한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제10권11호
  • /
  • Pages.149-159
  • /
  • 2019
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

그룹 Fuzzy AHP와 GRA를 이용한 식스시그마 프로젝트 선정방안

Project Selection of Six Sigma Using Group Fuzzy AHP and GRA

  • 유정상 (가천대학교 산업경영공학과) ;
  • 최성운 (가천대학교 산업경영공학과)
  • Yoo, Jung-Sang (Department of Industrial Engineering, Gachon University) ;
  • Choi, Sung-Woon (Department of Industrial Engineering, Gachon University)
  • 투고 : 2019.10.22
  • 심사 : 2019.11.20
  • 발행 : 2019.11.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

식스시그마는 시장과 고객의 패러다임과 트렌드의 변화에 맞추어 모든 사업의 프로세스와 전략을 개선하는 경영 혁신운동이다. 식스시그마 프로젝트 선정에 관한 기존의 연구는 있으나 불완전한 정보환경 하에서 프로젝트 선정을 위한 연구는 거의 없다. 본 연구의 목적은 불완전한 정보 하에서 올바른 프로젝트 선정을 위해 통합 MCDM 기법을 적용 방법을 제안하는 것이다. 식스시그마 프로젝트 선정을 위해 4단계인 1) 평가기준 간 가중치 결정 2) 팀 멤버 간 전문역량의 상대적 중요도 결정 3) 프로젝트 선호도 척도 산정 4) 최종 프로젝트 우선순위 결정 등을 위해 그룹 Fuzzy AHP, 불완전한 정보환경 하에서의 비퍼지화 TrFN 변환, GRA의 통합기법을 제안하였다. 본 연구에서 제안한 식스시그마 프로젝트 선정단계의 적용방안에 대한 이해를 돕기 위해 수치예가 제시되었다.

Six sigma is an innovative management movement which provides improved business process by adapting the paradigm and the trend of market and customers. Suitable selection of six sigma project could highly reduce the costs, improve the quality, and enhance the customer satisfaction. There are existing studies on the selection of Six Sigma projects, but few studies have been conducted to select the correct project under an incomplete information environment. The purpose of this study is to propose the application of integrated MCDM techniques for correct project selection under incomplete information. The project selection process of six sigma involves four steps as follows: 1) determination of project selection criteria 2) calculation of relative importance of team member's competencies 3) assessment with project preference scale 4) finalization of ranking the projects. This study proposes the combination methods by applying group fuzzy Analytical Hierarchy Process (AHP), an easy defuzzified number of Trapezoidal Fuzzy Number (TrFN) and Grey Relational Analysis (GRA). Both of the weight of project selection criteria and the relative importance of team member's competencies can be evaluated by group fuzzy AHP. Project preferences are assessed by easy defuzzified scale of TrFN in case of incomplete information.)

