Lean Six Sigma Lean Six Sigma

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<ul><li> 1. Lean Six SigmaBy R.R Sawhney Ph D Ph.D. Department of Industrial and Information Engineering University of Tennessee, Knoxville406 East Stadium Hall Knoxville, TN 37996 865 974 7653sawhney@utk.edu </li></ul><p> 2. CPI-Center for Productivity &amp;InnovationsDr. Rupy Sawhney Dr. Xueping Li ChrisRobert Sirisha Yanzhen Sashi K. Joesph Gagan PaulBarbara Wright Keyser NukalaLiNaidu Stainback Rajpal CastoOwens Laigang YuerongWang Dengfeng JosephArun Naveed Ashutosh SongChen Jiao Yang Amalesh Balasundaram Ahmed Hengle ZeidKarthik El- El-AkkadSubburaman 3. Comparison of Lean and Six Sigma d What is Lean? What is Six Sigma? 4. THEN WHAT IS LEAN SIX SIGMA? 5. A LEAN SIX SIGMA PROBLEM 6. Effect of Variation on Flow 7. Simulation #1Push SystemFive workstations in a cell. The first workstation will never be starved. Every station on a line has the same level of variation. The average process time is the same for every machine center (10 time units). However, the individual process times are taken randomly from a normal distribution. Each machine on Line One has a coefficient of variation of 5% and each machine on Line Two has a coefficient of variation of 50%. All other parameters besides variation are identical for both lines. lines Parts are pushed through the system meaning that when a machine is finished with a part it will immediately travel to the queue for the succeeding machine. 8. Effect of Variability -Push SystemLead Times for Push SystemProcessStandard Variability1 2 34 5 6 7 8 910 Average Deviation5% 88.988.194.3104.795.1 101.995.187.888.7 97.0 94.1 5.91 10% 165.6 155.3 99.4120.3 122.6 138.9114.6 102.8 140.4208.4136.8 33.05 20% 186.7 168.3 149.4 184.6 236.2 235.0204.4 226.5 174.5237.4200.3 32.11 30% 242.4 254.1 394.3 257.9 308.6 358.6213.6 373.8 299.5235.6293.8 63.48 40% 359.8 521.8 382.7 360.4 212.3 419.2335.6 671.8 452.0422.3413.8 121.67 50% 464.9 431.2 366.2 381.9 568.8 525.4490.5 242.8 277.2486.9423.6 105.86AVERAGE LEAD TIMES PER COEFFICIENT OF VARIATION Co-efficient of variation 50%40%30%o 20% Series1 10%5%0 100 200 300400 500Flow Times (seconds) *Note: These values represent the average results of simulations replicated ten times for a given level of variation 9. Effect of Variability -Push System Average Throughput for Push SystemProcessStandard Variability1 234 567 8 910AverageDev5% 358.8 359359.1358.6358.4 359358.9 358.9 358.9 358.4358.8 0.25 10% 357.9 357.9357.8357.4357.6 358357.1 357.9 357.5 355.9357.5 0.63 20% 356.8 354.9354.7353.7353.2355.8 354.7 354.5 356.1 354.7354.9 1.08 30% 352.6 354.7352.6 352 353.7354.1 353.2 351.5 353.3 351.6352.9 1.07 40% 350.8 351348.7347.6345.2350.8 352.1 349.2 347.5 349.9349.3 2.08 50% 347.4 345.3349354.2344.4 348348.3 346.8 348345 346.7 1.65AVERAGE THROUGHPUT PER COEFFICIENT OF VARIATION50.0%oefficient of Variation40.0% 30.0% 20.0%Series1 10.0% C5.0% 340 345350 355 360Average Throughput(pieces/hours) *Note: These values represent the average results of simulations replicated ten times for a given level of variation 10. Effect of Variability -Push System Average Overall WIPProcess Standard Variability12 345 67 8 910 Average Deviation5% 8.9 8.89.410.59.410.2 9.58.88.9 9.79.4 0.59 10% 16.515.5 9.912.012.2 13.911.410.2 14.020.8 13.63.31 20% 18.5 18 5 16.716 714.814 8 18.3 18 323.5 23 5 23.523 520.520 522.622 6 17.4 17 423.5 23 5 19.919 93.213 21 30% 23.825.1 39.5 25.530.8 35.721.337.2 29.923.5 29.26.41 40% 35.651.5 37.8 35.620.5 42.433.167.7 44.841.9 41.1 12.44 50% 45.442.1 35.8 37.256.1 52.548.223.6 26.847.7 41.5 10.63AVERAGE OVERALL WIP PER COEFFICIENT OF VARIATION 50% oefficient of variation 40% v 30%20%Series110%C 5% 0.05.0 10.0 15.0 20.0 25.030.0 35.0 