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Six sigma case study (bjs)

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of this six sigma's DMAIC Case Study performed by Dr. Bikram Jit Singh

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Six sigma case study (bjs)

  1. 1. SOME SIX SIGMA IMPERATIVES FOR INDIAN FOUNDRIES: A CASE STUDY By Bikram Jit Singh Professor MMDU Mullana
  2. 2. Main Focus 1. Introduction 2. Research Plan 3. Literature Survey 4. Research Gap 5. Problem Formulation 6. Objectives of Study 7. Research Work Findings 8. Case Study Based on DMAIC 9. Result Appraisal 10. Economic Analysis 11. Conclusions 12. Scope in Future 13. References 14. Publications From Work
  3. 3. FOUNDRIES OVER THE WORLD • 35000 foundries in the world with annual production of 90 MMT • Provide employment to about 20 million people. • Indian foundry industry is the fourth largest in the world. • There are more than 7,000 foundries in India INTRODUCTION
  4. 4. FOUNDRIES IN INDIA • 80% of foundries are in SME sector. • Sector employs 0.5 million people directly and 1.5 million indirectly. • Rs 7000 per ton produced to National exchequer by way of Excise and other levies.
  5. 5. TYPES OF FOUNDRIES IN INDIA
  6. 6. FOUNDRY CUSTOMERS
  7. 7. Performance Norms Indian Foundries Overseas Foundries Production per annum (%) 8 to 9 92 to 93 Rejection (%) 10 to 25 2 to 12 Capacity Utilization (%) 45 to55 60 to 75 Productivity (T/Man/Year) 12 to 20 100 to 120 (Japan) 50 to 55 (Germany) 10 to 30 (China) Energy Requirement (KWH/T) 700 to 900 400 to 600 Comparison of Indian and Overseas Foundries
  8. 8. • For global competitiveness, Indian industries need overall operational and service excellence and are extensively engaged in Quality Circles, TQM and ISO Certification (Panchal, 2010). However, these methods have failed to deliver required performance over the last decade or so (Sadagopan et al., 2005). • It has been seen that Six Sigma is not fully explored by Indian industries (Antony and Banuelas, 2002). With the reduction of geographical barriers and the pressure of competing in the global market, overall operational and service excellence has become a necessity for these industries to remain globally competitive (Rao and Rao, 2007). Quality Programmes Undertaken
  9. 9. • Satisfaction with other quality and productivity improvement initiatives turned out to be the strongest reason for not embarking on Six Sigma program, followed by lack of awareness and unsuitability of the initiative to their type of business (Montgomery, 2009). • Approximately 30 per cent of them have applied Six Sigma in their business operations and the remaining 70 per cent are yet to experience the Six Sigma initiative for a number of reasons (Sehwail and Yong, 2003).
  10. 10. SO… To check low productivity levels and for global competitiveness, this industry must strive hard to achieve overall operational excellence in business by looking for flexibility, shorter lead time and defects reduction; because only such a configuration of production system can fulfill the customer expectations. In this context Six Sigma can be a powerful business improvement strategy, that enables industry to use simple but powerful statistical methods for achieving operational excellence.
  11. 11. SIX SIGMA ?? A break through strategy to significantly improve customer satisfaction and shareholder value by reducing variability in every aspect of our business Improve Performance & Service • reduce defects • stabilize processes • customer satisfaction Reduce Costs • improve efficiency • eliminate waste (3.4 ppm) • reduce cost of poor quality
  12. 12. SIX SIGMAS PARADIGMS SN Years/Author Company / Unit Parameters Achievements; Financial Savings LARGE SCALE UNITS 1. 1986-2001 Motorola In-process defect level 200-times reduction; $15billions 2. 1996-1999 General Electric Turnaround time at repair shops 62% reduction; $2billions 3. 1998-2000 Honeywell Concept to shipment cycle time Reduced from 18 months to 8 months; $1.2 billions 4. 2000-2002 Ford recalls and production delays Reduced defects by 70%; $1billions 5. 2000-2002 Caterpiller Quality improvement, Reduced cost structure Reduced defects by 75%; Not Reported 6. 1999-2002 Our lady of Lourdes Medical Centre Hospital bed availability delays Bed availability reduced from 267 to 235 minutes (12% reduction); $66000
  13. 13. MEDIUM SCALE UNITS 1. Ingle and Roe (2001) Medium sized welding unit Optimization of welding process parameters Joint strength is increased by 26% and scrap work is reduced by 3% 2. Zu et al. (2008) A bulb manufacturing SME Improve the process and reduced the shell cracking of bulbs Sigma level increased from 3.1 to 4.5 3. Sharma (2003) Baxer Battery Limited Life of battery increased by 15% by proper utilizing of resources. Customer satisfaction and 12% annual increased in sale. 4. Holtz and Campbell (2004) Ford Motors Maintenance management by improving MTBF. Savings of $ 60000 achieved in six months. Sigma level improved from 1.4 to 2.1 5. Wang et al., et al. (2006) A medium scale IC engine manufacturing unit Improve the Cpk Process capability improved from 1.1 to 2.9
  14. 14. SMALL SCALE UNITS 1. Johnson (2002) Orange box Limited Office seat and furniture design £60000 saving 2. Basu (2004) A washing machine manufacturing unit Failure of drum bearings reduction in customer complaints and customer satisfaction achieved 3. Marti (2005) A Steel valves and fittings manufacturing unit Production planning improvement 50% improvement in delivery failures and a level of 2 sigma achieved 4. Desai (2006) Two wheeler automobile company Gear pinion noise Noise reduced by 13%, Sigma level increased from 3.134 to 3.944 5. Gadallah (2009) “P S Soot Girani Ltd” Maharashtra -a cotton mill Manufacturing system performance Sigma level improved result in production of premium quality yarn
  15. 15. MICRO SCALE UNITS 1. Byrne (2003) Carriage and Wagon Works Axle rejection 59% reduction in axle rejections 2. Wang et al. (2004) Tata Honeywell Limited Travel costs per kilometer per month Decreased travel cost 3. Flott (2000) General Electric company Keys for successful implementation of quality tools Finding Keys factors for successful implementation 4. Antony et al. (2007) A Bulb manufacturing unit Shell cracking of bulb Sigma level increased from 3.2 to 4.5 5. Garg (2010) Indian automobile sector Effective implementation of quality tools in Indian automobile sector Finding Success factors
  16. 16. RESEARCH PLAN OBJECTIVES OF THE STUDY LITERATURE SURVEY RESEARCH GAPS PROBLEM FORMULATIONS RESEARCH PLAN IMPLEMENTATION OF SIX SIGMA’S DMAIC METHODOLOGY EXECUTION OF EACH PHASE AS PER PROPOSED FRAMEWORK RESULT APPRAISAL CONCLUSIONS AND SCOPE FOR FUTURE WORK RESEARCH FINDINGS: A CASE STUDY
  17. 17. LITERATURE SURVEY The following issues were predominantly addressed during an extensive literature review on various aspects of Six Sigma and its application to foundry industry. These issues primarily covered in various journals, reference manuals, handbooks, text books, e-resources etc are: a) Six Sigma: Concept and Evolution b) Six Sigma in Manufacturing Sector
  18. 18. (a) SIX SIGMA-CONCEPT AND EVOLUTION A chronological review of the concept of Six Sigma is given in table Six Sigma- The Concept SN Author(s) Six Sigma as a Concept 1. Behara et al. (1995) Six Sigma is a rating that signifies “best in class”, with only 3.4 defects per million units or operations. A defect can be anything froma faulty part to an incorrect customer bill. 2. Katayama and Bennett (1996) Six Sigma is a rigorous data gathering and statistical analysis technique which can be used to pinpoint sources of errors and suggests ways of eliminating them. 3. Mikel (1997) Six Sigma is a systematic, highly disciplined, customer-centric and profit-driven organization-wide strategic business improvement initiative. 4. Hendricks and Kelbaugh (1998) A quality improvement program with a goal of reducing the number of defects to as low as 3.4 parts per million opportunities or 0.0003 per cent. 5. Hahn et al. (1999) Six Sigma is a quality improvement initiative and quality management framework which is disciplined for quality improvement through defect reduction. 6. Sanders and Hild (2000) Six Sigma methodologies link the tools and techniques in a sequential manner. Finally, Six Sigma creates a powerful infrastructure for training of Champions, Master Blac k Belts etc. 7. Ingle and Roe (2001) A philosophy that employs a well-structured continuous improvement methodology to reduce process variability and drive out waste frombusiness using various techniques.
  19. 19. 8. Antony (2002) A business performance improvement strategy that aims to reduce the number of defects – to as low as 3.4 occasions per million opportunities 9. Hong and Goh (2003) Six Sigma is a customer focus, data-driven, and robust methodology, which is well rooted in mathematics and statistics. It is based upon the phenomenon of defect reduction 10. Markarian (2004) Six Sigma is a business improvement strategy that seeks to find and eliminate causes of defects in business process by focusing on outputs that are critical for customers. 11. Marti (2005) Sigma is basically Greek term for variation. Six Sigma is extremely structured program whose prime objective is to improve business process by minimizing the variation. 12. Kwak and Anbari (2006) A business strategy used to improve business profitability, to improve the effectiveness and efficiency of all operations to meet or exceed customer needs and expectations 13. McCarty and Fisher (2007) Six Sigma becomes ingrained in the cultural context of the organization and represents a management and cultural philosophy of process orientation and continuous improvement. 14. Hamza (2008) Six Sigma is a process improvement methodology that aims to increase business performance through accurate focus. It is a symmetric approach to achieve improvement. 15. Snee (2009) A strategic, company-wide, approach focusing on variation reduction, projects have the potential of simultaneously reducing cost and increasing customer satisfaction 16. Pepper and Spedding (2010) Six Sigma is an approach to the improvement of the quality of products or services which strive for near perfection. I.e. not more then 3.4 DPMO.
