Strategies to Reap Operational Gains From Smart ... · PDF file22.10.2015 · Strategies to Reap Operational Gains From Smart Manufacturing and Industrial IoT ... industries by Resource

Strategies to Reap Operational Gains From

Smart Manufacturing and Industrial IoT

SAP Forest Products, Paper, and Packaging Forum October 22nd, 2015

Valentijn de Leeuw

Vice President

ARC Advisory Group

[email protected]

2© ARC Advisory Group

What ARC Does

ARC helps Suppliers

• Accelerate Revenue Growth & Manage Costs

• Bring Products & Services to Market Faster and more Effectively

ARC helps Industrial Companies

• Understand the Value of Emerging Technologies

• Choose Appropriate Suppliers for their Unique Needs

• Implement Operational Best Practices

Blog: Newsletter:http://industrial-iot.com http://industrial-iot.com/subscribe-to-newsletter/


Contents

Smart Manufacturing and Industrial IoT

• A winning strategy?

Initiatives, Technologies and Application Examples

• Vocabulary

• Key Initiatives SMLC, Industrie 4.0 and Horizon 2020

• Innovators’ application examples

Industrial data analytics

IoT Reference Architectures and Standards

• Standards-based integration

People and IoT

• Social sustainability

• Human – machine integration


A WINNING STRATEGY?


Smart manufacturing or Industrial IoT: A strategy for growth?

Strategy for growth?

Hype?

Miss the train?


If Germany can, others can as well!

Manufacturing contributes over-proportionally to

• Trade, export

• R&D, Innovation

• Productivity growth

Multiplier effect on the rest of the economy

Source: McKinsey Manufacturing report 2012


Innovation, Manufacturing and Growth

Value of EU manufacturing has been declining

• Price decreases related to productivity growth!

Comparative international advantage

• Industries with high manufacturing complexity, technology content and quality

Energy cost and price have a significant impact on industrial competitiveness

Source: EU Competitiveness report 2014


Manufacturing Innovation as economic engine

Innovation improves non-cost competitiveness

• Product, process and productivity innovation

• Value-based competiveness raises value of output

• Productivity growth has a net positive impact on employment

Source: McKinsey Manufacturing report 2012, EU Competitiveness report 2013

Source: EU Competitiveness report 2014

R&D and down-turn resilience

Manufacturing and Service innovators have

• More employment growth during upturns

• Less employment decline during downturns


Manufacturing growth and competitiveness

Manufacturing

• Resilience

• Competitiveness

• Growth

Are correlated with a High degree of

• Technology intensity

• Technology/manufacturing complexity

• Quality

DE

Complexity index 2010 versus 1995

SEUK

FRIT

ES

Impacted by Smart Manufacturing


IOT INITIATIVES,TECHNOLOGIES ANDAPPLICATIONS


Smart Manufacturing

• Advanced Manufacturing

• Modular production

• Additive production

• …

• Smart Manufacturing Technologies

• Industrial Internet of Things (IIoT)

• IT and Automation based technologies

• …

Smart Manufacturing Initiatives

vo•cab•u•la•ry (vō-kăbˈyə-lĕrˌē)


Smart Manufacturing Initiatives

Smart Manufacturing Leadership Coalition (US)

(High Value Manufacturing) Catapult (UK)

Industrial Internet Consortium (International)

Industrie 4.0 (Germany, Intl.)

Industrie du Futur (France)

Horizon 2020 (EU)

SPIRE (Sustainable process industries by Resource and Energy Efficiency

Factory of the Future

Alliance for IoT Innovation (EU)

Confederation of Indian Industries’ Smart Manufacturing (India)

Made in China 2025 (China)

Different visions for different outcomes


Key Characteristics

• Revitalize US manufacturing since 2006, innovation

• Oil and Gas, Process and Hybrid focused

• Engineering, Manufacturing and Supply Chain

• Private-public partnerships

• Open SM platform, test beds, market place (standards)