40.045.0Flow Times (seconds)*Note: These values represent the average results of simulations replicated ten times for a given level of variation 11. Simulation #2Pull System-Kanban Pull System KanbanFive workstations in a cell.cellThe first workstation will never be starved.Every station on a line has the same level of variation.The average process time is the same for every machine center (10 time units). However, the individual process times are taken randomly from a normal distribution.Each machine on Line One has a coefficient of variation of 5% and each machine on Line Two has a coefficient of variation of 50%.All other parameters besides variation are identical for both lines.th t b idi ti id ti l f b th li 12. Pull System- Kanban Pull System A WIP level of one unit is allowed between each station.WS1WS2 WS3The logic used to model this system is that a machine will work only when there is zero or one WIP between itself and the succeeding station. station If the queue between stations is equal to 2 the machine will not work. 13. Effect of Variability -Kanban Pull SystemLead Times For Kanban PullSystem Process StandardVariability1234 5678 9 10Average Deviation5% 71.2 68.9 70.8 66.871.8 67.9 68.3 66.471.4 71.969.52.1210% 70.7 68.7 71.1 70.573.2 71.1 69.6 72.772.0 72.471.21.4120% 72.3 71.2 73.6 73.673.3 73.7 72.6 73.573.6 73.573.10.8030% 75.6 75.5 76.8 77.974.5 78.0 77.0 76.175.1 74.776.11.2440% 81.1 80.7 81.4 78.877.8 79.1 79.6 78.778.7 78.979.51.1950% 81.3 82.3 82.3 83.779.2 82.4 84.8 81.183.8 82.382.31.57AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION50%C o -e ffi c i e n t o f v a r i a ti o n40% 30% 20% Series1 10% 5% 0 100200300400 500*Note: These values represent the average results of simulations replicated ten times for a givenFlow Times (seconds) level of variation 14. Effect of Variability -Kanban Pull System Throughput For Kanban Pull SystemProcess StdVariatio D n1 2 345 67 8 910 Average ev5% 356.2 356.9356.8357356.6 357 357356.5357.1356.8356.80.28 10% 351.8 352.6352.2353.4352.8 352.8 352.6353.1353.1353.4352.80.52 20% 338.5 340.4339.7341.3339.4 340.7 339338.9340.4339.6339.80.89 30% 322.2 321.7321.8323.2324.6 320.9 324.2320.2325.6322.3322.71.7 40% 305.1 300.5304.3304.6309.2 304.4 305304307.4306.5305.12.3 50% 285.3 283.8289288.4295.5 288.5 285.5288.6283.8289.1287.73.45 AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION50%Coefficient of v ariation40% 30% 20%Series1 10% 5% 0510 15 20 2530 3540 45 Flow Times (seconds)*Note: These values represent the average results of simulations replicated ten times for a given level of variation 15. Effect of Variability -Kanban PullSystem Average Overall WIP for Kanbang Process StandardVariability 1 23 4567 8 9 10 Average Deviation 5% 7.0 6.87.0 6.6 7.1 6.7 6.86.67.17.16.9 0.2110% 6.9 6.77.0 6.9 7.2 7.0 6.87.17.17.17.0 0.1420% 6.8 6.76.9 7.0 6.9 7.0 6.86.97.06.96.9 0.0830% 6.8 6.86.9 7.0 6.7 7.0 6.96.86.86.76.8 0.1140% 6.9 6.76.9 6.7 6.7 6.7 6.76.66.76.76.7 0.0850% 6.4 6.56.6 6.7 6.5 6.6 6.76.56.66.66.6 0.10AVERAGE FLOW TIMES PER COEFFICIENT OF VARIATION 50%ariation 40%Coefficient of va 30%20% Series110%5%0.0 005.0 50 10.010 015.015 020.020 0 25.0 25 0 30.030 0 35.0 35 0 40.040 0 45.0 45 0 Flow Times (seconds) *Note: These values represent the average results of simulations replicated ten times for a given level of variation 16. Simulation #3 Pull System CONWIPSystem-The same assumptions apply for the conwip system as did the pull system except for the following: A WIP level of seven units are allowed within the cell. cell The logic used to model CONWIP is that no part was allowed to enter the system until a part exited the system. This established the CONstant WIP. Within the system, the cell operates in the same y p