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Productivity improvement under manufacturing environment using Shainin system and fuzzy analytical hierarchy process: a case study

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Abstract

“Productivity is never an accident, it is always the result of a commitment to excellence, intelligent planning, and focused approach,” the phrase by Paul J. Meyer, an American businessman, has everything explained within it. Various quality improvement tools and techniques along with their integration have been attempted in the past for enhancing productivity levels in large-scale organizations across the globe. Similarly, new unification of these techniques can bring positive results even in a small- and medium-sized enterprise (SME). Authors, in this case study, use the synergy of two approaches, namely, “Shainin system” and “fuzzy analytical hierarchy process (AHP)” to enhance the productivity of a system. Shainin system stands close to a set of instruments that are clear to understand and easy to be applied, whereas AHP technique has a proven potential in decision-making and evaluation. Results, after successful implementation, indicate a monetary saving of $100,000, which is substantial for a SME. Emphasis is put on a coherent step-wise implementation of both the strategies and linking them together to inculcate the best potential outcomes.

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References

  1. Akao Y (1997) QFD: past, present, and future. In: International Symposium on Quality Function Deployment. p 19

  2. Aksu B, Baynal K (2010) Shainin and Taguchi methods and their comparison on an application. In: International Symposium on Computing in Science & Engineering. pp 801–809

  3. Antony J (1999) Spotting the key variables using Shainin’s variables search design. Logist Inf Manag 12:325–331. doi:10.1108/09576059910284140

    Article  Google Scholar 

  4. Arshinder, Kanda A, Deshmukh SG (2007) Coordination in supply chains: an evaluation using fuzzy logic. Prod Plan Control 18:420–435. doi:10.1080/09537280701430994

    Article  MATH  Google Scholar 

  5. Balan S (2015) Multi-response optimisation using Grey relational analysis and Shainin design of experiments Srinivasan Balan. Int J Qual Eng Technol 5:57–78

    Article  Google Scholar 

  6. Beg J, Shunmugam MS (2003) Application of fuzzy logic in the selection of part orientation and probe orientation sequencing for prismatic parts. Int J Prod Res 41:2799–2815. doi:10.1080/0020754031000095130

    Article  Google Scholar 

  7. Bhote K (1999) World class quality: using design of experiments to make it happen, 2nd edn. AMACOM Div American Mgmt Assn

  8. Bilgen B, Şen M (2012) Project selection through fuzzy analytic hierarchy process and a case study on six sigma implementation in an automotive industry. Prod Plan Control 23:2–25. doi:10.1080/09537287.2010.537286

    Article  Google Scholar 

  9. Boaden R (1997) What is total quality management… and does it matter. Total Qual Manag 8:25

    Article  Google Scholar 

  10. Chang C, Wu C, Chen H (2008) Using expert technology to select unstable slicing machine to control wafer slicing quality via fuzzy AHP. Expert Syst Appl 34:2210–2220. doi:10.1016/j.eswa.2007.02.042

    Article  Google Scholar 

  11. Chen C (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114:1–9

    Article  MATH  Google Scholar 

  12. Chen C, Klein C (1997) An efficient approach to solving fuzzy MADM problems. Fuzzy Sets Syst 88:51–67

    Article  MathSciNet  Google Scholar 

  13. Chen JK (2007) Utility priority number evaluation for FMEA. J Fail Anal Prev 7:321–328. doi:10.1007/s11668-007-9060-2

    Article  Google Scholar 

  14. Chen LH, Kao C, Kuo S et al (1996) Productivity diagnosis via fuzzy clustering and classification: an application to machinery industry. Omega 24:309–319. doi:10.1016/0305-0483(96)00002-3

    Article  Google Scholar 

  15. Cigolini R, Rossi T (2008) Evaluating supply chain integration: a case study using fuzzy logic. Prod Plan Control 19:242–255. doi:10.1080/09537280801916249

    Article  Google Scholar 

  16. Cox S, Garside J, Kotsialos A (2012) Concise process improvement—a process variation diagnosis tool. In: International MATADOR Conference. pp 1–5

  17. Cox S, Garside J, Kotsialos A, Vitanov V (2013) Concise process improvement definition with case studies. Int J Qual Reliab Manag 30:970–990. doi:10.1108/IJQRM-03-2012-0029

    Article  Google Scholar 

  18. Danijela T, Alev Taskin G, Slavko A et al (2013) An evaluation of quality goals by using fuzzy AHP and fuzzy TOPSIS methodology. J Intell Fuzzy Syst 25(3):547–556