키워드

참고문헌

  1. S. Choi. (2006). Classification of Six Sigma Innovation Process. Journal of the Korea Safety and Management Science, 8(4), 239-247.
  2. S. Lee, Y. Jo & Y. Kim. (2010). A Study on the Recognition Difference of the Success Factors of Six Sigma on the Line of Size. Journal of Digital Convergences, 8(2), 177-188.
  3. R. Banuelas, C. Tennant, I. Tuersley & S. Tang. (2006). Selection of Six Sigma Projects in the UK. The TQM Magazine, 18(5), 514-527. DOI : 10.1108/09544780610685485
  4. T. Yang & C. Hsieh. (2009). Six-Sigma Project Selection Using National Quality Award Criteria and Delphi Fuzzy Multiple Criteria Decision-Making Method. Expect Systems with Applications, 36, 7594-7603. DOI : 1 0.1016/j.eswa.2008.09.045 https://doi.org/10.1016/j.eswa.2008.09.045
  5. S. Sharma & A. R. Chetiya. (2016). Six Sigma Project Selection : An Analysis of Responsible Factors. International Journal of Lean Six Sigma, 1(4), 280-292. DOI : 10.1108/20401461011096069
  6. K. Mittal, P. C. Tewari & D. Khanduja. (2017). On the Right Approach to Selecting a Quality Improvement Project in Manufacturing Industries. Operations Research and Decisions, 1, 105-124., DOI : 10.5277/ord170106
  7. B. Kornfeld & S. Kara. (2013). Selection of Lean and Six Sigma Projects in Industry. International Journal of Lean Six Sigma, 4(1), 4-16. DOI : 10.1108/20401461311310472
  8. P. S. Mueller & J. A. Cross. (2018). Factors Impacting Individual Six Sigma Adoption. International Journal of Lean Six Sigma. DOI : 10.1108/IJLSS-04-2018-0040
  9. S. Ray & P. Das. (2010). Six Sigma Project Selection Methodology. International Journal of Lean Six Sigma, 1(4), 293-309. DOI : 10.1108/20401461011096078
  10. S. Sadi-Nezhad. (2017). A State-of-Art Survey on Project Selection Using MCDM Techniques. Factors Impacting Individual Six Sigma Adoption. DOI : 10.5267/j.jpm.2017.6.001
  11. R. Padhy. (2017). Six Sigma Project Selections : A Critical Review. International Journal of Lean Six Sigma, 8(2), 244-258. DOI : 10.1108/IJLSS-06-2016-0025
  12. J. Oh & S. Lee. (2019). A Movie Recommendation System Processing High-Dimensional Data with Fuzzy-AHP and Fuzzy Association Rules. Journal of Digital Convergences, 17(2), 347-353. DOI : 10.14400/JDC.2019.17.2.347
  13. B. Ahadian & A.G.M. Abadi. (2012). Six Sigma Pilot Project Selections Using an MCDM Approach. Management Science and Engineering, 6(1), 34-43. DOI : 10.3968/j.mse.1913035X20120601.1999
  14. M. Kumar, J. Antony & B. R. Cho. (2009). Project Selection and its Impact on the Successful Deployment of Six Sigma. Business Process Management Journal, 15(5), 669-686. DOI : 10.1108/14637150910987900
  15. S. K. Tiwari, R. K. Singh & S. C. Srivastava. (2013). Six Sigma Project Selection Using Extent Fuzzy AHP : An Empirical Study, International Journal of Research in Industrial Engineering, 2(2), 47-60.
  16. I. Otay & C. Kahraman. (2017). Six Sigma Project Selection Using Interval Neutrosophic TOPSIS. Proceedings of EUSFLAT-2017-The 10 th Conference of the EUROPEAN Society for Fuzzy Logic and Technology, 83-93.
  17. A. Hadi-Vencheh & A. Yousefi. (2018). Selecting Six Sigma Project : A Comparative Study of DEA and LDA Techniques. International Journal of Lean Six Sigma. 9(4), 506-522. DOI : 10.1108/IJLSS-11-2016-0067
  18. U. D. Kumar, H. Saranga, J. E. Ramirez-Marquez & O. Nowicki. (2007). Six Sigma Project Selection Using Data Envelopment Analysis. The TQM Magazine, 19(5), 419-441. DOI : 10.1108/09544780710817856
  19. S. Percin & C. Kahraman. (2010). An Integrated Fuzzy Multi-Criteria Decision-Making Approach for Six Sima Project. International Journal of Computational Intelligence Systems, 3(5), 610-621. DOI : 10.1080/18756891.2010.9727727