manner as a push system 17. Effect Of Variability- CONWIP SystemLead Time for CONWIP ProcessStandardVariability12 34 5 67 8910Average Deviation 5% 70.370.3 70.570.470.3 70.4 70.470.4 70.370.3 70.4 0.0710% 71.471.3 71.371.471.2 71.2 71.371.3 71.371.3 71.3 0.0620% 74.274 274.474 4 74.4 74 474.7 74 774.1 74 1 74.374 3 74.6 74 674.1 74 1 74.674 674.974 9 74.4 74 4 0.250 2530% 78.278.3 78.778.078.0 78.0 78.578.1 78.678.7 78.3 0.2940% 82.782.3 83.082.683.0 82.3 82.882.9 83.282.3 82.7 0.3350% 87.886.7 86.887.187.9 87.1 86.987.9 87.087.4 87.3 0.47 AVERAGE LEAD TIMES PER COEFFICIENT OF VARIATION50%oefficient of variation40% 30% 20%Series1 10% C5%0.0 100.0200.0300.0400.0 500.0 Flow Times (seconds) *Note: These values represent the average results of simulations replicated ten times for a given level of variation 18. Effect Of Variability- CONWIP SystemThroughput For CONWIP Pull SystemProcess1 2 34 5 67 89 10 Average Standard 5%358.4 358.5357.5357.7 358.4 358.1 358 357.8358.4 358.5358.10.3610%353 353.6353.4353.1 354.1 353.8 353.3 353.3353.6 353.6353.50.3120%339.5 339 5 338.8 338 8338.6338 6337.6 337 6 340.3 340 3 338.9 338 9 338 340.1 340 1337.8337 8 336.6336 6338.6 338 61.151 1530%322.2 321.7320.5323.1 323.3 323.2 321 322.5320.8 320.1321.81.1940%305 305.7303.6305.4 303.9 306.3 304.2 304.3303 306.5304.81.1950%287 290.7290.4289.6 287 289.1 290 286.5289.8 288.1288.81.57AVERAGE THROUGHPUT PER COEFFECIENT OF VARIATION0.50.4 0.3 0.2Ser i es1 0.10.05 0 50 100150200250300350 400A v e r a ge T hr oughput ( pi e c e s / hour ) 19. Effect Of Variability- CONWIP System Overall Average WIP for CONWIP Process StandardVariability1 2 3 456789 10 Average Deviation 5%7.0 7.0 7.07.0 7.07.0 7.0 7.07.0 7.07.00.0010%7.0 7.0 7.07.0 7.07.0 7.0 7.07.0 7.07.00.0020%7.0 7.0 7.07.0 7.07.0 7.0 7.07.0 7.07.00.0030%7.0 7.0 7.07.0 7.07.0 7.0 7.07.0 7.07.00.0040%7.0 7.0 7.07.0 7.07.0 7.0 7.07.0 7.07.00.0050%7.0 7.0 7.07.0 7.0 7.0 7.0 7.0 7.0 7.07.00.00AVERAGE WIP PER COEFFICIENT OF VARIATION 50% Co-efficient of variation 40%30% f 20% Series110%5%0.05.0 10.0 15.020.0 25.0 30.0 35.0 40.0 45.0*Note: These values represent the average results of simulations replicated ten times for a givenFlow Times (seconds)level of variation 20. SummaryHigher levels of variation effect flow time, WIP, and throughput (capacity)Variation early in the push production system is more detrimental than variation late in the routing gVariation late in the kanban pull production system is more detrimental than variation early in the routingPull systems establish a WIP cap, that decreases flow time, while maintaining a similar throughput level (Little's Law).Variation of flow times is drastically reduced when using pullTradeoff of zero WIP is lost capacity for decreased flow timesWIP of one is more robust to variation 21. US Manufacturer Response to Global CompetitionHypothesisIs not Lean Six SigmaIs based on greater work performed by US workforce IsSource: National Institute for Occupational Safety and Health (NIOSH) 22. US Manufacturing In The NewsPlant Startups Manufacturing Income480 460Decline in real earnings in440 Number manufacturing by 9.1%420400 Global Competition pnationally3802003 2004 2005 Year Plant ClosuresManufacturing Jobs GM, Ford announces Decline in Manufacturingplant shutdowns andemployment by 59% since 60,000 layoffs1998Source: Bureau of Labor Statistics 23. How are Manufacturers Responding? 24. Manufacturing Employment TrendManufacturing Employment; 1995-2004 1995 2004120110 US Employment TrendCanada100 AustraliatJapan 90 GermanyUK E 80 701995 1996 1997 1998 1999 2000 2001 2002 2003 2004 YearSource: Bureau of Labor Statistics 25. Manufacturing Output Trend Manufacturing Output; 1995 20041995-2004200180US Canada160Australia Output Japan140Germany UK1201001995 1996 1997 1998 1999 2000 2001 2002 2003 2004 YearSource: Bureau of Labor Statistics 26. Less People Higher Productivity? 