embedded
information devices have also been recognised as offering the prospect of more potent lifecycle
management due to their particular ability to track and trace product information; this is especially
seen as being of benefit in the post-product delivery phases of the product lifecycle. Accordingly, it is
evident that the deficiencies in current PLM systems need to be addressed, while researching and
developing even more powerful tools. It is of the utmost importance to reiterate, however, that the
primary focus of PLM should be on knowledge and the literature highlights that this can often present
complex issues in terms of PLM and KM implementation.
Research [115] identifies KM as a process or practice that develops the ability of an organisation to
create, capture, store, maintain and disseminate its internal employee and organisational knowledge.
Liebowitz [117] has suggested that knowledge cannot necessarily be managed and instead is a set of
activities that companies must practice in order to achieve competitive advantage. Researchers [119]
[121] , furthermore, have identified clear distinctions between data, information and knowledge and
Nonaka [65] further classified knowledge into two distinct forms: explicit and tacit. Explicit
knowledge has been recognised [119] as information that can be codified, while tacit knowledge is
information held by individuals. This tacit knowledge is clearly perceived as being difficult to capture
and share with others, while explicit knowledge is seen as easier to communicate. Finally, a clear
dividing line has been drawn between personal knowledge and organisational knowledge [23] [135]
[136] and it is evident that knowledge possesses different shades of meaning which result in
complexity when organisations seek to manage it effectively during PLM processes.
The management of knowledge is undoubtedly a highly demanding task particularly when set against
the backdrop of a collaborative supply chain. Nonaka [23] described organisational knowledge as the
information flow among various resources within an organisation; the knowledge may be represented
through IT systems, organisational processes, products, company rules and corporate culture and,
when the final component of SCM is introduced into this research, a further level of complexity is
added.
The concept of knowledge is complex, the management of it is demanding and the requirements
placed on PLM systems are wide-ranging within organisations. The extension of processes beyond
the boundary of single organisations into extended enterprises results in participants in processes
potentially being dispersed around the globe and, consequently, the challenge of using PLM as a basis
for strategic knowledge within extended enterprises is formidable. However, it is believed that
enterprises which share organisational knowledge internally and with partners in the supply chain are
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more likely to survive than those which retain knowledge within single entities. So, how effective is
knowledge management in business today?
Both researchers and practitioners suggest that the sharing of tacit knowledge is extremely
challenging and this is a key reason for the emergence of knowledge management systems as an
important focus for research and development activity, which continues to expand. Researchers and
practitioners alike seek to make available processes and tools which facilitate the conversion of tacit
knowledge into explicit and, consequently, increase its re-usability. Customer, supplier and
competitor expertise represent external sources of knowledge and numerous barriers continue to
exist to inhibit the effective sharing of knowledge.
Technical barriers, such as the interoperability of systems, are identified to account for some of the
impediments to knowledge exchange, but successful knowledge sharing within supply chains is also
seen to necessitate an openness and willingness to share or collaborate between parties. Trust in
relationships is a key issue and Simatupang and Sridharan [167] suggest that partners in supply chains
are usually reluctant to share confidential information fully.
At a corporate level, businesses can inhibit knowledge sharing for relatively simple reasons, such as
office layout, hierarchical structures or even the failure to justify to employees why certain IT
systems are being introduced – but, social factors are frequently far more difficult to address than the
technical.
Choi et al. [172] posit that trust and reward mechanisms may be more important for encouraging
knowledge sharing than the facilitation through technical support systems, but trust and reward
research is a whole new field for consideration. In addition, language and cultural barriers, especially
in extended enterprises, can result in significant difficulties in sharing knowledge with overseas
partners. In this regard, the impact of culture on knowledge management is reported to differ
between areas of the world and even countries [182] and it is reported that Spanish and Japanese
employees are more open to knowledge sharing than those working, for instance, in North America.
Unfortunately, given the apparent developing trend towards inter-organisational collaboration,
Samuel et al. [184] , as recently as 2011, concluded that studies into the combined fields of SCM and
KM were limited. Romano [185] , Levinson and Asahi [186] and Mowery et al. [187] also identified
the paucity of empirical studies into the capture and sharing of knowledge in global supply chains, but
recognised that knowledge sharing is a key to success in extended enterprises and an important
contributor to enhanced competitiveness. It has been suggested [149] [188] [190] that the main
focus of knowledge management within supply chain research has only focussed upon the working of
the supply chain partnerships, as opposed to the management of knowledge.
Nevertheless, it has been noted [191] [192] that research into knowledge exchange is increasing in
importance. It has been concluded that few companies have successfully exploited the knowledge
that their supply chain partners possess and Bowersox [195] has suggested that end to end supply
chain knowledge sharing is still in the future. Park and Ungson [209] have suggested that in order to
create conditions for the successful sharing of knowledge in supply chains, partners must anticipate
mutual benefit. Kim et al. [191] reported that if one supply chain partner appears to gain more
knowledge too quickly, then the other may become reluctant to share further, thereby reducing the
overall effectiveness of knowledge exchange for both parties. It is clear that much R&D resources
must continue to be devoted to the generation of knowledge-based tools, which capture and store
collective knowledge to be made available within extended enterprises [218] .
