quantifying benefits of lean manufacturing tools ...

Technology Development

QUANTIFYING BENEFITS OF LEAN MANUFACTURING TOOLS IMPLEMENTATION WITH SIMULATION IN COOLANT HOSE FACTORY Effendi Mohamad1,4, Teruaki Ito2, Dani Yuniawan3,5 Graduate School of Advanced Technology and Science University of Tokushima, Tokushima, Japan

1,3

Institute of Technology and Science University of Tokushima, Tokushima, Japan 2

Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Malaysia 4

Universitas Merdeka, Malang, East Java, Indonesia

5

Email: [email protected], [email protected], [email protected] ABSTRACT Lean Manufacturing (LM) is a philosophy aiming at detecting and eliminating waste throughout a product’s value stream by means of a set of synergistic tools and techniques. Examples of LM tools and techniques are Single Minute Exchange of Die (SMED), Kanban, 5-S, Value Stream Mapping, Preventive Maintenance, Cellular Manufacturing (CM), Standardised Work, Heijunka, and Poka Yoke. Although these tools could resolve many manufacturing issues, it is difficult to quantify the benefits of implementing LM before it is actually implemented. Therefore, this study focused on a process simulation approach to see the effectiveness of LM tools before their implementation. A process model of a coolant hose manufacturing (CHM) factory was designed and its simulation model was then implemented. Subsequently, this paper presents the implemented simulation model and shows how it could be used to see the effect of LM tools using an example of SMED LM tool and Cellular Manufacturing LM tool. KEYWORDS: Lean Manufacturing, Simulation, Key Performance Indicators

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Journal of Human Capital Development

1.0 INTRODUCTION The lean concept was first introduced by Taiichi Ohno and Shingeo Shingo from Toyota Motor Company. Later, Womack et.al. (1990) introduced the term “lean manufacturing” (LM) in the book entitled The Machine That Changed The World. In recent years, LM has been widely used by discrete and process manufacturing companies in order to remain competitive. LM has been applied to various sectors including automotive, electronics, fabrication plants, and consumer product manufacturing to improve their productivity and to gain higherquality products in shorter lead time at a reduced cost. Basically, LM is a philosophy aiming at detecting and eliminating waste throughout a product’s value stream by means of a set of synergistic tools and techniques (de Treville & Antonakis, 2006). Detailed explanation on concept, objectives, implications, structure, and tools of LM can be acquired from Kumar & Kumar (2012). Examples of LM tools and techniques are Single Minute Exchange of Die (SMED), Kanban, 5-S, Value Stream Mapping, Preventive Maintenance, Cellular Manufacturing (CM), Standardised Work, Heijunka, and Poka Yoke. Most lean tools and techniques are based on “pencil and paper” technique involving analysis of static models (Sevillano et.al., 2011). These tools are also available as computer software to apply in practical manufacturing lines. Even though these tools could resolve many manufacturing issues based on the tenets of LM, expected results by LM tools cannot be seen before their implementation (Ito et.al. 2013), leading to difficulties to convince the management team to implement LM. This is due to the lack of tools to quantify the effectiveness of LM implementation (Detty & Yingling, 2000). Oftentimes, the decisions to adopt LM have to be made based on faith in LM philosophy and experiences of other management teams (Abdulmalek & Rajgopal, 2007). A simulation-based approach is therefore needed to quantify various performance measures to enable management teams to make informed decisions before actually implementing LM tools (Standridge & Marvel, 2006). This study focused on the process simulation approach to see the effectiveness of LM tools before their implementation. In this study, a sample process simulation model was designed and implemented based on the manufacturing process data of a coolant hose manufacturing (CHM) factory. Moreover, an idea of user understanding support was implemented in this study as several functions, which are layout function, zoom-in/zoom-out function, task status function, and KPI status function. This paper presents the simulation model based on the 14

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CHM factory and shows how it could be used to see the effect of LM tools using an example of SMED and CM. 2.0

MANUFACTURER AND PRODUCT DESCRIPTION

The CHM factory produces four types of products including Coolant Hose 4 (CH4), CH6, CH8, and CH10. The factory floor consists of six sections (Section 1 to Section 6) as depicted in the flow diagram of CHM factory floor (Figure 1). Section 1 (S1) represents incoming warehouse, Section 2 (S2) represents crimping manufacturing line. Section 3 (S3) represents CH4 & CH6 manufacturing line. Section 4 (S4) represents CH8 & CH10 manufacturing line. Section 5 (S5) represents packaging line. Finally, Section 6 (S6) represents outgoing warehouse.