  20. 20. Table depicts certain themes whose synergy with Six Sigma has created considerable interest among the researchers during the last 15 years. Themes Related to Six Sigma SN Themes References 1. Quality Approaches Hoerl (1998); Hild et al. (2000); Klefsjo et al. (2001); Sadagopan et al. (2005); Jones and Johnson (2009); Parsad and Reddy (2010) 2. Six Sigma Education Echempati and White (2000); Maleyeff and Kaminsky (2002); Sehwail and De Yong (2003); Rao and Rao (2007); Gijo and Scaria (2010) 3. DAMIC/DFSS Methodology Bayle et al. (2001); Kuei and Madu (2003); Antony (2006); Bandyopadhyay and Jenicke (2007); Montgomery (2009) 4. Tools and Techniques Hendricks and Kelbaugh (1998); Graves (2002); Banuelas and Antony (2003); Banuelas et al. (2005); Antony (2007); Harjac et al. (2008) 5. Belt System Henderson and Evans (2000); Ingle and Roe (2001); Andersson et al. (2006); Savolainen and Haikonen (2007); Schroeder et al. (2008)
  21. 21. 6. Benefits Behara et al. (1995); Johnson (2002); Kueiand Madu (2003); Chan et al. (2005); Das et al. (2006); Agarwaland Bajaj (2008) 7. Organizational Change Carnell and Lambert (2000); Brewer (2004); Craven et al. (2006); Davison and Al- Shaghana (2007); Fazzariand Levitt (2008) 8. Challenges Feld and Stone (2002); Goh and Xie (2004); Gijo and Rao (2005); Goh et al. (2006); Kumar (2007); 9. Success Factors Antony and Banuelas (2002); Antony and Fergusson (2004); Buch and Tolentino (2006); Chung et al. (2008) 10. Organizational Learning Carnell and Lambert (2000); De Feo and Bar-El(2002); Box (2006); Savolainen and Haikonen (2007);
  22. 22. SN Research Methodology References 1. Case-Focused Articles Smith (1993); Behara et al. (1995); Hoerl (1998); Does et al. (2002); Brett and Queen (2005); Horell and Snee (2010) 2. Review-Focused Articles Kuei and Madu (2003); Andersson et al. (2006); ; Rao and Rao (2007); Aggogeri and Gentili (2008) 3. Survey-Focused Articles Bhatnagar and Pandey (2005); Chakrabarty and Tan (2007); Chang and Wang (2008); Nabeel et al. (2010) Nature of Research Methodology Used for Six Sigma Among the surveyed articles of last 20 years, 63 percent were classified as empirical using either surveys or case studies, while about 37 percent of the articles were theoretical articles that usually employ extensive literature review to focus on the development of concepts, propositions, models or theory building.
  23. 23. (b) SIX SIGMA IN MANUFACTURING INDUSTRY SN Author(s) Company / Unit Parameters Achievements 1. Henderson & Evans (2000) General Electric Company Implementation as a quality tool. Reduced the cycle time at repair shops. Annual saving of $2 billion Process improvement done 2. Ingle and Roe (2001) Medium sized welding unit Optimization of welding process parameters Joint strength is increased by 26% and scrap work is reduced by 3% 3. Does et al. (2002) A bulb manufacturing SME Improve the process and reduced the shell cracking of bulbs Sigma level increased from 3.1 to 4.5 Table cites major works of the researchers related to application of Six Sigma in manufacturing sector during the past decade.
  24. 24. 4. Sharma (2003) Baxer Battery Limited Life of battery increased by 15% by proper utilizing of resources. Customer satisfaction and 12% annual increased in sale. 5. Holtz and Campbell (2004) Ford Motors Maintenance management by improving MTBF. Savings of $ 60000 achieved in six months. Sigma level improved from 1.4 to 2.1 6. Hu et al. (2005) A mediumscale IC engine manufacturing unit Improve the Cpk Process capability improved from 1.1 to 2.9 7. Sokovic et al. (2006) A gravity die casting unit Casting scrap reduced from 23% to 11% 40% reduction in manufacturing cost with annual savings of $72000 p.a. 8. Aggogeri and Gentili (2008) Cranberry Drinks Ltd. Improvement in packing process. DPMO level improved from 3011 to 178 only. 17% reduction in packing time. 9. Kuthe and Tharakan (2009) Wilson Tools Shorten the heat treatment time Roughly $10000 per year savings. 2% reduction in overall Lead time 10. Bewoor and Pawar (2010) A copper wire manufacturing plant Quality improvement in rolling operation Defect are decreased by 19% with in nine months of DMAIC project
  25. 25. • After an extensive literature review, it appears that the application of Six Sigma in companies other than the big multinational companies has been rare and inconsistent. Little evidence exists in literature on Six Sigma implementation in foundry industry, which is counted as the most important supplier to other manufacturing industries. Since SME industries are facing serious sickness levels, an attempt can be made to implement Six Sigma over various systems and sub systems to considerably improve their respective productivity levels. • Literature has also reported tremendous financial gains through Six Sigma in large manufacturing sector, so an attempt can be made to replicate it in foundry SME sector too. RESEARCH GAPS
  26. 26. PROBLEM FORMULATION • Govt. had introduced “Automotive Mission Plan” (AMP-2006) to rise the GDP by four times up to 2016. • Main focus of Govt. is on boosting Auto sector industries (that’s why Car production is increasing exponentially after 2006) • Due to which orders of foundries (as a major Supplier of most of parts) have also been elevated from last 6 to 7 years. • But Indian foundries showed their in-capabilities to supply ordered quantities because of poor performance & productivity levels.
  27. 27. IMPACT of AMP (2006-2016) • Auto industries are importing casted parts from other countries like; China, USA, Australia and Russia. • It means our domestic foundries are losing business because of their poor production and quality levels.
  28. 28. So Primarily, the major objectives of the present study are: • To formulate a comprehensive strategy for the successful implementation of Six Sigma in Indian foundry industry without ignoring its existing constraints. • To provide standard step by step methodologies to execute different phases of DMAIC projects successfully in Indian foundry environments. • To devise a strategy for selection of right tool/technique for right problem. • To demystify the different myths prevailing about Six Sigma in Indian SMEs after implementing it in a SME itself. OBJECTIVES OF THE STUDY
  29. 29. ABOUT ORGANIZATION • A case study has been carried out in a non-ferrous foundry at Federal Mogul India Limited Bhahadurgarh, Patiala (Punjab) Foundry has a covered area of about 50144 sq. m and was established in 1954. • It is a medium scale unit used to cast pistons for export and domestic market. It uses mostly semi-automatic die casting machines. The unit under study casts pistons of diameter range from 30mm to 300mm and is capable to manufacture 13 million pistons per annum. RESEARCH WORK FINDINGS
  30. 30. • Location: Patiala, Punjab, India • Product : Pistons, Pins (by semi-automatic die casting) • FM Share: 48.4% • TS 16949, • OHSAS 18001, • ISO 14001 FEDERAL MOGUL GOETZE INDIA LIMITED
  31. 31. LEADING CUSTOMERS
  32. 32. PISTON FOUNDRY MELTING NAAM Casting Cell Domestic Casting Cell HEAT TREATMENT
  33. 33. DIE - CASTING Alloy Si Cu Mg Ni Fe FM132 8.5~10.5 2.0~4.0 0.5~1.5 0.5 Max 1.0 FM425 14.8~18 1.9~2.9 0.5~1.0 0.35~0.8 0.85 FM413/CSA12/ AC8A 11~13.5 0.5~1.3 0.8~1.5 0.7~1.3 0.8 CSA18 16~19 0.8~1.5 0.8~1.5 0.8~1.5 0.75 CSA24 22~26 0.5~1.3 0.8~1.5 0.7~1.3 0.7 VERTICAL GRAVITY DIE CASTING (Twin Cavity Mould) VERTICAL GRAVITY DIE CASTING (Single Cavity Mould)
  34. 34. DEFINE • Problem • Voice of customer • Project charter • Project planning • Project scheduling • Snaps of problem • History of problem • COPQ matrix • Project goal • Process by SIPOC diagram MEASURE • Measure Cpk of process • Calculate present sigma level • Pareto charts to find reasons of scrap • Brainstorming by 5Ms • Access CTQs by Process mapping • Cause and effect matrix of CTQs • MSA • (Gauge R&R) • (Bias checking) • (Stability test) ANALYSE •Analysis of Shift dependency by Chi- square test •Analyse the variation of B.T. with Die coating thickness by One way ANOVA •Apply 2-Sample t-test to verify the effect of In-gate design on scrap •Analyse the impact of Operator-skill by 2- sample t-test • Multi-Regression analysis of Casting process- parameters • Fish bone -analysis of Metal sticking on pins of die • Why-Why analysis of Dimensional in - accuracy of machine variables IMPROVE • Optimization of process- parameters by Full factorial DOE • Continuous Improvement s by Kaizens • Poka-Yoke for machine & Die CONTROL • Evaluation of sigma level after improvement • X-bar R chart for controlling Bottom thickness variation of Piston. • P-charts to vigil overall scrap continuously under its control limits PHASES OF DMAIC STUDY
  35. 35. The first step was to precisely define the problem, keeping in mind business objectives, customer needs and feed back. This involves identification of Critical to Quality (CTQ) issues and other items that have an impact on quality and customer satisfaction. Major Tools Used Voice of Customer (QFD), Project Charter, Project Scheduler, Snaps of Problem, Historical analysis of problem, COPQ Matrix, Process by SIPOC Diagram and Project Goals. 1. DEFINE PHASE
  36. 36. PROPOSED FRAMEWORK FOR D-PHASE
  37. 37. Θ Θ Θ Θ ImportancetoCustomer EvaultionofVendors GoodProcessControls ReductioninScrapdueto CastingDefects LessDie/MachineSetuptime Implementationof MaintenanceSchedules EnsureAdherenceofQuality System GoodInspectionPlans Technology Upgradation/Innovation TrainingPlansforWorkForce EfficientSchedulingto completeProductionOrders Completeness Criteria M M H H M H M M L M 15 15 45 45 15 45 15 15 5 15 L M H M M M M M L L 5 15 45 15 15 15 15 15 5 5 M M M N L H H L M N 12 12 12 0 4 36 36 4 12 0 N M L H L L L H M M 0 15 5 45 5 5 5 45 15 15 L M H N N M M M M N 5 15 45 0 0 15 15 15 15 0 L L N N N L N H M L 3 3 0 0 0 3 0 27 9 3 H M H N L M M M M L 27 9 27 0 3 12 12 12 12 3 M H L N H M M M L N 15 45 5 0 15 15 15 15 5 0 M M H N L M L M N M 9 9 27 0 3 9 3 9 0 9 M M H L M M M M H H 15 15 45 5 15 15 15 15 45 45 IMPORTANCE RATING OF X's 106 153 256 110 75 170 131 172 123 95 10% Symbol Relationship Between X & Y Rating H Strong (H) 9 M Medium (M) 3 L Weak (W) 1 N No Relation (N) 0 Θ 5 3 5 3 5 5 4 3 5 5 6 5 4 3 Meet Deadlines/Schedules Strong Information System Reduce Rework TARGET DIRECTION 1 7 Satisfy Quality Initiatives (Overall Quality of the Product) Reduction in Production Cost Reduce Waste Develop Closer Supplier Relations Less Dimensional Problems Accountabilty of Supplied Product 2 Cycle Time Reduction Targer Directions More is Better Less is Better Specific Amount 125 48 117 130 230 150 VOC ( Machine Shop) TARGET VALUE OF CTC (Critical to Customer) FACTOR 128 155 78 23010 9 8 VOC (HOUSE OF QUALITY)
  38. 38. NON MEAUUREABLE DEFECTS CASTING DEFECTS
  39. 39. MEAUREABLE DEFECT (BT VARIATION)
  40. 40. 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% Jan Feb Mar Apr May Jun H-273 (Avg. 21.5%) H-749 (Avg. 20.9%) H-519 (Avg.22.5%) HISTORICAL ANALYSIS OF SCRAP
  41. 41. COPQ MATRIX Financial Parameters H-273 H-519 H-749 Average Scrap/Month 1,866 1,100 885 Scrap Cost/Piston 183 183 183 Total Rejection Cost/ Month 3,41,478 2,01,300 1,61,955 Total Rejection Cost / Year 40,97,736 24,15,600 19,43,460 Total Rejection Cost for H- family Pistons Annually 84,56,796
  42. 42. SIPOC DIAGRAM
  43. 43. Phases Scheduled Dates Actual Dates Start date End date Start date End date Define 01/07/10 15/7/10 01/07/10 15/7/10 Measure 16/7/10 15/8/10 16/7/10 15/8/10 Analyse 16/8/10 07/10/10 16/8/10 22/9/10 Improve 08/10/10 22/11/10 23/9/10 30/11/10 Control 23/11/10 31/12/10 01/12/10 31/12/10 Schedule Chart Scheduled dates are decided after brain storming with concerned people from production, maintenance and quality departments. Actual dates are filled as different phases were accomplished with time.
