||Researchers of Six Sigma have often suggested that the quality improvement activities are continuous in order to meet customer satisfaction or requirement. Another important objective of a six sigma project was to gain the profit result from the decreased Cost of poor quality (COPQ) after improvement has been done. The most of enterprises were carrying out quality continuous improvement in order to increase productivity and profit margin. Six sigma was an effective tool to improve quality and ultimately the goal of Six Sigma is to move toward no variation in process. However, considering the extra investment that may be made in a six sigma project, it is important to identify the profit that is brought to the organizations. On the other hand, it wasn’t easily for a project leader or manager to understand the purpose and to set a threshold to decide when the improving was optimal. Now, we try to develop a model to evaluate the six sigma project decision making with a multi-objective programming model that will assist manager and project leader to decide process improving opportunities. It is a multi-objective goal programming model about calculating Sigma level for a typical Thin Film Transistor (TFT) process. The model considers a multi-stage process rolled throughput yield in a TFT Liquid Crystal Display (LCD) major Array, Cell and Module process. Meanwhile, the other factors such as investment cost, profit …and expected sigma level will be also taken in consideration in this model.|
Result of this study showed a multi-objectives goal programming model can calculate the optimal six sigma level for performing process alternative and the organization can reap the profit from it at the same time. We can determine to continue or to terminate the six sigma project to meet optimal quality from VOC with strategic consideration. When the organizations want to decide the optimal process alternatives, the organizations need to consider many constraints such as minimum cost investment, improvement budget, maximum sigma level and optimal process capability…etc. The multi-objective problems can be solved in this model with optimal solution! Optimal Sigma level decision making model can effectively help manager to decide whether to continue the six sigma project.
||1. Kumar, U. Dinesh; Nowicki, David; Ramírez-Márquez, José Emmanuel; et. al. On the optimal selection of process alternatives in a Six Sigma implementation International Journal of Production Economics Volume: 111, Issue: 2, February, 2008, pp. 456-467|
2. Chen, K.S.; Wang, C.H.; Chen, H.T., A MAIC approach to TFT-LCD panel quality improvement Microelectronics Reliability Volume: 46, Issue: 7, July, 2006, pp. 1189-1198
3. Donald P. Lynch, What is six sigma? University of Michigan Center for Professional Development.
4. Spencer Graves (1998) “Statistical Quality Control of a Multi-Step Production Process using Total Process Yield”, Quality Engineering, 11(2), pp. 187-195 (2001) “Six Sigma Rolled Throughput Yield” Quality Engineering, 14(2) (forthcoming)
5. M. Schneiderman, Optimum Quality Costs and Zero Defects: Are They Contradictory Concepts? Quality Progress, November 1986
6. Ching-Ter Chang *, A modified goal programming model for piecewise linear functions, 2002 Elsevier Science B.V., May 2001
7. Bernard W. Multicriteria Decision Making, Taylor III Introduction to Management Science 9th ed. P355-P388 2007.
8. Kalyanmoy Deb. Non-linear Goal Programming using Multi-Objective Genetic Algorithms. Technical Report CI-60/98, University of Dortmund, Germany, 1999.
9. Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters. Elsevier, Amsterdam, PAYS-BAS (1977) (Revue)
10. Kun-Lin Hsieh a,*, Yen-Sheng Lu b,1 Model construction and parameter effect for TFT-LCD process based on yield analysis by using ANNs and stepwise regression. Expert Systems With Applications Volume: 34, Issue: 1, January, 2008, pp. 717-724
11. Kalyanmoy Deb. Non-linear Goal Programming using Multi-Objective Genetic Algorithms. Technical Report CI-60/98, University of Dortmund, Germany, 1999.
12. Joly, M.; Pinto, J.M. Mixed-integer programming techniques for the scheduling of fuel oil and asphalt production Chemical Engineering Research and Design Volume: 81, Issue: 4, April, 2003, pp. 427-447
13. Hong Mo Yang, Byung Seok Choi, Hyung Jin Park, Min Soo Suh, Bongsug (Kevin) Chae Supply chain management six sigma: a management innovation methodology at the Samsung Group
14. F. Zhang and W. B. Roush1, 2002, Multiple-Objective (Goal) Programming Model for Feed Formulation:An Example for Reducing Nutrient Variation, Poultry Science Association,
15. Markarian, Jennifer, What is Six Sigma? Reinforced Plastics Volume: 48, Issue: 7, July - August, 2004, pp. 46-49
16. Ling Xu, Jian-Bo Yang, Introduction to Multi-Criteria Decision Making and the Evidential Reasoning Approach, Working Paper No. 0106, May 2001