Figure 1: 1: Flow diagram factoryfloor floor Figure Flow diagram of of CHM CHM factory S1 supplies raw materials to S2, S3, S4,toand Then, S3,S5. S4, Then, and S5 supply S1 supplies raw materials S2,S5.S3, S4, S2, and S2, S3,their S4,processed and parts to S3/S4, S4, S5, and S6, respectively. Production capacity for each line is 150 units/day S5 supply their processed parts to S3/S4, S4, S5, and S6, respectively. for 9 hrs. Materials' handling of parts in production lines is performed by either forklift or Production capacity for each line is 150 units/day for 9 hrs. Materials’ trolley. Table 1 shows these conditions.

handling of parts in production lines is performed by either forklift or Table 1: Manufacturing conditions trolley. Table 1 shows these conditions. From S1

To S2 S3 S4 S5

S2 S3 S4 S5

S3 S4 S5 S5 S6

Material Units Material Handler (MH) Raw Material Crimping 50 Forklift CH4 & CH6 and CH8 & CH10 Raw Material CH4 & CH6 50 Forklift Raw Material CH8 & CH10 50 Forklift Raw Material 50 Forklift Wrapping/Packaging/Labelling Crimping CH4 & CH6 25 Trolley Crimping CH8 & CH10 25 Trolley CH4 & CH6 25 Trolley CH8 & CH10 25 Trolley ISSN: 1985-7012 Vol. 6 No. 2 July-December 2013 15 Final Products 25 Trolley

S1 supplies raw materials to S2, S3, S4, and S5. Then, S2, S3, S4, and S5 supply their processed parts to S3/S4, S4, S5, and S6, respectively. Production capacity for each line is 150 units/day forJournal 9 hrs.ofMaterials' handling of parts in production lines is performed by either forklift or Human Capital Development trolley. Table 1 shows these conditions. Table 1: Manufacturing conditions

From S1

To S2 S3 S4 S5

S2

S3 S4 S5 S5 S6

S3 S4 S5

Table 1: Manufacturing conditions

Material Raw Material Crimping CH4 & CH6 and CH8 & CH10 Raw Material CH4 & CH6 Raw Material CH8 & CH10 Raw Material Wrapping/Packaging/Labelling Crimping CH4 & CH6 Crimping CH8 & CH10 CH4 & CH6 CH8 & CH10 Final Products

Units 50

Material Handler (MH) Forklift

50 50 50

Forklift Forklift Forklift

25 25 25 25 25

Trolley Trolley Trolley Trolley Trolley

The number of workstation (WS) in each section is three WSs in S2, five WSs in S3, six WSs in S4,The and three WSs inofS5, as shown in Table 2. Each of these WSs is is operated one operator. number workstation (WS) in each section threebyWSs in S2, Table 2 also shows the task type of each WS and changeover (C/O) operation. Changeover five WSs in S3, six WSs in S4, and three WSs in S5, as shown in Table operation is scheduled in S2, S3, and S4 because of die switches for product type change in the 2. Eachline. of these WSs is operated by one operator. Table 2 also shows production

the task type of each WS and changeover (C/O) operation. Changeover operation is scheduled in S2, S3, and S4 because of die switches for product type change in the production line. Table Task conditions conditions Table 2: 2:Task Section

S2

S3

Task WS1 (Machining) WS2 (Testing) WS3 (Marking) WS1 (Machining) WS2 (Deburring) WS3 (Crimping) WS4 (Testing) WS5 (Marking)

No of Operator 1

C/O Section *

1 1 1

S4

Task WS1 (Machining) WS2 (Deburring) WS3 (Crimping)

*

1 1 1 1

*

S5

No of Operator

C/O

1

*

1 1

WS4 (Welding)

1

WS5 (Testing) WS6 (Marking) WS1 (Wrapping) WS2 (Packaging) WS3 (Labelling)

1 1 1

*

1 1

*Changeover (C/O): For product switch in production line. 3.0

MODEL DEVELOPMENT

process model of CHM factory was developed in this study using Arena 12.0 simulation 3.0 A MODEL package (Kelton et al.,DEVELOPMENT 2010), based on the SIMAN language. Firstly, the layouts of all sections in CHM factory were created. From these layouts, model logics were created for all sections. For

