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Tecnomatix Plant Simulation Worldwide User Conference 2015 Siemens Industry Software Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015 Pushing the Limits of Lean Thinking by Simulation and Data Analytics Univ.-Prof. Dr.-Ing. Jochen Deuse Stuttgart, 16 June 2015 1 dortmund university of technology Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015 2 Institute of Production Systems Research Areas Work System Design Time and Motion Studies Digital Manufacturing Manufacturing Data Analytics Human-Robot- Collaboration Factory Physics

Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

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Page 1: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Pushing the Limits of Lean Thinkingby Simulation and Data Analytics

Univ.-Prof. Dr.-Ing. Jochen DeuseStuttgart, 16 June 2015

1

dortmunduniversity of technology

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015 2

Institute of Production SystemsResearch Areas

Work System Design

Time and Motion Studies Digital Manufacturing Manufacturing DataAnalytics

Human-Robot-Collaboration

Factory Physics

Page 2: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Decoding the DNA of theToyota Production System

Basic rules of „Lean Thinking“ (Toyota-DNA)

3

Rule 1: All work shall be highly specified as tocontent, sequence, timing, and outcome.

Stabilise

Rule 2: Every customer-supplier connection mustbe direct, and there must be anunambiguous yes-or-no way to sendrequests and receive responses.

Pull

Rule 3: The pathway for every product and servicemust be simple and direct.

One-Piece-Flow

Rule 4: Any improvement must be made inaccordance with the scientific method,under the guidance of a teacher, at thelowest possible level in the organisation.

Single-Factor Experiments[Spear & Bowen 1999]

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Basic rules of „Lean Thinking“ (Toyota-DNA)

4

Rules are formulated generically andtherefore they suggest universaltransferability.

However, the rules must be interpreted in adomain-specific context.Context is defined by Value AddedVariability (VAV).Methods have to be chosen depending onfeatures and expressions of variability, butalso principles have to be scrutinised.

Decoding the DNA of theToyota Production System

Page 3: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Categories of Variability

5

Does not contribute toadded value

Loss of availabilityLoss of qualityLoss of performance

should be eliminated

Does not contribute toadded value

Probabilistic risk“Common Cause”WeatherEnvironmentalimpacts

can’t be eliminatedentirely

Contributeto added value

Customised deliveryquantityCustomer specificdue-dateCustomised productspecification

should not be eliminated

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Features of Value Added Variability (VAV)

6

Sequence of operations

Process routing

Work content

Target lead timeDemand

Similarity: adaptedJaccard-coefficient

Dispersion: coefficientof variation

Measurement parametersFeatures of Variability

Page 4: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Domains of Production Systems defined by VAV

7

Variability oftarget lead time

Variability of work content

Machine Tools &Industrial Equipment

Ford 1913

Toyota 1987

Automotive-OEM2013

[Daimler 2010]

[dapd/Ronald Wittek 2013]

[AP/Paul Sakuma]

[Hulton Collection]

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Simple/ Obvious: low VAVApply existing methods and tools

sense – categorise – respond

Complex: high VAVExperimental/ Explorative

probe – sense – respond

Classification of Production Systems acc. to Cynefin Framework

8

Chaotic

act – sense – respond

Complicated: high VAVAnalytical/ Experimental

sense – analyse – respond

[acc. to Snowden 2007]

OrderedStatic cause-effect relationship

UnorderedDynamic cause-effect relationship

Page 5: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Simple/ Obvious: low VAVApply existing methods and tools

sense – categorise – respond

Complex: high VAVExperimental/ Explorative

probe – sense – respond

Classification of Production Systems acc. to Cynefin Framework

9

Chaotic

act – sense – respond

Complicated: high VAVAnalytical/ Experimental

sense – analyse – respond

OrderedStatic cause-effect relationship

UnorderedDynamic cause-effect relationship

[acc. to Snowden 2007]

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Use case Industrial Engineering-Training Centre: Gear Box Assembly Line

10

Ordered, simple/ obvious SystemSimulated work environment,i. e. laboratorySimple and well defined tasksSimple and static cause-effectrelationships