  44. 44. Measure phase is the second step after defining the problem which involves measurement system analysis, capability studies and finding performance gaps for the identified problem. Major Tools Used Sigma Calculator, Cpk study, Pareto Charts, FMEA Table, Cause and Effect Matrix, Gauge R&R study, Bias Checking and Stability Test. 2. MEASURE PHASE
  45. 45. PROPOSED FRAMEWORK FOR M-PHASE
  46. 46. PROCESS MAPPING OF FOUNDRY Step 5: F A B B,C C D E J,K,L I H G Steps 10.- Die preparation Input Classification 20.- Ingots transportation to foundry Critic 30.- Ingots storage (in Cell) Controlled 40.- Furnace charge Noise 50.- Melting 60.- Degassing and flux treatment A.- Die coating 70.- Holding time and impurity flotation B.- Furnace charge relation 80.- Start up casting cell machine C.- Molten metal temperature 90.- Pouring D.- Density Index (ID) 100.- Croppers E.- Cycle time 110.- AQFD F.- Pouring speed 120.- Visual inspection and baskets accommodation G.- AQFD 130.- Storage before heat treatment H.- Without visual defects 140.- Heat Treatment I.- Aging 150.- Q.A. Release J.- Microstructure 160.- Transportation to release material area K.- Hardness 170.- Storage before machining L.- Chemical analysis Write Down and Classify the Key Process Input CTQ's 10 20 30 40 50 60 ID=1,5 max. Si No 70 80 90 100 110120130 Si No Scrap 130140150 Si No Scrap 160 170 Die temperature Die coating density Spray gun Free of humidity Free of slag Charge relation (60 Ingot /40 scrap) Temperature Metal Temp. N2 Flow RPM Time Time Water cooling system Cycle time Pouring speed Ladle cleaning Metal temperature Die Coating Water cooling time Water coling temp. Ingate separation AQFD Free of visual defects Separate in baskets by cavity Temperature Time Chemical Analysis Microstructure Hardness Q.A. Release card 10 20 30 40 50 60 ID=1,5 max. Si No 70 80 90 100 110120130 Si No Scrap 130140150 Si No Scrap 160 170 10 20 30 40 50 60 ID=1,5 max. Si No 70 80 90 100 110120130 Si No Scrap 130140150 Si No Scrap 160 170
  47. 47. SIGMA CALCULATION (EXISTING) Production results of June-2010 Total number of Machined parts 8482 Scrap type Nos. Bottom Thickness Defect 480 Blow Holes 432 Cold Lap 120 Depression 120 Hydrogen Porosity 48 Shrinkage 457 Defective Pin Hole 210 Total Scrap in June 1866 Nos. of Opportunities 7 DPMO 31428 Sigma Level of Process 3.43 Yield (%) 76.96
  48. 48. 10.089.929.769.609.449.289.12 LSL USL LSL 9.5 Target * USL 9.9 Sample Mean 9.59583 Sample N 60 StDev (Within) 0.150559 StDev (O v erall) 0.210772 Process Data C p 0.44 C PL 0.21 C PU 0.67 C pk 0.21 Pp 0.32 PPL 0.15 PPU 0.48 Ppk 0.15 C pm * O v erall C apability Potential (Within) C apability PPM < LSL 333333.33 PPM > USL 83333.33 PPM Total 416666.67 O bserv ed Performance PPM < LSL 262219.76 PPM > USL 21678.76 PPM Total 283898.52 Exp. Within Performance PPM < LSL 324670.34 PPM > USL 74494.82 PPM Total 399165.15 Exp. O v erall Performance Within Overall Process Capability W.R.T. BT (BEFORE) Cpk STUDY
  49. 49. NUMBERS 26 19 52426 203 133 99 98 52 40 32 Percent 2.2 1.6 4.436.1 17.2 11.3 8.4 8.3 4.4 3.4 2.7 Cum % 94.0 95.6100.036.1 53.3 64.6 73.0 81.3 85.7 89.1 91.8 DEFECTS Other Cold lap Def. Blanks Inside Porosity Skirt Shrinkage Skirt Blow Holes Bottom Shrinkage Bottom Porosity Ring Zone Blow Holes Ring Zone Bottom thickness variation Shrinkage Ring Zone 1200 1000 800 600 400 200 0 100 80 60 40 20 0 NUMBERS Percent Pareto Chart of H-749 PARETO CHART FOR DEFECTS
  50. 50. CTQ PROCESS FACTORS Critic Control Noise 10 9 9 6 6 10 9 Defective blanksbottom BlowHoles ColdLaps Depression Hydrogen Porosity Shrinkage DefectivePin Hole S.NO Process Step Process Input 1 Design Bottom Seating Design 5 0 0 3 0 0 0 68 2 Design Gate Feeding Design 3 3 3 0 2 5 0 146 3 Die repair Dimensional accuracy of Die parts 5 0 2 5 0 0 0 98 4 Die repair Dimensional accuracy of casting Machine parts 4 0 1 3 0 0 0 199 5 Set up Die Temperature & Preheating 0 5 4 0 5 5 2 179 6 Set Up Die Cooling Connections 0 5 4 0 4 4 0 145 7 Set Up Die Coating Thickness 4 4 4 0 4 4 0 176 8 Set up Water Cooling Pressure too low 0 3 0 0 5 4 0 97 9 Set up Discharge of Cooling water inside die parts 0 5 3 0 3 5 0 140 10 Set up Vaccum Pressure in air vents 0 3 5 0 4 2 0 116 11 Set up Ratio of water to Dycote 0 3 4 0 3 2 0 101 12 Casting Low Pouring Speed 0 2 5 0 0 3 0 93 13 Casting Die Core grouping 3 0 0 2 0 0 0 42 14 Casting Non-continuity in casting process 0 3 5 2 5 5 0 164 15 Casting Metal Sticking on Pin 0 0 0 5 0 0 5 75 16 Pouring Casting Temperature too High 0 4 0 0 5 5 0 116 17 Pouring Casting Temperature too Low 0 4 3 0 5 5 2 161 18 Pouring Degassing Procedure & waiting time 0 5 2 0 4 5 0 137 19 Pouring Skill of Operator 3 1 3 1 2 2 2 122 270 450 432 126 306 560 99 CAUSE AND EFFECT MATRIX TotalEFFECTS Correlation of Input to Output Characteristic RATING OF IMPORTANCE TO CUSTOMER Actions Decided
  51. 51. GAUGE R&R STUDY (MSA) PART NAME :PISTON GAGE NAME : BT CHECKING INSTRUMENT CHARACTERISTICS:BOTTOM THICKNESS GAGE NO.: MBT-03 L.C 0.01 MM SPECIFICATIONS :0.40 MM DATA SHEET PART AVERAGE APPRAISER TRIALS 1 2 3 4 5 6 7 8 9 10 AVTAR SINGH 1 8.12 7.88 8.16 8.00 7.81 8.12 7.88 7.87 8.13 8.10 2 8.10 7.86 8.16 8.00 7.80 8.11 7.87 7.88 8.12 8.10 3 8.11 7.86 8.13 8.02 7.81 8.10 7.87 7.88 8.12 8.10 Average 8.11 7.87 8.15 8.01 7.81 8.11 7.87 7.88 8.12 8.10 Xa= 8.00 Range 0.02 0.02 0.03 0.02 0.01 0.02 0.01 0.01 0.01 0.00 Ra= 0.01 NACHATTAR SINGH 1 8.12 7.88 8.16 8.02 7.81 8.12 7.88 7.87 8.13 8.10 2 8.10 7.87 8.14 8.00 7.80 8.11 7.86 7.86 8.12 8.11 3 8.11 7.86 8.13 8.02 7.80 8.10 7.87 7.88 8.11 8.10 Average 8.11 7.87 8.14 8.01 7.80 8.11 7.87 7.87 8.12 8.10 Xb= 8.00 Range 0.02 0.02 0.03 0.02 0.01 0.02 0.02 0.02 0.02 0.01 Rb= 0.02 MOHINDER SINGH 1 8.10 7.88 8.16 8.00 7.81 8.12 7.88 7.87 8.13 8.10 2 8.10 7.90 8.16 8.00 7.82 8.11 7.90 7.88 8.12 8.11 3 8.12 7.86 8.13 8.02 7.81 8.12 7.87 7.88 8.12 8.10 Average 8.11 7.88 8.15 8.01 7.81 8.12 7.88 7.88 8.12 8.10 Xc= 8.01 Range 0.02 0.04 0.03 0.02 0.01 0.01 0.03 0.00 0.01 0.00 Rc= 0.02 16 Part Ave Xp 8.11 7.87 8.15 8.01 7.81 8.11 7.88 7.87 8.12 8.10 Rp= 0.34 17 Ra+Rb+Rc/N ofApraiser R= 0.0170 18 Max X-Min X=Xdiff Xdiff= 0.0047 19 R* D4 =UCLR UCL 0.044 20 R* D3 =LCLR LCL 0
  52. 52. Two-Way ANOVA Table With Interaction Source DF SS MS F P PartID 9 0.948327 0.105370 1433.24 0.000 Operator 2 0.000243 0.000122 1.65 0.219 PartID * Operator 18 0.001323 0.000074 0.92 0.564 Repeatability 30 0.002400 0.000080 Total 59 0.952293 Alpha to remove interaction term = 0.25 Two-Way ANOVA Table Without Interaction Source DF SS MS F P PartID 9 0.948327 0.105370 1358.39 0.000 Operator 2 0.000243 0.000122 1.57 0.219 Repeatability 48 0.003723 0.000078 Total 59 0.952293 Gage R&R %Contribution Source VarComp (of VarComp) Total Gage R&R 0.0000798 0.45 Repeatability 0.0000776 0.44 Reproducibility 0.0000022 0.01 Operator 0.0000022 0.01 Part-To-Part 0.0175487 99.55 Total Variation 0.0176285 100.00 Study Var %Study Var Source StdDev (SD) (6 * SD) (%SV) Total Gage R&R 0.008932 0.053590 6.73 Repeatability 0.008807 0.052844 6.63 Reproducibility 0.001485 0.008909 1.12 Operator 0.001485 0.008909 1.12 Part-To-Part 0.132471 0.794829 99.77 Total Variation 0.132772 0.796633 100.00 Number of Distinct Categories = 20