• Step-change improvements• Project cost and

duration• Efficiency, productivity,

cost reduction• Flexibilty and agility• Sustainability and safety

Smart Manufacturing Leadership Coalition


Industrie 4.0

Key Characteristics• German > International

• Rather discrete focused

• ALM, Manufacturing and Supply Chain

• Private-public partnerships

• Technology / Approach

• Digitalization

• IT/OT/Process integration

• Ubiquitous sensing / CPS

• Big data – analytics

• Step change or gradual change

• Industry growth, biz models

• Project cost and duration

• Efficiency, productivity, cost reduction

• Flexibilty and agility

• Sustainability and safety


Interpretation by ThyssenKrupp

More flexible reaction on customer requests

Reduce cost

Increase quality

Increase throughput

Reduce environmental footprint

Goals

Source: R. Achatz, ThyssenKrupp, Orlando 2015


Interpretation by ThyssenKrupp

(Industrial) IT security

Seamless vertical integration

Smart Tools

Smart Factory

Cross-factory Exchange

Seamless Engineering

Support and skill development

Actions



Increased throughput in existing plant

Industrie 4.0 at ThyssenKrupp

Supply chain

integration

• Thyssen-Krupp and clients

• Pull manufacturing

• Throughput increase

• Avoid equipment/ surface size increase

Other SC examples: Supply Chain Operating Networks



Improve flexibility and time-to-marketPlug & Produce!

Unit

Line Controller

MES

SAP

PackML

Industrie 4.0: ISA-88 based PackML at Arla Foods

• Standard integration of equipment in packaging process

• Reduced engineering and integration with several months

Source: Arla Foods


Intermezzo: implications of industry trends

Industry pressures

• Increased pressure on flexibility, agility, sustainability and productivity

• Smaller batches

• Shortened time-to-market

Requirements for Paper and Packaging

• Flexibility, agility, reactivity and smaller batch sizes

• Reduced environmental footprint and cost

• Increased service levels

• Shortened time to market, product changes, etc.


INDUSTRIALSMALL AND BIG DATA ANALYTICS


Predictive maintenance at Storaenso

Red

uce m

an

ual erro

r

A

uto

mate

d t

rig

ger o

f m

ain

ten

an

ce

B

en

efi

t: R

ed

uced

do

wn

tim

e

Source: JP Vande Maele, Storaenso, OSIsoft EMEA UC 2015


Optimizing Manufacturing with IoT at IntelExample of Industrial Quality Analytics

IoT solution in parallel with MES

• Flat architecture

• Processing at the ‘edge’

• Controller connection to Cloud

• Storage, analytics and predictions

Complex partner network

Case 1: predictive combined asset and quality analytics: 9M$ benefits

• Reduce non-genuine off-spec (losses -25%)

• Predictive maintenance (cost -20%)

Two other cases with major benefits

Lessons for other sectors

• Incremental Industrial IoT application

• Large data streams from ubiquitous sensing need large bandwidth

• Data selection at controller level

• Wide range of analytics applications possible

http://www.intel.com/content/www/us/en/internet-of-things/white-papers/industrial-optimizing-manufacturing-with-iot-paper.html


Emerging Architecture – Analytics

Plant Operations

CorporatePurchasingEngineering

Central Central Headquarter

Maintenance

Central

Device buses

Production Management

Logic & Motion

Discrete ControlProcess Control

Infrastructure (Networks…)

Wireless

HMI / Workstations

Fieldbus

Application Specific

Appliances

Safety

Production site

Machine Manufacturer

3rd Parties

Service Provider

Physical asset with sensors, actuators

Local IoT Compute and Communicate module

Smart Machine

IoT Smart Module

Connected Assets Using IIoT

New IoT Analytics and Applications

Purdue Hierarchy

IIoT Hierarchy

Enterprise


From MRPII to Advanced P&S and Analytics

Collaborative

Forecasting

and

Demand

Management

Supply

&

Demand

Balancing

Scheduling

And

Capable to Promise

Rough Cut Capacity Planning

Distribution RequirementsPlanning

Sales and Operations Planning

Master Production Scheduling

Material Requirements Planning

Infinite CapacityScheduling

Available toPromise

Statistical Forecastin

g

Towards

Predictive

Supply Chain

Analytics and

Network

Optimization


1bData Points

Storaenso Langerbrugge site, Belgium. Source: JVandeMaele OSIsoft EMEA UC 2015


Industrial Data

Is

Big Data


Your Grandfather’s BI & Analytics…

OperationalSystems

(ERP, MES, SCM,Financials etc.)