  20. P. Pangsri. (2015). Application of the Multi Criteria Decision Making Methods for Project Selection. Universal Journal of Management, 3(1), 15-20. DOI : 10.13189/ujm.2015.030103
  21. M. A. Ortiz, H. A. Felizzola & S. N. Isaza. (2015). A Contrast Between DEMATEL-ANP and ANP Methods for Six Sigma Project Selection : A Case Study in Healthcare Industry. BMC Medical Information & Decision Making, 15(Suppl 3), 1-12. DOI : 10.1186/1472-6947-15-S3-S3
  22. F. Wang, C. Hsu & G. Tzeng. (2014). Applying a Hybrid MCDM Model for Six Sigma Project Selection. Mathematical Problems in Engineering, 1-13. DOI : 10.1155/2014/730934
  23. G. Buyukozkan & D. Ozturkcan. (2010). An Integrated Analytic Approach for Six Sigma Project Selection. Expert Systems with Applications, 37, 5835-5847. DOI : 10.1016/j.eswa.2010.02.022
  24. S. Vinodh & V. Swarnakar. (2015). Lean Six Sigma Project Selection Using Hybrid Approach Based on Fuzzy DEMATEL-ANP-TOPSIS. International Journal of Lean Six Sigma, 6(4), 313-338. DOI : 10.1108/IJLSS-12-2014-0041
  25. R. K. Padhy & S. Sahu. (2011). A Real Option Based Six Sigma Project Evaluation and Selection Model. International Journal of Project Management, 29, 1091-1102. DOI : 10.1016/j.ijproman.2011.01.011
  26. V. Kalashnikov, F. Benita, F. Lopez-Ramos & A. Hernandez-Luna. (2017). Bi-Objective Project Portfolio Selection in Lean Six Sigma. International Journal of Production Economics, 186, 81-88. DOI : 10.1016/j.ijpe.2017.01.015
  27. A. S. Rayarikar. (2016). Using Big Data Analytics for Effective Six Sigma Project Selection. Master Thesis, Purdue University. DOI : open_access_theses/989
  28. R. Rathi, D. Khanduja & S. K. Sharma. (2015). Six Sigma Project Selection Using Fuzzy TOPSIS Decision Making Approach. Management Science Letters, 5, 447-456. DOI : 10.5267/j.msl.2015.3.009
  29. R. Rathi, D. Khanduja & S.K. Sharma. (2016). Efficacy of Fuzzy MADM Approach in Six Sigma Analysis Phase in Automotive Sector, Journal of Industrial Engineering International, 12, 377-387. DOI : 10.1007/s40092-016-0143-0
  30. A. Jafarian, M.S. Nikabadi & M. Amiri. (2014). Framework for Prioritizing and Allocating Six Sigma Projects Using Fuzzy TOPSIS and Fuzzy Expert System. Scientia Iranica Transactions E : Industrial Engineering, 21(6), 2281-2294.
  31. S. Choi. (2019). Implementation Strategy of Risk Evaluation Using Fuzzy DST/AHP, TOPSIS and Grey Theory in FMEA Under Uncertain Environment. Journal of the korea Management Engineers Society, 24(3), 65-87. https://doi.org/10.35373/KMES.24.3.5
  32. D.Y. Chang. (1996). Applications of the Extent Analysis Method on Fuzzy AHP. European Journal of Operational Research, 95, 645-655. DOI : 10.1016/0377-2217(95)00300-2
  33. C. B. Chen & C. M. Klein. (1997). A Simple Approach to Ranking a Group of Aggregated Fuzzy Utilities. IEEE Transactions on Systems Man and Cybernetics, Part B, 27(1), 26-35. DOI : org/10.1109/3477.552183
  34. S. Liu & Y. Lin. (2010). Grey Systems : Theory and Applications. Berlin Hidelberg : Springer Verlag. DOI : 10.1007/978-3-642-16158-2
  35. H. C. Liu, L. Liu, Q. H. Bian, Q. L. Lin, N. Dong & P. C. Xu. (2011). Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning Approach and Grey Theory. Expert Systems with Applications, 38, 4403-4415. DOI : 10.1016/j.eswa.2010.09.110
  36. K. Chin, Y. Wang, G. K. K. Poon & J. Yang. (2008). Failure Mode and Effects Analysis Using a Group-Based Evidential Reasoning Approach. Computers & Operations Research, 36, 1768-1779. DOI : 10.1016/j.cor.2008.05.002
  37. Korean Standards Association. (2018). Case of Excellent Quality Circle, Korean National Quality Award [Online]. http://Knqa.ksa.or.kr/knqa/2294/subview