  53. 53. Part-to-PartReprodRepeatGage R&R 100 50 0 Percent % Contribution % Study Var 0.04 0.02 0.00 SampleRange _ R=0.01067 UCL=0.03485 LCL=0 1 2 3 8.2 8.0 7.8 SampleMean __ X=8.0047 UCL=8.0247 LCL=7.9846 1 2 3 10987654321 8.2 8.0 7.8 PartID 321 8.2 8.0 7.8 Operator 10987654321 8.2 8.0 7.8 PartID Average 1 2 3 Operator Gage name: B.T. Gauge Date of study : 02/08/10 Reported by : Bikram jit singh Tolerance: 0.40mm Misc: Components of Variation R Chart by Operator Xbar Chart by Operator Measure by PartID Measure by Operator Operator * PartID Interaction Gauge R & R for B.T. Gauge GAUGE R&R STUDY
  54. 54. BIAS STUDY PART NAME: H-273 Gaug Name & Immersion Pyrometer Parameter Temperature L.C. 1 degree Appraiser Name Sukhdev Singh - 50972 Reference Value. 755 Degree C TRIALS(n) 1 756 1.000 2 754 0.000 3 749 -6.000 4 750 -5.000 5 751 -4.000 6 752 -3.000 7 752 -3.000 8 754 -1.000 9 753 -2.000 10 753 -2.000 11 752 -3.000 12 752 -3.000 13 750 -5.000 14 747 -8.000 15 745 -10.000 751.33333 -3.600000 -3.66667 Plot Histogram related to the reference value Anayse the histogram for any special cause Max(Xi)-Min(Xi) Calculate s Reapatability( sr)= d2* 3.0956877 Where d2* & d2 is taken from the table(appendix C) d2*= 3.5533 Calculate Where n = no. of trials d2= 3.4179 sb 0.7993031 1.7189648 Lower limit -5.38563 Pyrometer is biased and need Caliberation Upper limit -1.94770 or repair BIAS n rs X Bias BIAS STUDY (MSA)
  55. 55. Date Feb 1 3 4 5 6 7 8 10 11 12 13 14 15 18 19 20 21 22 24 25 Time X1 X2 X3 X4 N/s M/s A/s N/s 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 X1 0.4 0.3 0.4 0.6 0.3 0.4 0.4 0.4 0.5 0.4 0.3 0.3 0.4 0.3 0.4 0.1 0.6 0.4 0.4 0.3 X2 1.2 0.2 0.3 0.4 0.4 0 0.5 0.3 0.3 0.3 0.4 1.2 0 0.4 0.3 0.2 0.4 0.5 0.4 0.4 X3 0.5 0.3 0.4 0.4 0.4 0.2 0.3 0.5 0.4 0.3 0.5 0.2 0.4 0.3 0.5 0.2 0.4 0.4 0.3 0.3 X4 0.2 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.6 0.5 2.3 0.5 0.4 0.5 0 0.5 0.4 0.4 0.3 Xbar 0.5750 0.3000 0.3500 0.4500 0.3750 0.2500 0.4000 0.4000 0.4000 0.4000 0.4250 1.0000 0.3250 0.3500 0.4250 0.1250 0.4750 0.4250 0.3750 0.3250 Range 1.000 0.200 0.100 0.200 0.100 0.400 0.200 0.200 0.200 0.300 0.200 2.100 0.500 0.100 0.200 0.200 0.200 0.100 0.100 0.100 Sub Group Size= 4 A2= 0.729 D3= 0 D4= 2.28 D2= Standard deviation (s) R = Gauge stability= s X100 % BIAS CONTROL CHART METHOD BIAS ULX = d2= Xdbar = = = LCLx = d2*= UCLr = tv,1-a/2 = Lower Limit = R bar = LCL = 0 alpha = 0.05 Upper Limit = Result Gage is not well stable 0.036220294 0.163 0.4075 2.05875 0.3350 0.76 2.06813 2.000 0.479612034 0.40750 0.16198208 28.26765674 0.6517 2.059 0.197873597 0.335388 R e a d I n g s Tolerance *2d R r =s g r b s s = STABILITY TESTING (MSA)
  56. 56. STABILITY TEST PART NAME : GAUGE NAME &L.C: Vac Tester TOLERANCE : MM MASTER VALUE: mm X=Averrage X UCL= X+A2R= LCL=X - A2R= R = average R UCL=D4xR LCL=D3xR 0 0.4075 0.3350 0.7645 CSA-18 alloy 0.70 0.16330.6517 0.00 0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 0.7000 0.8000 0.9000 1.0000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0.0000 0.5000 1.0000 1.5000 2.0000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Oneunit=Oneunit= ucl Lcl ucl Lcl MEAN & RANGE CHARTS
  57. 57. 3. ANALYSE PHASE The authenticity and impact realization of each SSV on scrap, is required to be judged by conducting suitable investigation under Analyze Phase. This phase helps to focus improvement efforts on those SSVs which can be highly significant for positive scrap reduction. Major Tools Used Table ahead gives a summary of various quantitative and qualitative techniques/tools used for analyzing the measured critical to quality (CTQ) process parameters.
  58. 58. ANALYSIS of SSVs Type of Analysis Technique used Name of Tool used SSV’s to be analysed Hypothesis testing (OFAT) Chi Square test Analysis of Shift dependency One way ANOVA Die coating thickness 2 Sample t-test In gate design (or Vol. of R&R) Operator skill Discharge of Cooling water MFAT Multi-regression analysis Alloy temperature Die temperature Delay time Graphical Analysis Interaction Plot Vaccume Pressure Qualitative technique Fish-bone analysis Metal sticking on a pin Why-why analysis Machine‘s dimensional In- accuracy ANALYTICAL PLAN
  59. 59. Null Hypothesis Ho: µ1 = µ2 = µ3 Actual Hypothesis Ha: µ1 ≠ µ2 ≠ µ3 Shifts Production Good Scrap Bottom thickness variation Blow hole Porosity Shrinkage Depression Cold lap Pin hole defect Night Shift (1) 128 102 26 13 4 1 3 2 1 2 Morning Shift (2) 124 100 24 8 6 2 3 1 2 2 Afternoon Shift (3) 127 101 27 10 2 3 5 2 3 2 ( Scrap depends upon the shifts) (Scrap is independent of Shifts) Chi-square Test TEST FOR SHIFT DEPENDENCY
  60. 60. Session Confirmation from Minitab Expected value = (Row obs. total) * (Column obs. total) Grand observation Total Expected Value of “B.T.” in Defect type of the N/S E = (26 X 31)/77 = 10.47 Chi-Square = (O-E) / E Chi-Square = (13-10.47) /10.47 = 0.613 2 2 Since P Value > 0.05 ; Reject Ha , Accept Ho Hence Scrap in piston foundry is independent of shifts B.T.vary B. hole Prsty Shrkg Dep. C. lap P.H. defect Total 1 13 4 1 3 2 1 2 26 10.47 4.05 2.03 3.71 1.69 2.03 2.03 0.613 0.001 0.520 0.137 0.058 0.520 0.000 2 8 6 2 3 1 2 2 24 9.66 3.74 1.87 3.43 1.56 1.87 1.87 0.286 1.365 0.009 0.054 0.200 0.009 0.009 3 10 2 3 5 2 3 2 27 10.87 4.21 2.10 3.86 1.75 2.10 2.10 0.070 1.158 0.382 0.339 0.035 0.382 0.005 Total 31 12 6 11 5 6 6 77 Chi-Sq = 6.150, DF = 12, P-Value = 0.908 Df= 2(n-1) n= no. Of sub groups CHI SQUARE CALCULATIONS
  61. 61. Null Hypothesis Ho: µ1 = µ2 = µ3 = µ4 Actual Hypothesis Ha: µ1 ≠ µ2 ≠ µ3 ≠µ4 Die coating thickness 50µm Die coating thickness 80µm Die coating thickness 110µm Die coating thickness 140µm 9.81 9.80 9.70 9.70 9.70 9.71 9.87 9.80 9.80 9.80 9.80 9.70 9.90 9.89 9.58 9.67 9.70 9.75 9.68 9.71 9.60 9.62 9.60 9.80 9.80 9.65 9.67 9.89 9.76 9.75 9.85 9.75 9.70 9.66 9.95 9.62 9.87 9.66 9.80 9.65 9.80 9.77 9.76 9.75 9.58 9.58 9.66 9.70 9.68 9.68 9.61 9.87 9.60 9.58 9.70 9.80 9.67 9.67 9.80 9.58 9.85 9.85 9.70 9.86 9.95 9.95 9.67 9.90 9.80 9.88 9.71 9.70 9.53 9.53 9.80 9.87 9.95 9.95 9.89 9.80 (Diecoating thickness do not effect Bottom thickness variation) (Diecoating thickness effects Bottom thickness variation) One way ANOVA to analyse B.T. defect with Die coating thickness ANOVA TEST FOR COATING THICKNESS