DataWarehouse

12

6

39

1

2

5

4

7

8

10

11


Add Velocity, Volume and Variety…

OperationalSystems,

M2M Data, Partner

Data, PublicData, Textual…

DataWarehouse

12

6

39

1

2

5

4

7

8

10

11


…Has Too Much Latency for IIoT

OperationalSystems


DataWarehouse

Events Insight


Cutting Latency

OperationalSystems,

M2M Data, Partner


DataWarehouse

1. MergedDatabase


Cutting Latency

2. Stream Processing (CEP)3. Predictive Analytics

OperationalSystems


DataWarehouse


Complex Event Processing

Complex Event Processing(aka Event Streaming)

Real-TimeAutomatedDecisions

Data Streams


What Predictive Analytics Isn’t…

3834

5117

6448

7908

9181

1149710788

10021

8341

Dow JonesIndustrial Average



Energy AnalyticsHistorical data analysis, process knowledge, actions

Real-time energy performance monitoring

• Heat recovery at AbitibiBowater Kenogami

• Analysis – Operational rules

• Savings 600.000 Euro/annum

• no CAPEX

Source: PEPiTE, Belgium


Value from Variety (Unstructured Data)

OperationalSystems,

M2M Data, Partner


DataWarehouse

4. Text Analytics


Process AnalyticsComparison of real-time and historical fingerprint

S

toraen

so

’sexp

erim

en

t w

ith

D

Sq

uare’s

TR

EN

DM

IN

ER

Source: JP Vande Maele, Storaenso, OSIsoft EMEA UC 2015


Unstructured Brings New Perspective


The Cloud

Industrial Data Analytics

• Smart manufacturing technology

In the Cloud

• Industrial IoT application


Is Industry Using Big Data…?

3%

“Big Data isIrrelevant for Us”



38%“Don’t Understand

Big Dataor Why it Matters”



16%“Projects Live,or Near Live”


Analytics Levels and Methodologies

Level 1

: h

isto

ric

al rep

orti

ng

•Report

ing,

analy

sis

Level 2

: p

red

icti

ve a

naly

tics

•Fore

casting

Level 3

: p

rescrip

tive a

naly

tics

•Recom

mendations,

optim

ization

Source: Tim Sharpe, Energy management at Sabic UK, Sabisu, EIF 2015


IOTREFERENCE ARCHITECTURESAND STANDARDS


Reference Architecture Model Industrie 4.0 (RAMI4.0)

VDI/VDE, ZVEI, and Industrie 4.0 Plattform WG 2

• Progress report April 2015 / Update July 2015

• Builds upon• IEC 62264 and 61512 (ISA-88 and ISA-95)

• Extends to connected device and value chain network

• IEC 62890 (life cycles Design/Production/Usage)

• I40 components can be built from existing objects plus I40 Admin shell (connector)

• Requirements definition


Overview of ISA 88/ ISA 95 standards (1)Levels and functions used in ISA-95 family of standards

Level 4

Level 1

Level 2

Level 3

Business Planning & LogisticsPlant Production Scheduling,Operational Management, etc.

Manufacturing

Operations ManagementDispatching Production, Detailed ProductionScheduling, Reliability Assurance, ...

Batch

Control

Discrete,

Packaging

Control

Continuous

Control1 - Sensing the production process, manipulating the

production process

2 - Monitoring, supervisory control and automated control of the production process

3 - Work flow / recipe control to produce the desired end products. Maintaining records and optimizing the production process.

Time FrameDays, Shifts, hours, minutes, seconds

4 - Basic plant schedule - production, material use, delivery, and shipping. Determining inventory levels.

Time FrameMonths, weeks, days

Level 0 0 - The actual production process

Level 5Business Management

5 - Strategic level, long term business planning,Financial, HR ..

Time FrameYears, Quarters, Months, Weeks

Adapted from ISA95 and A. Svendsen, Arla Foods


The rationale for using open standard integration

Proprietary Solutions

PI-PCS ……

SAP Business Connector / XML

MESSystem X Plant A,B,C ..

MESSystem Y Plant M,N,..

MESSystem ..Plant ..

PLC PCS

Open Standards

SAP R/3 MM – PP-PI – QM

PLCPLC PLC

SAP R/3 MM – PP-PI – QM

MES/PCSPlant X

MES/PCSPlant Y

MES/PCSPlant ..