A process model factory was study using sections with scheduledof C/OCHM operation (S2, S3, and S4), developed submodels of the in C/Othis process were also created. Example of layout (Figure 2), model logic (Figure 3), and submodel of C/O process Arena 12.0 simulation package (Kelton et al., 2010), based on the (Figure 4) are shown in this paper, using S4 as an example. SIMAN language. Firstly, the layouts of all sections in CHM factory were created. From these layouts, model logics were created for all sections. For sections with scheduled C/O operation (S2, S3, and S4), submodels of the C/O process were also created. Example of layout 16

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*Changeover (C/O): For product switch in production line. 3.0

MODEL DEVELOPMENT


A process model of CHM factory was developed in this study using Arena 12.0 simulation package (Kelton et al., 2010), based on the SIMAN language. Firstly, the layouts of all sections in CHM factory were created. From these layouts, model logics were created for all sections. For (Figure 2), model logic (Figure 3), and submodel of C/O process (Figure sections with scheduled C/O operation (S2, S3, and S4), submodels of the C/O process were also 4) arecreated. shown in this paper, using S4 logic as an example. Example of layout (Figure 2), model (Figure 3), and submodel of C/O process (Figure 4) are shown in this paper, using S4 as an example.

Figure Figure 2: Layout of S4 ininCHM Factory 2: Layout of S4 CHM Factory

Figure 3: Simulation model logic for S4

S4

4: Submodel of C/O forWS1 WS1 at at S4 FigureFigure 4: Submodel of C/O for S4 Next, animation of CHM factory floor was developed to represent the model logic (Figure 5).

Included in the animation are all the products produced by S2, S3,toS4, and S5. Detailed Next, animation of CHM factory floor was developed represent the information on the products5). is depicted in Figure 6 toanimation Figure 9. model logic (Figure Included in the are all the products produced by S2, S3, S4, and S5. Detailed information on the products is depicted in Figure 6 to Figure 9.

S4

S3

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Next, animation of CHM factory floor was developed to represent the model logic (Figure 5).

Included in the Capital animation are all the products produced by S2, S3, S4, and S5. Detailed Journal of Human Development information on the products is depicted in Figure 6 to Figure 9.

S4

S3

S1

S6

S5

S2

Figure5: 5:Snapshot Snapshotof ofbird’s-eye bird’s-eyeview viewof ofthe theCHM CHMfactory factoryfloor floor Figure

Figure 5: Snapshot of bird’s-eye view of the CHM factory floor

CrimpingCH8 CH8& &CH10 CH10 CrimpingCH4 CH4& &CH6 CH6 Crimping Crimping Figure 6: Products ofofS2S2 Figure 6:Products Products Figure 6: of S2

CH4 CH4

CH6 CH6 Figure 7:Products Products ofS3 S3 Figure 7: Products ofof Figure 7: S3

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CH6

CH4


Figure 7: Products of S3

CH10

CH8

Figure Figure 8: 8: Products Products ofof S4S4

Wrapped CH4, CH6, CH8 & CH10

Packed CH4, CH6, CH8 & CH10

Labelled CH4, CH6, CH8 & CH10

Figure Figure 9: 9: Products Products ofof S5S5 Basically, animation was used to check the model logic in order to ensure that the model was Basically, animation was used to check modelthat logic order to error-free. Furthermore, the model logic was verifiedthe to ensure the in model closely ensure that was error-free. Furthermore, the all model logic was approximated the the real model system. Verification was deployed by scrutinising the products from theverified point of their creationthat (S1: the Incoming warehouse) the point of their disposal from the to ensure model closely to approximated the real system. system (S6: Outgoing warehouse). Finally, the model was validated by means of comparison Verification was deployed by scrutinising all the products from the between simulation result and mathematical results.

point of their creation (S1: Incoming warehouse) to the point of their Table 3: Validation of CHM factory model disposal from the system (S6: Outgoing warehouse). Finally, the model was validated by means of comparison between simulation result Similarity Confidence Statusand Simulation Mathematical (%) interval range result calculation mathematical results. Section (minute)

S2 S3 S4 S5

385.59 834.61 887.14 118.89

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result (minute) 380.02 853.60 853.60 111.40