Knowledge management and learning from others in supply chains can introduce innovative thinking
and learning to the benefit of all partners. Knowledge management is a diverse and complex field for
research, but it offers the potential for a higher level of corporate performance, especially when
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customers, suppliers and other stakeholders are integrated into organisational processes; this is
especially so in the case of product lifecycle management.
Supply chain management has been seen as particularly significant for corporate decision making and
new product development, but increasingly its value in terms of knowledge management is also
recognised. Effective SCM allows for improved collaboration, which can facilitate the creation and
sharing of new knowledge, ultimately enhancing learning [236] [237] .
The combination of effective and integrated SCM and PLM systems may be seen as two fundamental
elements for corporate success; enhanced knowledge management deriving from their employment
offers the potential to deliver considerable enterprise competitive advantage. Indeed, the vital
relationship between SCM and KM is made apparent from the reported use of terms, such as
“knowledge supply chain” [256] and “knowledge supply networks” [257] ; these highlight and
emphasise the importance of a better understanding of the process of knowledge exchange within
extended enterprises.
16 CONCLUSION
ICTs adoption can be a source of competitiveness and sustainability for SMEs. In the other hand, the
introduction of new technologies (PLM) is a complex process that involves challenging the existing
organization, not only in terms of information flow but also the human resources management and
OEM/Suppliers relationship level. As seen in literature review, there are a number of factors that
facilitate the adoption of ICT technology, but we also identified a number of obstacles that will need
to act as the adoption takes place.
Modeling different role-based views and finding interoperability between the various information and
communication technology tools used in the manufacturing enterprises is significant. In this study,
due to the specific needs and requirements in terms of PLM-based solution for SMEs and OEMs, the
research focuses on studying various information modeling framework, business process and
interaction between processes by studying the links between computers aided design (CAD) tools
and PLM tools. The future work on PLM in SMEs should be directed towards Investigating
requirements of SMEs and OEMS for having a PLM system and analysis of industrial problems and
gaps, proposing a generic process model to illustrates the current situation in studied enterprises to
show the needs and the way these enterprises have interaction with their sub contracts, Studding on
exist data models of PLM system and propose one which covers the investigated requirements
(regards to our process model and modifies our process model) depict an ontology of exist
information models regard to SMEs and OEMs to covers the related requirements and finally
Development of a demonstrator based on a free PLM software and integrating our developed
process and information models.
In today’s highly competitive environment, it is widely recognised that no business can single-
handedly deliver outstanding products at diminishing cost on a continuous basis and, accordingly,
organisations must develop more efficient and effective supply chains to ensure customer satisfaction,
while securing competitive advantage [243] .
Knowledge management is a diverse and complex field for research, but it offers the prospect of
enhanced corporate performance, especially when customers, suppliers and other stakeholders are
integrated into organisational processes; this is especially so in the case of product lifecycle
management.
It is apparent from the review of literature and discussion in this report that the three separate fields
of PLM, SCM and KM when considered holistically present significant scope for the development of
competitive advantage on the part of OEM’s and their supply chains. A more informed understanding
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of the views and SME’s involved in such processes within extended operations are clearly necessary
to allow the project to develop the relevant tools to deliver more effectively and efficiently the
benefits on offer.
Sharing knowledge and collaborating with supply chain partners in today’s dynamically changing
business environment is an organisational imperative for enterprises endeavouring to survive,
establish competitive advantage and, ultimately, succeed in the prevailing business climate. Enterprises
have to become immersed in the knowledge presented to them by external sources and not just rely
on internal sources.
In 2000, Nonaka [258] proposed a knowledge creation model (see figure 8), which illustrated the
dynamic interaction which takes place between an organisation and its outside supply chain partners,
including customers and suppliers. It was suggested that companies should exploit all forms of
communications with external partners in order to unearth new knowledge, which may benefit them
in developing new and innovative products.
Figure 27. Knowledge Creation Model [258]
Focussing upon the PLM process, it is recommended that a framework be developed to illustrate
clearly how all forms of information and tacit and explicit knowledge found within supply chains may
be captured and shared more effectively to optimise outcomes.
A more informed understanding of the views of OEM’s and SME’s involved in PLM processes within
extended operations, specifically in relation to knowledge management, would clearly be of benefit
and would add to the literature; an in-depth industrial investigation is, therefore, recommended.
Finally, it is proposed that the investigation provide the foundation for the subsequent development
of a relevant knowledge management tool to deliver more effectively and efficiently the benefits
potentially on offer from PLM to supply chain partners of all sizes.
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