  62. 62. One-way ANOVA: Die coating , Die coating , Die coating , Die coating Source DF SS MS F P Factor 3 0.0054 0.0018 0.15 0.932 Error 76 0.9361 0.0123 Total 79 0.9415 S = 0.1110 R-Sq = 0.57% R-Sq(adj) = 0.00% Level N Mean St Dev Die coating thickness 50 20 9.7525 0.1217 Die coating thickness 80 20 9.7365 0.1236 Die coating thickness 110 20 9.7400 0.1021 Die coating thickness 14 0 20 9.7560 0.0935 Individual 95% CIs For Mean Based on Pooled St Dev Level -+---------+---------+---------+-------- Die coating thickness 50 (----------------*---------------) Die coating thickness 80 (----------------*---------------) Die coating thickness 11 (----------------*---------------) Die coating thickness 14 (---------------*---------------) -+---------+---------+---------+-------- 9.690 9.720 9.750 9.780 Df= n-1 (n= nos. of data population or groups) Since p value is coming out to be more than 0.05, so Null hypothesis is accepted. Hence in present case, Die- coating thickness do not effect the Bottom- thickness variation, substantially. ANOVA STATISTICS
  63. 63. ANOVA GRAPHS Die coating thickness 140µm Die coating thickness 110µm Die coating thickness 80µm Die coating thickness 50µm 10.0 9.9 9.8 9.7 9.6 9.5 Data Individual Value Plot of thickness 50µm, 80µm, 110µm, 140µm Die coating thickness 140µm Die coating thickness 110µm Die coating thickness 80µm Die coating thickness 50µm 10.0 9.9 9.8 9.7 9.6 9.5 Data Boxplot of thickness 50µm, 80µm, 110µm, 140µm 0.40.30.20.10.0-0.1-0.2-0.3-0.4 99.9 99 95 90 80 70 60 50 40 30 20 10 5 1 0.1 Residual Percent Normal Probability Plot (responses arethickness 50µm, 80µm, 110µm and 140µm)
  64. 64. Null Hypothesis Ho: µ1 = µ2 Volume of riser & runner has no impact on casting scrap Actual Hypothesis Ha: µ1 ≠ µ2 Volume of riser & runner has impact on casting scrap Trails With Existing volume of Runner & Riser (240-250 cubic cm) With Enlarged volume of Runner & Riser (260-270 cubic cm) 1 18% 15% 2 20% 16% 3 19% 14% 4 17% 12% 5 21% 13% 6 16% 11% Taken 50 pistons per trail to assess the actual scrap in %age Analysis of Scrap(in %age) with variation in Volume of Riser & Runner 2 Sample t-test 2 SAMPLE t-TEST FOR IN-GATE DESIGN
  65. 65. Conclusion: as P value is <0.05 So, Discard Ho and Accept Ha. It implies, there is a relation between Runner & Riser Volume and casting scrap. Two-Sample T-Test and CI: With Existing volume of , With Enlarged volume of Two-sample T for With Existing volume of Runner vs With Enlarged volume of Runner N Mean StDev SE Mean With Existing volume of 6 0.1850 0.0187 0.0076 With Enlarged volume of 6 0.1350 0.0187 0.0076 Difference = mu (With Existing volume of Runner) - mu (With Enlarged volume of Runner) Estimate for difference: 0.0500 95% CI for difference: (0.0259, 0.0741) T-Test of difference = 0 (vs not =): T-Value = 4.63 P-Value = 0.001 DF = 10 2(n-1) n= number of trails or group size t-TEST CALCULATIONS
  66. 66. 2 Sample t - TEST GRAPHS With Enlarged volume of RunnerWith Existing volume of Runner 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Individual Value Plot of Existing V/s Enlarged R & R With Enlarged volume of RunnerWith Existing volume of Runner 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Boxplot of Existing V/s Enlarged volume of R & R 23.00% 22.00% 21.00% 20.00% 19.00% 18.00% 17.00% 16.00% 15.00% 14.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 With Existing volume of Runner Percent Mean 0.185 StDev 0.01871 N 6 AD 0.136 P-Value 0.947 Probability Plot With Existing volume of R & R Normal 18.00%16.00%14.00%12.00%10.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 With Enlarged volume of Runner Percent Mean 0.135 StDev 0.01871 N 6 AD 0.136 P-Value 0.947 Probability Plot With Enlarged volume of R & R Normal
  67. 67. Conclusion: as P value is <0.05 So, Discard Ho and Accept Ha. It implies, Operator skill has an impact over scrap Two-Sample T-Test and CI: Semi-skilled Die operator, Skilled Die operator Two-sample T for Semi-skilled Die operator vs Skilled Die operator N Mean StDev SE Mean Semi-skilled Die operato 6 0.1833 0.0216 0.0088 Skilled Die operator 6 0.1367 0.0216 0.0088 Difference = mu (Semi-skilled Die operator) - mu (Skilled Die perator) Estimate for difference: 0.0467 95% CI for difference: (0.0189, 0.0745) T-Test of difference = 0 (vs not =): T-Value = 3.74 P-Value = 0.004 DF = 10 IMPACT OF OPERATOR SKILL
  68. 68. 24.00%22.00%20.00%18.00%16.00%14.00%12.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 Semi-skilled Die operator Percent Mean 0.1833 StDev 0.02160 N 6 AD 0.143 P-Value 0.934 Probability Plot of Semi-skilled Die operator Normal 20.00%18.00%16.00%14.00%12.00%10.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 Skilled Die operator Percent Mean 0.1367 StDev 0.02160 N 6 AD 0.143 P-Value 0.934 Probability Plot of Skilled Die operator Normal Skilled Die operatorSemi-skilled Die operator 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Individual Value Plot of Semi-skilled Die operator, Skilled Die operator Skilled Die operatorSemi-skilled Die operator 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Boxplot of Semi-skilled Die operator, Skilled Die operator GRAPHICAL RESULTS
  69. 69. 24.00%22.00%20.00%18.00%16.00%14.00%12.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 Discharge (3-5 LPM) Percent Mean 0.1817 StDev 0.02317 N 6 AD 0.191 P-Value 0.818 Probability Plot of Discharge (3-5 LPM) Normal 18.00%16.00%14.00%12.00%10.00% 99 95 90 80 70 60 50 40 30 20 10 5 1 Discharge (7-10LPM) Percent Mean 0.1383 StDev 0.01941 N 6 AD 0.329 P-Value 0.380 Probability Plot of Discharge (7-10LPM) Normal Discharge (7-10LPM)Discharge (3-5 LPM) 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Individual Value Plot of Discharge (3-5 LPM), Discharge (7-10LPM) Discharge (7-10LPM)Discharge (3-5 LPM) 22.00% 20.00% 18.00% 16.00% 14.00% 12.00% 10.00% Data Boxplot of Discharge (3-5 LPM), Discharge (7-10LPM) RESULTS FOR DISCHARGE OF WATER P = 0.007
  70. 70. Alloy Temperature Stoppage (IN Sec) Die Temp Total pistons Scrap %age of Scrap 755 50 239.7 10 4 40.0 757 85 251.8 5 2 40.0 760 45 240 8 3 37.5 763 5 265 7 0 0.0 764 35 273.6 13 2 15.4 765 120 283 17 6 35.3 752 90 276.7 10 2 20.0 754 10 250.2 10 3 30.0 Mean Multi Regression Data ANALYSIS OF CASTING PARAMETERS
  71. 71. REGRESSION STATISTICS Regression Analysis: %ageof Scrap versus Alloy Temp.,Stoppage(in secs) & Die Temp. Theregression equationis %ageof Scrap = 114 + 0.118 Alloy Temperature+0.306 Stoppage(in Sec) - 0.745 DieTemp Predictor Coef SE Coef T P Constant 114.4 476.2 0.24 0.822 Alloy Temperature 0.1184 0.6554 0.18 0.865 Stoppage(INSec) 0.30615 0.08153 3.76 0.020 DieTemp -0.7454 0.2130 -3.50 0.025 S = 7.70734 R-Sq = 83.5% R-Sq(adj)=71.1% Analysisof Variance Source DF SS MS F P Regression 3 1200.75 400.25 6.74 0.048 ResidualError 4 237.61 59.40 Total 7 1438.37 Source DF Seq SS Alloy Temperature 1 129.92 Stoppage(INSec) 1 343.56 DieTemp 1 727.28 As P>0.05 forAlloy temp. It implies it is already in control. As P<0.05 for Stoppage or delay It implies it effects Scrap positively. As P<0.05 for Die temperature It implies it also effects Scrap substantially. As P<0.05 for overall regression It implies, its input variables have impact on dependent variables.