PI-PCS …

MES/PCS

…

ISA-95/WBF standard (B2MML XML)

Source: A. Svendsen, Arla Foods


Overview of ISA 88/ ISA 95 standards (2)Zoom in on Levels 3 – 4

Business Planning & LogisticsPlant Production Scheduling,Operational Management, etc

ManufacturingOperations & ControlDispatching Production, Detailed ProductionScheduling, Reliability Assurance, ...

BatchControl

DiscreteControl

ContinuousControl

PI-PCS ……

SAP XI

MESSystem X Plant A,B,C ..

MESSystem Y Plant M,N,..

MESSystem ..Plant ..

PLC PCS

SAP R/3

MM – PP-PI – QM

PLCPLC PLC

ISA-95/WBF standard

(B2MML XML)

Adapted from ISA95 and A. Svendsen, Arla Foods


Efficient intra-company integration

Scenario 1: Company1 acquires Company 2

• Most likely ERP2 will be replaced over time• Most likely several MES platform will remain (cost)

Scenario 2: Company1 outsources part of mfg. to Company 2

• All systems will remain => even more new interfaces ..

Adapted: A. Svendsen, Arla Foods

MES 1 MES 2

MES 3

ERP 1 ERP 2 Company 2Company 1


Costs and benefits of standards-based integration

Specification:

• 2 domains, 2 projects, double project management!

R/3-configuration

• needed extra configuration for integration minimal

xMII

• Hourly rates on xMII/MES at acceptable 85 Euro/hour

Site-implementation, approx. 8-900 hours total (2007) + License costs pr. site

2003-2006 2007 2008

Specification

R/3 -config

MES-config

Testing +

go-live

24

x7

XI-config

24

x7Specification

R/3 -config

MES-config

Testing + go-live

24

x7

xMII-config24x7

R/3 -config

MES-config

Testing +go-live

24

x7

xMII-config

24x7

Specification

Adapted from A. Svendsen, Arla Foods


IOTAND PEOPLE


Improve productivity AND well-being

Social sustainability at work

• Johnsonville Foods

• Harley Davidson (motor cycles)

• Zappos shoes

• Chronoflex (ind. Services)

• Auchan (retail)

• Fabi (automotive components)

• Belgian ministry of social security


We continue to need unique human skills

, collaborative forecasts,

engaged social networking, motivation,

sustainable value eco-systems, decision making …


Human-Machine Integration

Human

• Provide data to the system

• Need to develop trust

• Assesses, delegates, interprets, judges and decides with consciousness and skill

Cognitive agents unload the human

Semantic interaction: meaningful human-

system communication

Source: Maurice Wilkins, Valentijn de Leeuw

Machine

• Allows focusing on problem solving and decision making

• Provide context

• Ecological interface design

Predictive analytics proposes actions

Acts ethically and with compassion


Implementation Strategies

Target radical efficiency improvements

• Start small

Choose areas of innovation in line with business strategy and sector needs

• Per production type, process or plant type

Set goals, define KPI’s

• Improve product, material, substance performance if possible

• Innovate business models (e.g. circular) and value creation ecosystem

• Sustainability

Assessment methodology and Roadmap

• Maturity model, business case, roadmap

• Feasible roadmap, with regular updates


Take away and recommendations For competitiveness, growth and resilience,

• Use the possibilities offered by smart manufacturing, smart supply chains and IIoT

Brace for more volatile and agile supply chains

• Use supply chain analytics and supply chain management to dampen volatility

Modern analytics reduce latency to decision making

• In memory merged data warehouse, stream or complex event processing

• Include unstructured information, data science and domain experts for optimal discovery, predictive and prescriptive analytics

Standards-based integration of OM, SCM and ERP

• Can substantially reduce integration cost (of M&A)

Make/update a IoT/Smart Manufacturing strategy and roadmap

Change management

• Sustainable culture change requires multi-level leadership qualities

Acknowledgement

David White

Senior Analyst

ARC Advisory Group

[email protected]

@addicted2data

IIoT Newsletter: http://industrial-iot.com/subscribe-to-newsletter/

Logistics Viewpoints: http://logisticsviewpoints.com

Thanks to David for the analysis and survey on IIoT, big data and analytics

http://industrial-iot.com/subscribe-to-newsletter/

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Strategies to Reap Operational Gains From Smart ... · PDF file22.10.2015 · Strategies to Reap Operational Gains From Smart Manufacturing and Industrial IoT ... industries by Resource