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98.56 97.77 96.22 93.70

No. 2

342.13–519.58 639.43–1001.3 572.08–989.3 91.36–203.70

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Valid Valid Valid Valid

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approximated the real system. Verification was deployed by scrutinising all the products from the point of their creation (S1: Incoming warehouse) to the point of their disposal from the Journal(S6: of Human Capital Development system Outgoing warehouse). Finally, the model was validated by means of comparison between simulation result and mathematical results. Table 3: Validation of CHM factory model

Table 3: Validation of CHM factory model Section S2 S3 S4 S5

3.1

Simulation result (minute) 385.59 834.61 887.14 118.89

Mathematical calculation result (minute) 380.02 853.60 853.60 111.40

Similarity (%)

Confidence interval range 95%

Status

98.56 97.77 96.22 93.70

342.13–519.58 639.43–1001.3 572.08–989.3 91.36–203.70

Valid Valid Valid Valid

Functions and features of CHM Factory Model

Considering the specific use for LM simulation, an idea of user understanding support was implemented in the CHM factory model, which provided several functions to help users understand how LM tools could improve the manufacturing process through an interactive use of process simulation. In other words, users could visualise production processes and quantify the effectiveness of LM tool implementation via the model.ofThis presents some of these functions to 3.1 simulation Functions and features CHM section Factory Model clarify the features of the CHM factory model. Considering the specific use for LM simulation, an idea of user understanding support was implemented in the CHM factory model, which provided several functions to help users i. understand Layout function: A bird’s-eye view of the whole CHM factory how LM tools could improve the manufacturing process through an interactive use of can be obtained by the interface asprocesses shownand in quantify Figurethe processfloor simulation. In other words, users coulduser visualise production effectiveness of LM tool implementation via the simulation model. This section presentsruns some of 5. This shows how the manufacturing process of CHM these functions to clarify the features of the CHM factory model. i.

ii. ii.

with the flow of materials/products in the whole six sections

Layout function: A bird’s-eye view of the whole CHM factory floor can be obtained by the of the factory. user interface as shown in Figure 5. This shows how the manufacturing process of CHM runs Zoom-in/zoom-out function: For detailed view of each with the flow of materials/products in the whole six sections of the factory.

section of manufacturing processes, function could Zoom-in/zoom-out function: For detailed view of eachzoom-in section of manufacturing processes, zoom-in function could be deployed. Figure 10 to 12 show the detailed view of all sections be deployed. Figure 10 to 12 show the detailed view of all in CHM factory after deploying the zoom-in function. sections in CHM factory after deploying the zoom-in function.

Figure 10: Snapshot of S1 and S6 using zoom-in function Figure 10: Snapshot of S1 and S6 using zoom-in function

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Technology Development Figure 10: Snapshot of S1 and S6 using zoom-in function

11: Snapshots and S3 S3 using function Figure Figure 11: Snapshots of of S2S2and usingzoom-in zoom-in function

Figure 11: Snapshots of S2 and S3 using zoom-in function

12: Snapshots and S5 S5 using function Figure Figure 12: Snapshots of of S4S4and usingzoom-in zoom-in function iii.

iii.

iii.

Task Status function: For intuitive understanding of task status in each process, status illustrations are used function: in every WS. For By doing so, user understanding would be able to understand Task Status intuitive of taskthe changing task status in real time during the simulation runs. Figure 13 shows an status in each process,ofstatus are used inexample every of Figure 12: Snapshots S4 and S5illustrations using zoom-in function status illustration to show the distinction in task status between busy, idle, and fail.

WS. By doing so, user would be able to understand the

Task Status function: For intuitive understanding of task status in each process, status changing task status real time during simulation runs. illustrations are used in every WS.inBy doing so, user wouldthe be able to understand the changing task status in real time during the simulation runs. Figure 13 shows an example of Figure 13 shows an example of status illustration to show the status illustration to show the distinction in task status between busy, idle, and fail.

distinction in task status between busy, idle, and fail.