  72. 72. INTERATCTION PLOT DATA Die No. Vaccume Pressure( in atm) Scrap (in %age) 1 0.2-0.3 14 1 0.4-0.5 16 1 0.6-0.7 17 1 0.8-0.9 21 2 0.2-0.3 15 2 0.4-0.5 15 2 0.6-0.7 18 2 0.8-0.9 20 3 0.2-0.3 12 3 0.4-0.5 14 3 0.6-0.7 16 3 0.8-0.9 19 4 0.2-0.3 14 4 0.4-0.5 19 4 0.6-0.7 21 4 0.8-0.9 22
  73. 73. Beside this parameter (Vaccume pressure) affects the scrap, but as we already working in the range of 0.2-0.3 bar, so scope of further reduction in Scrap by optimizing Vaccume Pressure, is not substantial 0.8-0.90.6-0.70.4-0.50.2-0.3 21 18 15 12 4321 21 18 15 12 Die No. Vaccume Pressure( in atm) 1 2 3 4 No. Die 0.2-0.3 0.4-0.5 0.6-0.7 0.8-0.9 (in atm) Pressure Vaccume Data Means
  74. 74. FISH BONE ANALYSIS (FOR PIN DEFECTS) Pin hole Defective Die Environment Measure Methods Material Machines Man bed management policies by HR Dept. Poor grief management Lack of trainig audits worries) Distraction (mental casual labours Faulty procedure to selct In adequate Training Zerking of hydraulic rams time Given less soldification parts Play in between the die Improper die closing Non flatness of M/c bed Moulds Haphazard movement of Fix the No criteria to control plan Absence of the coating No control over Selection of Improper material coating Lack of parting temperature of Low unskilled labour Deputation of training Scaracity of Process procedures Non-standard set-up supevision In adequate Absence of check lists procedure Lack of standard caliberation No check on needed gadgets Scaracity of maintenance of Poor identify No system to assessment Improper Risk installing Lack of MSA for enviornmental Absence of conditions Bad enviornmental Less space Bad state of 5-S Lack of illumination Poor working posture coating Wear & tear of coating on Pins Scarcity of before set-up Dirty Pins on Pins Metal sticking Pins Blunt edges of diameter Variable Pin
  75. 75. Out of control Causes With in control Causes Die Machine Material Method Man Measurement Enviornment VariablePin diameter Haphazard movementof Moulds Lowtemperatureof metal Lack of standard procedure In adequateTraining Lack of MSAfor installing instruments Poor working posture Bluntedges of Pins Non flatnessof M/c bed Lack of parting materialcoating Absenceof check lists Faulty procedureto selctcasuallabours Improper Risk assessment Lack of illumination Metalsticking on Pins Improper dieclosing Improper Selection of parting material In adequatesupevision Distraction (mentalworries) No system to identify deficiencies Bad stateof 5-S Dirty Pins beforeset-up Play in between thedieparts No controlover thecoating thickness Non-standard set-up procedures Lack of trainig audits Poor maintenanceof instruments Less space Scarcity of coating on Pins Given less soldification time Absenceof controlplan Scaracity of Processtraining Poor grief managementby HRDept. Scaracity of needed gadgets Bad enviornmentalconditions Wear &tear of coating Zerking of hydraulic rams No criteria to Fix therecoating period Deputation of unskilled labour bed managementpolicies No check on caliberation periods Absenceof enviornmentalsurveys ROOT CAUSE RESULTS
  76. 76. WHY - WHY ANALYSIS FOR M/C Improper seatingof Die’s bottom (B.T. variation) Bottom and catcher assembly mounted on sameRam Mis-alignment of Die’s bottom w.r.t. mould seat Play or mis- alignment of bottom carryingRam Bottom tooland catcher design of machineis faulty Operator’serror Die is not checked properlyby Die- fitter beforeset-up or installation Only fourcorner head padsare provided for bottom guidance Seating arrangementof Die’s bottom is not proper. Problem in Die design Faulty Die- checking/passing procedure Key reasons Non-criticalreasons
  77. 77. RESULT AFTER A-PHASE Type of Analysis Technique used Name of Tool used SSV’s to be analysed Hypothesis testing (OFAT) Chi Square test Analysis of Shift dependency One way ANOVA Die coating thickness 2 Sample t-test Volume of Runner & riser Operator skill Discharge of cooling water MFAT Multi-regression analysis Alloy temperature Die temperature Delay time Graphical Analysis Interaction Plot Vaccume Pressure Qualitative technique Fish-bone analysis Metal sticking on a pin Why-why analysis Machine’s dimensional in- accuracy OUTCOMES OF ANALYSE PHASE
  78. 78. 4. IMPROVE PHASE In this phase actions are piloted and real tolerances are established to deliver desired performance. Various suggestions and new activities are added during optimization of the out-put variable. Major Tools Used: Design of Experiments (DOE), Tanguchi’s Method, RSM, Poka-Yoke, Kaizen etc.
  79. 79. STEP-1 STEP-2 Improvement by optimizations Improvement by incorporating Error proofing Define objective of improve phase As prescribed from D, M and A phase Improve Phase Principles of Poka-Yoke, Pilot Studies DoE, RSM, Tanguchi Methods, Product/Process Simulation Technological innovation and overhauling Triz, Creativity, Quality Tools etc. Improvement through Continuous improvements Kaizen, Quality Circles, TPM, TQM, 5S Improvement by virtue of Training and knowledge Six Sigma Black Belt, Green Belt Training, Knowledge of Statistics PROPOSED FRAMEWORK FOR I-PHASE
  80. 80. TOOL USED SSV’s TO BE IMPROVED OPTIMIZATION WITH DESIGN OF EXPERIMENTS (DOE) Volume of Runner & riser Discharge of cooling water Die temperature Delay time POKA-YOKE Machine’s dimensional in-accuracy KAIZEN Metal sticking on a pin Operator Skill On-Job Training IMPROVEMENT PLAN
  81. 81. DESIGN OF EXPERIMENTS StdOrder RunOrder CenterPt Blocks Die Temp Discharge of water Delay Volume of R&R 3 1 1 1 -1 1 -1 -1 27 2 1 1 -1 1 -1 1 6 3 1 1 1 -1 1 -1 4 4 1 1 1 1 -1 -1 1 5 1 1 -1 -1 -1 -1 29 6 1 1 -1 -1 1 1 23 7 1 1 -1 1 1 -1 13 8 1 1 -1 -1 1 1 11 9 1 1 -1 1 -1 1 14 10 1 1 1 -1 1 1 9 11 1 1 -1 -1 -1 1 19 12 1 1 -1 1 -1 -1 24 13 1 1 1 1 1 -1 22 14 1 1 1 -1 1 -1 28 15 1 1 1 1 -1 1 25 16 1 1 -1 -1 -1 1 8 17 1 1 1 1 1 -1 16 18 1 1 1 1 1 1 12 19 1 1 1 1 -1 1 26 20 1 1 1 -1 -1 1 32 21 1 1 1 1 1 1 17 22 1 1 -1 -1 -1 -1 18 23 1 1 1 -1 -1 -1 2 24 1 1 1 -1 -1 -1 15 25 1 1 -1 1 1 1 20 26 1 1 1 1 -1 -1 10 27 1 1 1 -1 -1 1 5 28 1 1 -1 -1 1 -1 31 29 1 1 -1 1 1 1 7 30 1 1 -1 1 1 -1 30 31 1 1 1 -1 1 1 21 32 1 1 -1 -1 1 -1 Factors/ Levels Die Temperature oC Discharge of Water (Litre/ Minutes) Delay (Seconds) Volume of R&R (Cm3) Low 250 7 60 260 High 330 10 180 285
  82. 82. StdOrder RunOrder CenterPt Blocks Die Temp Discharge of water Delay Volume of R&R Scrap (%) 3 1 1 1 250 10 60 260 14 27 2 1 1 250 10 60 285 10 6 3 1 1 330 7 180 260 18 4 4 1 1 330 10 60 260 17 1 5 1 1 250 7 60 260 15 29 6 1 1 250 7 180 285 11 23 7 1 1 250 10 180 260 12 13 8 1 1 250 7 180 285 11 11 9 1 1 250 10 60 285 9 14 10 1 1 330 7 180 285 16 9 11 1 1 250 7 60 285 10 19 12 1 1 250 10 60 260 13 24 13 1 1 330 10 180 260 18 22 14 1 1 330 7 180 260 20 28 15 1 1 330 10 60 285 15 25 16 1 1 250 7 60 285 9 8 17 1 1 330 10 180 260 18 16 18 1 1 330 10 180 285 12 12 19 1 1 330 10 60 285 13 26 20 1 1 330 7 60 285 15 32 21 1 1 330 10 180 285 13 17 22 1 1 250 7 60 260 16 18 23 1 1 330 7 60 260 19 2 24 1 1 330 7 60 260 18 15 25 1 1 250 10 180 285 9 20 26 1 1 330 10 60 260 17 10 27 1 1 330 7 60 285 15 5 28 1 1 250 7 180 260 17 31 29 1 1 250 10 180 285 10 7 30 1 1 250 10 180 260 14 30 31 1 1 330 7 180 285 16 21 32 1 1 250 7 180 260 17 EXECUTION OF EXPERIMENTS
  83. 83. Factorial Fit: Scrap (%) versus Die Temp, Discharge of water, Estimated Effects and Coefficients for Scrap (%) (coded units) Term Effect Coef SE Coef T P Constant 14.281 0.1362 104.84 0.000 Die Temp 3.937 1.969 0.1362 14.45 0.000 Discharge of water -1.813 -0.906 0.1362 -6.65 0.001 Delay 0.438 0.219 0.1362 1.61 0.128 Volume of R&R -4.313 -2.156 0.1362 -15.83 0.002 Die Temp*Discharge of water 0.062 0.031 0.1362 0.23 0.821 Die Temp*Delay -0.187 -0.094 0.1362 -0.69 0.501 Die Temp*Volume of R&R 0.563 0.281 0.1362 2.06 0.056 Discharge of water*Delay -0.687 -0.344 0.1362 -2.52 0.026 Discharge of water*Volume of R&R 0.312 0.156 0.1362 1.15 0.268 Delay*Volume of R&R -0.187 -0.094 0.1362 -0.69 0.501 Die Temp*Discharge of water*Delay 0.187 0.094 0.1362 0.69 0.501 Die Temp*Discharge of water* -0.813 -0.406 0.1362 -2.98 0.009 Volume of R&R Die Temp*Delay*Volume of R&R -0.313 -0.156 0.1362 -1.15 0.268 Discharge of water*Delay* -0.313 -0.156 0.1362 -1.15 0.268 Volume of R&R Die Temp*Discharge of water*Delay* -0.437 -0.219 0.1362 -1.61 0.128 Volume of R&R S = 0.770552 PRESS = 38 R-Sq = 97.09% R-Sq(pred) = 88.36% R-Sq(adj) = 94.36% DOE ANALYSIS OF ORTHOGONAL MATRIX