Categories of VariabilityNon-VAV:

Loss of availabilityLoss of qualityLoss of performance

VAV:None

Simple Production Systems

Select and apply existing methodsand tools (Lean Recipes),e.g. value stream mapping, linebalancing or Kanban

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Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015 11

Applying Pencil & Paper Methods

Value Stream Mapping Line Balancing

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Stabilise Value Streams: Eliminate Mura

12

Start:stabilise the bottleneck

Gradual stabilisation ofpre-processes

O XO X

Bottleneck

Buffer againstvariability in demand

Buffer againstvariability in supply

Stabilisation ofbottleneck

Pre-Process 2 Pre-Process 1

[Richter & Deuse 2011]

Non value added variability (Mura) is a major root cause of waste (Muda)

Page 7: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Stabilise: Dynamic Value Stream Mapping

13

FS (44)VZ[min]: Ø=108, =49PZ[min]: Ø=53, =24SZ[min]: Ø=55, =40

NG: Ø= 34%; =10%

FW (03)VZ[min]: Ø=146, =126PZ[min]: Ø=92, =50SZ[min]: Ø=53, =110

NG: Ø= 67%; =13%

SZ[min]: Ø=27, =61

NG: Ø= 87%; =21%

FG (37)VZ[min]: Ø=612, =316PZ[min]: Ø=503, =253SZ[min]: Ø=125, =112

NG: Ø= 90%; =21%

FG (34)VZ[min]: Ø=413, =209PZ[min]: Ø=341, =145SZ[min]: Ø=73, =178

NG: Ø= 76%; =27%

SperrlagerQS-Lager

STR (07)VZ[min]: Ø=67, =223PZ[min]: Ø=35, =17SZ[min]: Ø=32, =222

NG: Ø= 52%; =14%

FW (06)VZ[min]: Ø=193, =77PZ[min]: Ø=128, =41SZ[min]: Ø=65, =55

NG: Ø= 54%; =13%

Ø= 106 h= 52 h

Ø= 50 h= 69 h

Ø= 35 h= 56 h

Ø= 58 h= 108 h

03 (PZ): Ø=1,5 h, =0,8 h06 (PZ): Ø=2,1 h, =0,7 h

36 (PZ): Ø=5,7 h, =2,2 h37 (PZ): Ø=8,4 h, =4,2 h34 (PZ): Ø=5,7 h, =2,4 h

07 (PZ): Ø=0,6 h, =0,3 h 44 (PZ): Ø=0,9 h, =0,4 hLZ = 50 hLZ = 69 h

VLZ = 1,38

PZ = 35 minPZ = 17 min

VPZ = 0,49

Quantify Variability within Value Streams

Simulate Value Streams using Plant Simulation

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Simple/ Obvious: low VAVApply existing methods and tools

sense – categorise – respond

Complex: high VAVExperimental/ Explorative

probe – sense – respond

Classification of Production Systems acc. to Cynefin Framework

14

Chaotic

act – sense – respond

Complicated: high VAVAnalytical/ Experimental

sense – analyse – respond

[acc. to Snowden 2007]

OrderedStatic cause-effect relationship

UnorderedDynamic cause-effect relationship

Page 8: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Use case: Manufacturing of steam turbine components

15

Analytical/ Experimental:Apply “Factory Physics” and conductsimulation experiments; keep “TrueNorth” of Lean Thinking in mind

Complicated Production Systems

Ordered, complicated systemCause and effect typicallytemporally and spatially distinctDifferent products with differentprocesses and sequences ofoperationsMake-to-Order setting,i. e. unpredictable order sequence

Categories of VariabilityNon VAV:

Loss of availability, quality orperformanceEnvironmental impacts

VAV:Sequence of operationsProcess routingsWork contentDemandLead time

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

disj

unct

ive

One-Piece-Flow: Layout Housing Manufacturing

16

Optimise factory layout according to Lean Thinking:“The pathway for every product and service must be simple and direct.”