BUSY

IDLE

FAIL

Figure 13: Task status illustration BUSY IDLE FAIL iv. KPI status function: Key performance indicators (KPI), which include total production output, total production time, and C/O task time, are presented by means of KPI table and Figure 13: Task statusofillustration updated in real time during simulation. Snapshot KPI table for S2, S3, S4, S5, and S6 is Figure 13: Task status illustration shown in Figure 14. To quantify the effectiveness of LM tool implementation (in this case, SMED andfunction: CM), users areperformance prompted toindicators observe the difference KPI before after LM iv. KPI status Key (KPI), whichininclude total and production tool implementation an example), in Figure 15. The totaltable production output, total production(using time, S4 andasC/O task time, as areshown presented by means of KPI and updated in real time during simulation. Snapshot of KPI table for S2, S3, S4, S5, and S6 is shown in Figure 14. To quantify the effectiveness of LM tool implementation (in this case, SMED and CM), users are prompted to observe the difference in KPI before and after LM ISSN: 1985-7012 6 No. 2 July-December 2013 tool implementation (using S4 as an Vol. example), as shown in Figure 15. The total production

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iv.

KPI status function: Key performance indicators (KPI), which include total production output, total production time, and C/O task time, are presented by means of KPI table and updated in real time during simulation. Snapshot of KPI table for S2, S3, S4, S5, and S6 is shown in Figure 14. To quantify the effectiveness of LM tool implementation (in this case, SMED and CM), users are prompted to observe the difference in KPI before and after LM tool implementation (using S4 as an example), as shown in Figure 15. The total production output after implementation of SMED and CM are 109 units/ day and 105 units/day, respectively, compared to 100 units/ day before implementation. It is also shown that the total production output increases to 110 units/day when SMED implemented with These results, outputis after implementation together of SMED and CMCM. are 109 units/day and 105coupled units/day, respectively, compared to 100 units/day before implementation. also shown that the total with total production time results, justifyIt isthat the benefit of production output increases to 110 units/day when SMED is implemented together with CM. LMtotal tools can betime quantified using Theseimplementing results, coupled with production results, justify that simulation the benefit of methods. implementing LM tools can be quantified using simulation methods.

Figure 14: 14: Snapshot KPItable table S3,S5, S4,and S5,S6and S6 Figure Snapshotof of KPI forfor S2, S2, S3, S4,

WO-LM TOOL

W-CM

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W-SMED

W- SMED & CM

Figure Snapshot of KPI table S4 2(WO-without, W-with)2013 ISSN:15: 1985-7012 Vol. 6 for No. July-December

Technology Development Figure 14: Snapshot of KPI table for S2, S3, S4, S5, and S6

WO-LM TOOL

W-SMED

W-CM

W- SMED & CM

Figure 15: Snapshot of KPI tablefor for S4 S4 (WO-without, W-with) Figure 15: Snapshot of KPI table (WO-without, W-with) For sections sections with C/O process, the KPI tables as shown Figure An example is also For with C/O process, the are KPI tablesin are as16.shown in Figure provided using S4 to show the difference in C/O task time before and after SMED 16. An example is also provided using S4 to show the difference in C/O implementation (Figure 17). task time before and after SMED implementation (Figure 17).

Changeover time for each task

Internal time

Number of changeover occurrence External time

Total changeover task time

Workstations

Figure 16: Snapshot of KPI tablefor forS2, S2, S3, S3, and forfor C/OC/O task time Figure 16: Snapshot of KPI table andS4S4 task time

WO-SMED

W-SMED

Figure 17: Snapshot of KPI table for S4 showing C/O task time WO-SMED and W-SMED implementation ISSN: 1985-7012 Vol. 6 No. 2 July-December 2013 As mentioned before, KPI values in this simulation model are generated and updated in real time during simulation. This way, users could understand the effectiveness of LM tools by a trial-and-

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Figure 16: Snapshot of KPI table for S2, S3, and S4 for C/O task time

WO-SMED

W-SMED

Figure 17: Snapshot of KPI table for S4 showing C/O task time WO-SMED and W-SMED Figure 17: Snapshot of KPI table for S4 showing C/O task time WOimplementation

SMED and W-SMED implementation

As mentioned before, KPI values in this simulation model are generated and updated in real time during simulation. This way, users could understand the effectiveness of LM tools by a trial-anderror use of simulation and by conducting analysis. In addition, for visual As mentioned before, KPI valueswhat-if in this simulation model areunderstanding generated

and updated in real time during simulation. This way, users could understand the effectiveness of LM tools by a trial-and-error use of simulation and by conducting what-if analysis. In addition, for visual understanding of KPI, bar charts of KPI table are also available during simulation (Figure 18). Similarly, a sample of KPI graph that represents of KPI, chartsand of KPI table areLM also tools available during simulation (Figure 18). Similarly, a the KPIsbarwith without implementation is shown in Figure sample of KPI graph that represents the KPIs with and without LM tools implementation is 19. shown in Figure 19.