  84. 84. 151050-5-10-15-20 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Effect Percent A Die Temp B Discharge of water C Delay D Volume of R&R Factor Name Not Significant Significant Effect Type ABD BC D B A Normal Plot of the Standardized Effects (response is Scrap (%), Alpha = .05) C BC ABD B A D 1614121086420 Term Standardized Effect 2.06 A Die Temp B Discharge of water C Delay D Volume of R&R Factor Name Pareto Chart of the Standardized Effects (response is Scrap (%), Alpha = .05) RELATIVE IMPACT OF CTQ FACTORS
  85. 85. ONE FACTOR AT A TIME ANALYSIS 330250 16 15 14 13 12 107 18060 16 15 14 13 12 285260 Die Temp Mean Discharge of water Delay Volume of R&R Data Means
  86. 86. TWO WAY INTERACTION PLOTS 107 285260 18 15 12 18 15 12 18 15 12 330250 18 15 12 18060 Die Temp Discharge of water Delay Volume of R&R 250 330 Temp Die 7 10 of water Discharge 60 180 Delay 260 285 of R&R Volume Data Means
  87. 87. 10 910 15 8 50 20 100 7 150 Scrap (%) Discharge of water Delay Surface Plot of Scrap (%) vs Discharge of water, Delay Delay Dischargeofwater 1801601401201008060 10.0 9.5 9.0 8.5 8.0 7.5 7.0 > – – – – – < 10 10 12 12 14 14 16 16 18 18 20 20 (%) Scrap Contour Plotof Scrap(%) vs Discharge of water, Delay SURFACE PLOT FOR BC
  88. 88. THREE WAY INTERACTION PLOT (ABD) 285 260 10 7 330250 Volume of R&R Discharge of water Die Temp 13.25 15.5010.25 9.50 17.50 18.7516.25 13.25
  89. 89. Scrap (y) = 57.4 + 0.03 (Die Temp) - 0.71 (Discharge of water) + 0.03 (Delay) - 0.19 (Volume of R&R) - 0.003 (Discharge of water * Delay) + 7.13 (Die temp. * Discharge * Volume of R&R) Scrap (y) = 57.4 + 0.03 (A) - 0.71 (B) + 0.03 (C) - 0.19 (D) - 0.003 (B * C) + 7.13 (A * B * D) OPTIMIZATION EQUATION
  90. 90. OPTIMIZATION BY RESPONSE OPTIMIZER Cur High Low0.99998 D Optimal d = 0.99998 Targ: 10.0 Scrap (% y = 10.0002 0.99998 Desirability Composite 260.0 285.0 60.0 180.0 7.0 10.0 250.0 330.0 Discharg Delay Volume oDie Temp [250.0] [9.9602] [136.3636] [281.7172]
  91. 91. Response Optimization Parameters Goal Lower Target Upper Weight Import Scrap (%) Target 8 10 20 1 1 Starting Point Die Temp = 250 Discharge of = 7 Delay = 60 Volume of R& = 260 Solution Die Temp = 250 Discharge of = 9.96019 Delay = 136.364 Volume of R& = 281.717 Predicted Responses Scrap (%) = 10.0002 , desirability = 0.999982 Composite Desirability = 0.999982 OPTIMIZATION RESULTS
  92. 92. Existing Feeding System with No Top Riser After Modification Feeding System with Top Riser Provided CHANGE IN RUNNER & RISER DESIGN
  93. 93. Existing semi-automatic die casting Machine POKA-YOKE FOR M/C
  94. 94. After improvement semi-automatic die casting Machine POKA-YOKE FOR M/C
  95. 95. VIDEOS OF MACHINE IMPROVEMENT
  96. 96. Four head Pads for sitting Full Head seat Provided For Head Sitting Existing Design Modified Design SEATING ARRANGEMENT (BOTTOM TOOL)
  97. 97. Graphite Coating on Die-Pins Die-Pins with Metal Sticking 1. Pins sorted in the Stand KAIZEN FOR DIE - PINS
  98. 98. 2. Cleaning of Pins by Buffing 3. Dipping of Pins in Caustic Soda solution for 2 hrs STANDARD CLEANING PROCEDURE
  99. 99. 4. Dipping of cleaned pins in Graphite solution (1:2) for 50 mins 5. Pins after Cleaning and Graphite Coating
  100. 100. Cell: H-273 Opistons Master Trainer: Shift: All Girish Kumar / Daljeet Singh Supervisor:DPS/SM/MIJ Status Date: 30.08.2010 51051 TARLOK SINGH To Be Trained for Temperature Checking of Holding Furnaces by 30 Sept. 51267 GURBAZ SINGH To be trained to shot blast and clean the die with shot blasting machine by 4 oct. 51807 PAWAN KUMAR to be trained to see temp by pyorometer by 30 sept. 51150 SUNIL KUMAR To be Trained on Full Self Inspection by 30 Sept. 51751 JASSA SINGH To be trained to do degassing and de slagging the molten metal by oct 5. 56771 GURMEET SINGH Trained the working on die casting machine 20 by 4 oct. DCM20 WASHINGMAN TEMPERATURE CHECKING SELFINSPECTION DCM9 DIEFITTER LINEHELPER/RING SHOTBLASTING COREMAKING Comments, Training Plans TRAINING MATRIX Capture Training Requirements Empty Square No training completed to date. and produces the quantity required by production targets. and is capable of training other team members. Knows and applies all job instructions and safety rules . . . and ensures quality according to work station job instructions.
  101. 101. 23-Dec2-Dec11-Nov27-Oct7-Oct16-Sep26-Aug5-Aug15-Jul 26 24 22 20 18 16 14 12 10 Date Scrap Actual Fits Variable Trend Analysis Plot for Scrap Linear Trend Model Yt = 25.20 - 0.518610*t DOE Implementation (7% reduction in scrap) Poka-yoke of M/C (2%) Kaizen (graphite coating of Pin 2% ) Training of human resource (1%) Target Scrap Parameters are getting Out of control SCRAP REDUCTION ACHIEVED
  102. 102. 5. CONTROL PHASE In this phase, process monitoring and corrective or preventive actions are documented and executed. Basically this phase tries to check and monitor the improved process and its parametric values. Major Tools Used Control Plan, X bar and R Chart for BT variation and p- Chart for overall scrap tracking.
  103. 103. STEP-1 STEP-2 Final capability is re-defined Checking the scope of achieved improvements Validate the measuring system Do necessary Calibration Control Phase Transfer of technique among similar areas Sigma Calculator, Cpk Study Sustaining the improved process Check Lists, Audit Sheets, 5S, Poka-Yoke, TPM, Patrolling teams etc. Monitoring and revision of control documents and risk management Control Charts, SPC, Control Plan, FMEA, CUMSUM Charts Financial benefits reviewed Cost and Benefit Analysis PROPOSED FRAMEWORK FOR C-PHASE
  104. 104. IMPROVED SIGMA LEVEL Production Results of Dec-2010 Total number of Machined parts 14079 Scrap type Nos. Bottom Thickness Defect 410 Blow Holes 320 Cold Lap 95 Depression 85 Hydrogen Porosity 40 Shrinkage 350 Defective Pin Hole 166 Total Scrap in December 1466 Nos. of Opportunities 7 DPMO 14875 Sigma Level of Process 3.67 Yield (%) 96.96
  105. 105. IMPROVED Cpk 9.909.849.789.729.669.609.54 LSL USL LSL 9.5 Target * USL 9.9 Sample Mean 9.71617 Sample N 60 StDev (Within) 0.0417718 StDev (O v erall) 0.0421897 Process Data C p 1.60 C PL 1.72 C PU 1.47 C pk 1.47 Pp 1.58 PPL 1.71 PPU 1.45 Ppk 1.45 C pm * O v erall C apability Potential (Within) C apability PPM < LSL 0.00 PPM > USL 0.00 PPM Total 0.00 O bserv ed Performance PPM < LSL 0.11 PPM > USL 5.39 PPM Total 5.50 Exp. Within Performance PPM < LSL 0.15 PPM > USL 6.58 PPM Total 6.73 Exp. O v erall Performance Within Overall
  106. 106. X BAR & R CHART FOR BT VARIATION 5:00 AM3:00 AM1:00 AM11:00 PM9:00 PM7:00 PM5:00 PM3:00 PM1:00 PM11:00 AM9:00 AM7:00 AM 1 0 .0 9 .8 9 .6 9 .4 T ime SampleMean __ X=9.72 UCL=9.9516 LCL=9.4884 1 21 11 0987654321 0 .8 0 .6 0 .4 0 .2 0 .0 Sample SampleRange _ R=0.4014 UCL=0.8488 LC L=0 11 1 Xbar-R Chart of BT
  107. 107. p- CHART FOR OVERALL SCRAP REDUCTION 30th Dec 27th Dec 24th Dec 21st Dec 18th Dec 15th Dec 12th Dec 9th Dec 6th Dec 3rd Dec 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 Time individualpvalues _ X=0.1043 UCL=0.1380 LCL=0.0707 111 1 1 1 1 1 1 1 p-value (np/n) chart
  108. 108. RESULT APPRAISAL Parameters Before After DMAIC DPMO 31,428 14,875 Cpk Index 0.21 1.47 Sigma Level 3.43 3.67 Scrap (%age) 21.7 10.4 Savings (Rs.) 30.7 Lakhs per Annum With mere 0.24 improvement in Sigma Level
  109. 109. • In Improvement Phase, four casting process parameters (i.e. Volume of R&R, Die temperature, Discharge of cooling water and Delay with in process) have been optimized to bring a significant reduction of 7% in scrap. The machine‟s dimensional in-accuracy has been enhanced by altering the design of machine through Poka-yoke principles and this helped to achieve a net reduction of around 2% in scrap. The metal sticking on pins has been avoided by introducing a standard process of pin cleaning by using Kaizen rules of continues improvement and hence bring a further eduction of 2% in scrap. Then a proposed training schedule has reduced the scrap by around 1%. So overall 11% to 12% (approximately) reduction in scrap has been achieved in Improvement Phase of the case study.
  110. 110. • Diversification of technology definitely increases the financial/non financial benefits. It leads to stability of system, which ultimately causes the customer satisfaction. After successful reduction of scrap for H-273 pistons, the same techniques and tools have been applied for other H-family pistons like H-749 and H-519 pistons that are similar as far as design, process parameters, casting machine and dies are concerned. • Total savings of around 30.7 lakhs per annum has been assessed after improving the sigma level by 0.24 only.