Using simulation experiments to identify capacity overload (Muri)Factory Layout and Flow of Material

pool

ing

ofbo

ttlen

eck

mac

hine

s

Simulation Experiments

Factory Layout and Flow of Material Inventory Utilisation

M/C 2

M/C 1

M/CPool

M/C 2

M/C 1

Horizontal fraises machineWashingVertical lathe (M/C 2)Vertical lathe (M/C 1)WeldingRadial drilling machineHousing assembly CHousing assembly BHousing assembly AGantryBlade assembly 4Blade assembly 3Blade assembly 2Blade assembly 1Disassembly

Simulation Results

Horizontal fraises machineWashingVertical lathe (M/C 1&2)Vertical lathe (M/C 1&2)WeldingRadial drilling machineHousing assembly CHousing assembly BHousing assembly AGantryBlade assembly 4Blade assembly 3Blade assembly 2Blade assembly 1Disassembly

[RIF e. V.]

Page 9: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Factory Physics: Experiments on Kingman’s Formula

17

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Simple/ Obvious: Low VAVApply existing methods and tools

sense – categorise – respond

Complex: High VAVExperimental/ Explorative

probe – sense – respond

Classification of Production Systems acc. to Cynefin Framework

18

Chaotic

act – sense – respond

Complicated: High VAVAnalytical/ Experimental

sense – analyse – respond

[acc. to Snowden 2007]

OrderedStatic cause-effect relationship

UnorderedDynamic cause-effect relationship

Page 10: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Complex Production Systems

19

Use case: Machining of engine components in a flexible manufacturing system

[Felsomat 2014]

Unordered, complex SystemCausalities are dynamic, butdiscoverableGrowing number of systemelements, interfaces and system-internal relationshipsHigh level of automation

Categories of VariabilityNon VAV:

Varying system loadDynamic bottlenecksLoss of availability, quality orperformance

VAV:Sequence of operationsProcess routingsWork contentDemandLead time

Experimental/ Explorative:Manufacturing Data Analytics basedon unsupervised and supervisedmachine learning

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Limited applicability of “Scientific Method”

20

Apply probing instead of Single-Factor ExperimentsIdentify unknown, multivariate patterns in industrial databasesUse data mining, i.e. unsupervised learning procedures

[Felsomat 2014]

System State:

StableUnstable

[RIF e. V.]

Page 11: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015 21

Trends in Big Data Analytics

[acc. to Schwab and Keil 2012]

Real-TimeAnalysis

Predictive Data Analysis

Real-TimeReport

ProcessAutomation

UnstructuredExternal Data

Unstructured Internal Data

Traditional InstructionsStrategicPlanning

Zetta(1021)

Exa(1018)

Peta(1015)

Tera(1012)

Years

Data Volume(in bytes)

Ad hoc decisionSupport

OperationalPlanning

AnalysisTime Line

Hig

hD

imen

sion

alD

ata

Seconds Years

Due to long run-times, simulation experiments are not applicable in real-time and predictive analysisSupervised machine learning supports real-time analysisLearning of prediction model by using simulation data

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

ChaoticNo manageable patternsNo occurrence in production systems

Complicated High VAVApply “Factory Physics” and conductsimulation experimentsKeep “True North” of Lean Thinking in mind

Complex High VAVProbing: unsupervised machine learningCombination of simulation and supervisedmachine learning

Simple/ Obvious Low VAVSelect and apply existing methods and tools

Conclusion

22

AP/Carmelo Imbesi [Kurier.at 2014]

[Felsomat 2014]Level of

IT-Support

Page 12: Pushing the Limits of Lean Thinking by Simulation and Data ...€¦ · Pushing the Limits of Lean Thinking by Simulation and Data Analytics ... Analytics Human-Robot- ... Optimise

Tecnomatix Plant Simulation Worldwide User Conference 2015

Siemens Industry Software

Prof. Dr.-Ing. J. Deuse Plant Simulation Worldwide User Conference 2015

Prof. Dr.-Ing. Jochen Deuse

TU Dortmund UniversityInstitute of Production Systems

[email protected]

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Thank you for your kind attention!