    MathSciNet  Google Scholar 

  19. De Mast J, Schippers W, Does RJMM, Van den Heuvel E (2000) Steps and strategies in process improvement. Qual Reliab Eng Int 16:301–311

    Article  Google Scholar 

  20. Fazil BM, Kesavan R (2014) Vendor selection in supply chain management using ISM and ANP. IOSR J Mech Civ Eng 81–89

  21. Goodman J, Wyld DC (2001) The hunt for the red X: a case study in the use of Shainin design of experiment (DOE) in an industrial honing operation. Manag Res News 24:1–17. doi:10.1108/01409170110782919

    Article  Google Scholar 

  22. Gupta H, Garg D, Gupta R (2011) Applicability of just in time in technical institution: a survey. J Eng Technol 1:31–36. doi:10.4103/0976-8580.74564

    Article  Google Scholar 

  23. Gupta R, Verma PL, Manoria A, Bajpai L (2016) Simplifying six sigma methodology using Shainin DOE. Int J Adv Eng Res Dev 3:363–368

    Google Scholar 

  24. Hwang C-L, Lai Y-J, Liu T-Y (1993) A new approach for multiple objective decision making. Comput Oper Res 20:889–899. doi:10.1016/0305-0548(93)90109-V

    Article  MATH  Google Scholar 

  25. Inman RR, Blumenfeld DE, Huang N, Li J (2003) Designing production systems for quality: research opportunities from an automotive industry perspective. Int J Prod Res 41:1953–1971. doi:10.1080/0020754031000077293

    Article  Google Scholar 

  26. Jegadheeson AJ, Karunamoorthy L, Arun Kumar N et al (2012) Evolutionary approach in process improvement—a case study in auto electrical alternator manufacturing. J Adv Manuf Syst 11:27–50. doi:10.1142/S0219686712500035

    Article  Google Scholar 

  27. Kaushik P, Khanduja D, Mittal K, Jaglan P (2012) A case study: application of six sigma methodology in a small and medium-sized manufacturing enterprise. TQM J 24:4–16. doi:10.1108/17542731211191186

    Article  Google Scholar 

  28. Kaushik P, Mittal K (2015) A general model for problem solving in manufacturing or service organizations. J Eng Technol. doi:10.4103/0976-8580.158566

    Google Scholar 

  29. Kuhlang P, Edtmayr T, Sihn W (2011) Methodical approach to increase productivity and reduce lead time in assembly and production-logistic processes. CIRP J Manuf Sci Technol 4:24–32. doi:10.1016/j.cirpj.2011.02.001

    Article  Google Scholar 

  30. Lee A, Chen W-C, Chang C-J (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34:96–107

    Article  Google Scholar 

  31. Liu H, Liu L, Liu N, Mao L (2012) Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Syst Appl 39:12926–12934

    Article  Google Scholar 

  32. Logothetis N (1990) A perspective on Shainin’s approach to experimental design for quality improvement. Qual Reliab Eng Int 6:195–202

    Article  Google Scholar 

  33. Lu LYY, Wu CH, Kuo T (2007) Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. Int J Prod Res 45:4317–4331. doi:10.1080/00207540701472694

    Article  MATH  Google Scholar 

  34. Luthra S, Mangla SK, Xu L, Diabat A (2016) Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain. Int J Prod Econ. doi:10.1016/j.ijpe.2016.04.001

    Google Scholar 

  35. Mangla SK, Kumar P, Barua MK (2015) Risk analysis in green supply chain using fuzzy AHP approach: a case study. Resour Conserv Recycl 104:375–390. doi:10.1016/j.resconrec.2015.01.001

    Article  Google Scholar 

  36. Michalos G, Fysikopoulos A, Makris S et al (2015) Multi criteria assembly line design and configuration—an automotive case study. CIRP J Manuf Sci Technol 9:69–87. doi:10.1016/j.cirpj.2015.01.002

    Article  Google Scholar 

  37. Mittal K, Khanduja D, Kaushik P (2011) Leveraging APQP methodology to drive improvement in SME: a case study. Int J Contemp Pract 1:38–51

    Google Scholar 

  38. Mittal K, Tewari PC, Khanduja D, Kaushik P (2016) Application of fuzzy TOPSIS MADM approach in ranking & underlining the problems of plywood industry in India. Cogent Eng 3:1–11

    Article  Google Scholar 

  39. Mooren J, de Mast J, Does RJMM (2012) Quality quandaries*: the case of premature drill wear out. Qual Eng 24:354–359. doi:10.1080/08982112.2011.652584