S1

S3

S5 S6 S4

S2 Figure 18: Snapshotof ofKPI KPI graph (bar charts) for all for sections of CHM factory Figure 18: Snapshot graph (bar charts) all sections of CHM factory

W-SMED

WO-LM TOOL

W-CM

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Figure 19: Snapshot of KPI graph for S4 with and without LM tools implementation

S2


Figure 18: Snapshot of KPI graph (bar charts) for all sections of CHM factory

W-SMED

WO-LM TOOL

W-CM

W- SMED & CM

Figure 19:Snapshot Snapshot of KPI for and S4 without with and without LM tools Figure 19: of KPI graphgraph for S4 with LM tools implementation implementation

4.0

CONCLUSIONS AND FUTURE WORK

By deploying a process simulation model of CHM factory, the impact of LM tools on the process performance of the factory could be quantified. This provides the management team of the factory a basis for decision making whether or not to implement a particular LM tool. Moreover, the idea of user understanding support, which was implemented in the model as several functions, also assists users in visualising the production processes and simulation outputs. This study picked up SMED and CM tool and implemented its simulation in the models. Future work includes considering the role of intelligent agent to provide decision support functionality to management teams in pursuing LM implementation. The agent would act as an expert assistant to the user in using LM tool implementation software. The design and development of an agent-based system will be presented in a separate paper. ACKNOWLEDGEMENT The researchers would like to thank the Malaysian Government, Universiti Teknikal Malaysia Melaka (UTeM), and University of Tokushima Japan for their financial support and provision of facilities to carry out this study.

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REFERENCES Abdulmalek, F.A. and Rajgopal, J. (2007). “Analyzing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study”, International Journal Production Economics, 107(1):223–236. Detty, R.B. and Yingling, J.C. (2000). “Quantifying benefits of conversion to lean manufacturing with discrete event simulation: a case study”, International Journal of Production Research, 38(2): 429-445. De Treville,S. and Antonakis,J.(2006). “Could lean production job design be intrinsically motivating?Contextual,configurational, and levels-ofanalysis issues. Journal of Operations Management, 24(2), 99-123. Ito, T., Mohamad, E. and Dani Yuniawan. (2013). “A process model of coolant hose factory for lean manufacturing simulation,” The Japan Society of Mechanical Engineer (JSME), No.13-6 Conference 2013, Manufacturing Systems Division, Tokyo, Japan, Vol.13, No.7, p.216, Mar. 2013. Kelton, W. D., Sadowski, R.P and Swets, N.B. (2010). Simulation with Arena, 5th ed., McGraw-Hill, International Edition. New York, NY. Kumar,R. and Kumar,V.(2012). “Lean Manufacturing System: An overview”, Proceedings of the National Conference on Trends and Advances in Mechanical Engineering, YMCA. University of Science & Technology, Faridabad, Haryana ,Oct 19th -20th 2012, pp.742-747. Ramakrishnan, S., Drayer, C.M., Tsai, P.F. and Srihari, K. (2008). “Using Simulation with design for six sigma in a server manufacturing environment”, Winter Simulation Conference, pp. 1904-1912. Rooda, J.E. and Vervoort, J. (2007). Analysis of Manufacturing Systems using χ1.0, Technische Universiteit Eindhoven, Department of Mechanical Engineering Systems Engineering Group, The Netherlands. Sevillano.F, Serna, M., Beltran,M. and Guzman, A.(2011). “ A simulation framework to help in lean manufacturing initiatives”, Proceedings 25th European Conference on Modelling and Simulation , Simulation in Industry, Business and Services (IBS 30), June 7th -10th 2011, Krakow, Poland. Standridge, C.R. and Marvel, J.R. (2006). “Why Lean Needs Simulation”, Winter Simulation Conference, pp. 1907-1913. Womack, J.P, Jones, D.T. and Roos, D. (1990). The Machine that Changed the World. HarperPerennial, NY, USA.

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