27. Strategy 1: Operational Excellence Lean, Agile Flexible Lean Agile, Flexible, Six Sigma, Automation Sigma MoveQueue Set- Up Process 2% 91%3% 4% Lead Time = 20 days Non Value AddedValue Added 28. Strategy 2: Lean Enterprise gypMoveSet- Upp ProcessLead Time = 1.8 daysNon Value AddedValue Added 29. Strategy 3: OutsourcingSource: Economic Policy Institute 30. Strategy 4: Global SupplierDevelopmentD lt Source: Economic Policy Institute 31. Strategy 5: Rapid New ProductIntroductionId i Average A 35.2 35 2Status Quo World-Class Manufacturers 32.6Delivery Value Top Performers p 17.7 ServiceManufacturing Source: www.industryweek.com Zeroing In On World Class D. Drickhamer,11/1/2001 32. Strategy 6: Premium Value gy 33. Strategy 7: Employees WorkingMore.Not SM N t Smarter? t ? What Workers Say About Stress on the Job One-fourth of employees view their jobs as the number one stressor intheir lives. -Northwestern National Life Three-fourths of employees believe the worker has more on-the-jobstress than a generation ago. -PrincetonSurvey Research Associates Problems at work are more strongly associated with health complaintsthan are any other life stressor-more so than even financial problems orfamily problems. -St. P l FireS Paul Fi and Marine Innsuance CoSource: National Institute for Occupational Safety and Health (NIOSH) 34. US Manufacturer Response to Global CompetitionHypothesis True???????Is not Lean Six SigmaIs based on greater work performed by US workforce IsSource: National Institute for Occupational Safety and Health (NIOSH) 35. What A Th Di Wh Are The Dimensions On Which Y iO Whi h You Design Continuous Improvement? 36. Do you design based on the following? Capacity Cit Sales Scheduling Capability Cp, Cpk, Cr, Pp, Ppk Cp Cpk Cr Pp Ppk Motivated and Skilled Workforce 37. Lean Six Sigma Definition D fi iti 38. Definition of Lean Six Sigma g Guidon Performance Solutions defines LeanSigma as the combination of LeanThinking and Six Sigma into a single, coordinated initiative, eliminating theguesswork about when and how to use these tools and eliminating months fromthe time it typically takes to implement them. http://www.guidonps.com/capabilities/lean_six_sigma.php 39. Why Lean Six Sigma Neither Lean nor Six Sigma can by themselves fulfill the operational improvement demands Lean and Six Sigma are required to meet the customer expectations The successful implementation of Lean will enhance the performance of Six Sigma and vice versa 40. Lean and Six SigmaContribution LeanSix Sigma Lean focuses on eliminating L f li i tiSix Sigma focuses on reducingSi Si f d i non-value added steps andvariation from the remaining value- activities in a processadded steps. Lean makes sure we are Six Sigma makes sure we are doing working on the right activitiesthe right things right the very first time Lean establishes the value flowSix Sigma makes the value flow as pulled by the customersmoothly without interruptionSource: Air Academy Associates 41. Integrating Lean &amp; Six SigmaSix Sigma will eliminate defects but it will not address the question of how to gq optimize process flow Lean principles exclude the advanced statistical tools often required to achieve the process capabilities needed to be truly 'lean Each approach can result in dramatic improvement, while utilizing both methods simultaneously holds the promise of being able to address all types of process problems with the most appropriate toolkit. Note: This chart is modified from a study done by Motorola Six Sigma Research Institute Source: Lean Sigma Institute 42. Roadmap to IntegrateLean &amp;...</p>