    Article  Google Scholar 

  40. Mourtzis D, Papakostas N, Makris S et al (2008) Supply chain modeling and control for producing highly customized products. CIRP Ann - Manuf Technol 57:451–454. doi:10.1016/j.cirp.2008.03.106

    Article  Google Scholar 

  41. Onut S, Kara SS, Mert S (2009) Selecting the suitable material handling equipment in the presence of vagueness. Int J Adv Manuf Technol 44:818–828. doi:10.1007/s00170-008-1897-3

    Article  Google Scholar 

  42. Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156:445–455

    Article  MATH  Google Scholar 

  43. Prashar A (2016) Using Shainin DOE for six sigma: an Indian case study. Prod Plan Control 27:83–101. doi:10.1080/09537287.2015.1078515

    Article  Google Scholar 

  44. Prashar A (2015) Using Shainin DOE for six sigma: an Indian case study. Prod Plan Control 27:83–101. doi:10.1080/09537287.2015.1078515

    Article  Google Scholar 

  45. Rathi R, Khanduja D, Sharma SK (2015) Six sigma project selection using fuzzy TOPSIS decision making approach. Manag Sci Lett 5:447–456. doi:10.5267/j.msl.2015.3.009

    Article  Google Scholar 

  46. Rowlands H, Wang LIREN (2000) An approach of fuzzy logic evaluation and control in SPC. Qual Reliab Eng Int 16:91–98

    Article  Google Scholar 

  47. Saaty T (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26

    Article  MATH  Google Scholar 

  48. Saaty T (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83–98

    MathSciNet  Google Scholar 

  49. Shainin P, Shainin R (1997) Managing statistical engineering. In: 51st Annual Quality Congress Proceedings. pp 818–832

  50. Shainin R (1993) Strategies for technical problem solving. Qual Eng 5:433–438

    Article  Google Scholar 

  51. Shanmugam B, Kalaichelvan K (2014) Rejection reduction of vacuum pump type alternator assembly. IOSR J Mech Civ Eng 3:51–58

    Google Scholar 

  52. Singh RK, Khilwani N, Tiwari MK (2007) Justification for the selection of a reconfigurable manufacturing system: a fuzzy analytical hierarchy based approach. Int J Prod Res 45:3165–3190. doi:10.1080/00207540600844043

    Article  MATH  Google Scholar 

  53. Steiner S, MacKay R (1997) Strategies for variation reduction. Qual Eng 10:125–136

    Article  Google Scholar 

  54. Thomas A, Antony J (2004) Applying Shainin’s variables search methodology in aerospace applications. Assem Autom 24:184–191. doi:10.1108/01445150410529973

    Article  Google Scholar 

  55. Verma AK, Srividya A, Mannikar AV et al (2004) Shainin method: edge over other DOE techniques. IEEE Int Eng Manag Conf 3:1110–1113. doi:10.1109/IEMC.2004.1408864

    Google Scholar 

  56. Yadollahi Farsi J, SiahkaliMoradi J, Jamali B (2012) Which product would be chosen? A fuzzy VIKOR method for evaluation and selection of products in terms of customers’ point of view; case study: Iranian cell phone market. Decis Sci Lett 1:23–32

    Article  Google Scholar 

  57. Yin KH, Choo HL, Halim D, Rudd C (2013) Multi-response parameters optimisation for energy-efficient injection moulding process via dynamic Shainin DOE method. Key Eng Mater 554–557:1669–1682. doi:10.4028/www.scientific.net/KEM.554-557.1669

    Article  Google Scholar 

  58. Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci (Ny) 8:199–249

    Article  MathSciNet  MATH  Google Scholar 

  59. Zammori F, Gabbrielli R (2012) ANP/RPN: a multi criteria evaluation of the risk priority number. Qual Reliab Eng Int 28:85–104. doi:10.1002/qre.1217

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology, Kurukshetra, Haryana, India

    Kapil Mittal, Puran Chandra Tewari & Dinesh Khanduja

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  1. Kapil Mittal
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  2. Puran Chandra Tewari
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  3. Dinesh Khanduja
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Correspondence to Kapil Mittal.

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Mittal, K., Tewari, P.C. & Khanduja, D. Productivity improvement under manufacturing environment using Shainin system and fuzzy analytical hierarchy process: a case study. Int J Adv Manuf Technol 92, 407–421 (2017). https://doi.org/10.1007/s00170-017-0123-6

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