  111. 111. 1. H-273 pistons: Average Scrap Saved per month = 400 (Approx.) Scrap Cost per Piston = 183 (Approx.) Total rejection cost saved per month = 400*183 = Rs 73,200/- Total rejection cost saved per year = 73,200*12 = Rs 8, 78,400/- 2. H-519 pistons: Average Scrap Saved per month = 600 (Approx.) Scrap Cost per Piston = 183 (Approx.) Total rejection cost saved per month = 600*183 = Rs 1, 09,800/- Total rejection cost saved per year = 73,200*12 = Rs 13, 17,600/- 3. H-749 pistons: Average Scrap Saved per month = 400 (Approx.) Scrap Cost per Piston = 183 (Approx.) Total rejection cost saved per month = 400*183 = Rs 73,200/- Total rejection cost saved per year = 73,200*12 = Rs 8, 78,400/- ECONOMIC ANALYSIS
  112. 112. • The existing high scrap (21.7% approx.) has been defined as most serious issue as far as overall productivity of pistons is concerned in foundry and machine shop. • Casting scrap has been found primarily due to defects like; shrinkage, bottom thickness variation, blow holes, porosity, pin hole defect and cold lap. • Process parameters (SSVs) like; In-gate design, dimensional inaccuracy of machine, die temperature, die coating thickness, discharge of cooling water, metal sticking on pins, alloy temperature, shift dependency and operator skill have been measured as main reasons of above defects in piston castings. SUMMARY
  113. 113. • Although during Measurement System Analysis (MSA), Gauge R&R of BT gauge has been found ok, yet Immersion Pyrometer (Temp. measuring device) has been found Biased and Vac-tester (Metal density checker) was coming out to be un-stable over a span of one month only. So both the measuring equipments were repaired and calibrated suitably before starting Analyse phase. • After analysis of all the eleven SSVs with appropriate statistical tools, following seven critical process parameters have emerged as responsible for high scrap. These are In-gate design (Volume of Runner & Riser), operator skill, discharge of cooling water, die temperature, delay time, metal sticking on pins and dimensional in accuracy of machine.
  114. 114. • Earlier existing sigma level was 3.43 and after improvement it has been raised by 0.24 but even this has resulted in savings of around 30.7 lakhs per annum and is remarkable for a medium scale make-to-order foundry unit. • Overall scrap of foundry has been monitored by drawing shift wise p-chart and controlled up to 10.43% only, after Six Sigma case study. BT variation has also been monitored at casting machine itself by chalking out X bar & Range chart hourly. Cpk study has also been conducted by SQC operator after every 4 shifts for time to time process capability-checking of casting process with respect to bottom thickness variation.
  115. 115. • This study has significantly reaffirmed the efficacy of Six Sigma strategy in Indian foundry industry to reduce scrap/waste from the operations, thus greatly improving the production efficiency. It also demystify the myth that Six-Sigma is only meant for big industries and require a lot of effort money and training etc. • „Project based‟ approach for Six Sigma implementation (rather then planning or investing in different phases of Six Sigma approach) is more motivating and helps a lot to demystify various fears on Six Sigma. CONCLUSIONS
  116. 116. • Researchers have laid more emphasis on defining various tools and techniques of Six Sigma but „tool selection criterion‟ for each phase, as per the given constraints and environment, is not available to industries. • Due to lack of this vital information, usually wrong tools and techniques are chosen and so DMAIC project fails by moving in a wrong direction, with course of time. But this fear is eradicated because of step-wise proposed frameworks for each phase of DMAIC project, which also categorizes concerned tools. • The challenge for all organizations is to integrate Six Sigma into their core business processes and operations rather than managing it as a separate initiative.
  117. 117. FUTURE SCOPE • Beside non-ferrous foundries, Six Sigma approach can be explored for ferrous foundries to bring breakthrough in rejections and increase yield per annum. • Six Sigma can also be used in energy intensive foundries, as it not only enhances productivity by process improvement but also it is a step to create „zero defect foundries‟ which indirectly leads to huge energy savings. • It can be implemented in green sand foundries specifically in sand re-use plants for not only to reduce the lead time of process but also to cast cost effective castings.
  118. 118. • Apart from foundry industries, other manufacturing sectors like; forging, forming, welding and machining industries can also take benefit to lean their business operations. • Six Sigma should further be explored service sector like; hospitals, education institutes, banking, traffic etc in India. FUTURE SCOPE
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  125. 125. PUBLICATIONS OUT OF WORK (A) Papers ‘Published’ in International Journals 1. Singh, B.J. and Khanduja, D. (2010), DMAICT: A Roadmap to Quick Changeovers, International Journal of Six Sigma and Competitive Advantage, Vol.6, No.1/2, pp.31-52. 2. Singh, B.J. and Khanduja, D. (2010), SME Sector of Punjab (India): From Renaissance to Recession, International Journal of Indian Culture and Business Management, Vol. 3, No. 5, pp. 544-559. 3. Singh, B.J. and Khanduja, D. (2010), SMED: For Quick Changeovers in Foundry SMEs, International Journal of Productivity and Performance Management, Vol. 59, No. 1, pp.98-116. 4. Singh, B.J. and Khanduja, D. (2010), Synergy of Cross Functional Process Mapping and SMED for Quick Changeovers: A Case Study, International Journal of Science Technology & Management, Vol-2, No. 2, pp.107-116. 5. Singh, B.J. and Khanduja, D. (2011), Enigma of Six Sigma for Foundry SMEs in India: A Case Study, International Journal of Engineering Management and Economics, Vol. 2, No. 1, pp. 81-105. 6. Singh, B.J., Khanduja, D. and Singh, A. (2011), Demystifying MSA: A Structured Approach for Indian Foundry SMEs, International Journal of Quality and Innovation, Vol. 1, No. 3, pp. 217-236. 7. Singh, B.J. and Khanduja, D. (2011), Does Analysis Matter in Six Sigma?: A Case Study, International Journal of Data Analysis Techniques and Strategies, Vol. 3, No. 3, pp. 300-324. 8. Singh, B.J. and Khanduja, D. (2011), Introduce Quality Processes through DOE: A Case Study in Die Casting Foundry, International Journal of Productivity and Quality Management, Vol. 8, No. 4, pp. 373-397.
  126. 126. 9. Singh, B.J. and Khanduja, D. (2011), Design for Set-ups: A Step towards Quick Changeovers in Foundries, International Journal of Sustainable Designs, Vol. 1, No. 4, pp. 402-422. 10. Singh, B.J. and Khanduja, D. (2012), Essentials of D-phase to Secure the Competitive Advantage through Six Sigma, International Journal of Business Excellence, Vol. 5, No. 1/2, pp. 31-51. 11. Singh, B.J. and Khanduja, D. (2012), Ambience of Six Sigma in Indian Foundry SMEs-An Empirical Investigation, International Journal of Six Sigma and Competitive Advantage, Vol. 7, No. 1, pp. 12-40. 12. Singh, B.J. and Khanduja, D. (2012), Risk Management in Complex Changeovers through CFMEA: An Empirical Investigation, International Journal of Industrial and System Engineering, Vol. 10, No. 4, pp. 470- 494. 13. Singh, B.J. and Khanduja, D. (2012), Scope of Six Sigma in Indian Foundry Operations, International Journal of Services and Operation Management, Vol. 13, No.1, pp.65-97. 14. Singh, B.J. and Bakshi, Y. (2012), Six Sigma for Sustainable Energy Management: A Case Study, International Journal of Science Technology & Management, Vol. 2, No. 2, pp. 60-72. 15. Singh, B.J. and Khanduja, D. (2012), Developing Operation Measurement Strategy during Six Sigma Implementation: A Foundry Case Study, International Journal of Advanced Operation Management, Vol.4, No. 4, pp. 323-349. 16.Singh, B.J. and Khanduja, D. (2014), “Perspectives of Control Phase to manage Six Sigma implements: A Foundry Case”, International Journal of Business Excellence (IJBEX), Vol.7, No.1, pp. 88-111.
  127. 127. (B) Papers ‘Presented’ in International Conferences 1.Singh, B.J. and Khanduja, D. (2010), DMAIC(S): Incubates Core Competencies in Indian Foundry SMEs: An Empirical Study in State of Punjab, 2nd International Conference on Production and Industrial Engineering (CPIE-2010), NIT, Jalandhar, India, pp. 1443-53. 2.Singh, B.J. and Khanduja, D. (2011), Enhancing Competitiveness of Foundry SMEs through Design for Changeover (DFC): A Case Study, International Conference on Emerging Trends in Mechanical Engineering (ICETME-2011), Thapar University, Patiala, India, pp. 507-515. 3.Singh, B.J., Khanduja, D. and Jaglan, P. (2012), “Six Sigma for Sustainable Energy Management in Foundries: A Case Study”, SOM-2012 Conference, IIT, Delhi (Held in Dec 2012). 4.Bakshi, Y., Singh, B.J., Singh, S.S. and Singla, R. (2012), “Performance Optimization of Backup Power Systems through Six Sigma: A Case Study”, IETET-2012, GITA Institutes, Kurukshetra (Held in Nov 2012) 5.Sodhi, H.S., Singh, B.J. and Khanduja, D. (2012), “Behavior Study of Cutting Parameters on Material Removal Rate for a Non-Ferrous Material While Turning on a CNC Turning Center”, IETET-2012, Kurukshetra (Held in Nov 2012). 6.Singh, B.J. and Khanduja, D. (2013), “Leveraging Six Sigma Disciplines to Reduce Scrap in Indian Foundry SMEs”, 26th SEAANZ Conference, Sydney (NSW), Australia, pp. 6-24. (Held in July-2013)
  128. 128. (C) Papers ‘Presented’ in National Conferences 1. Singh, B.J. and Khanduja, D. (2009), Set-Up Time Reduction for Higher Productivity in Indian Foundries, Indian Institute of Foundry Men (IIF) organized by Chandigarh Chapter at Ludhiana, pp. 51-57. 2. Singh, B.J. and Khanduja, D. (2010), Exploring Set-up Management in Indian Foundries, Advances in Mechanical Engineering (AME-2010), Organized by B.B.S.B.C.E. Fatehgarh Sahib, Punjab, pp. 70-76. 3. Singh, B.J., Bakshi, Y. and Kaushik, P. (2011), Role of Six Sigma in Engineering Institutes: A Case Study, 41st ISTE Convention, India, pp. 7-20. 4. Sachin, Singh, B.J. and Dhull, V. (2013), “Six Sigma: From Concept to Implementation”, AMMM-2013 National Conference, Haryana.
  129. 129. THANKS

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