Finding and Optimising the Key Factors for the Multiple-Response Manufacturing Process

With the advent of modern technology, manufacturing processes became so sophisticated that a single quality characteristic cannot reflect the true product quality. Thus, it is essential to perform the key factor analysis for the manufacturing process with multiple-input (factors) and multiple-output (responses). In this paper, an integrated approach of using the desirability function in conjunction with the Mahalanobis-Taguchi-Gram Schmit (MTGS) system is proposed in order to find and optimise the key factors for a multiple-response manufacturing process. The aim of using the MTGS method is to standardise and orthogonalise the multiple responses so that the Mahalanobis distance for each run can be calculated and the multi-normal assumption for the correlated responses can be relaxed. A realistic example of the solder paste stencil printing process is then used to demonstrate the usefulness of our proposed approach in a practical application

Optimization of Engineering Tolerance Design Using Revised Loss Functions

Engineering tolerance design plays an important role in modern manufacturing. Both symmetric and asymmetric tolerances are common in many manufacturing processes. Recently, various revised loss functions have been proposed for overcoming the drawbacks of Taguchi\u27s loss function. In this article, Kapur\u27s economic tolerance design model is modified and the economic specification limits for both symmetric and asymmetric losses are established. Three different loss functions are compared in the optimal symmetric and asymmetric tolerance design: a revised Taguchi quadratic loss function, an inverted normal loss function and a revised inverted normal loss function. The relationships among the three loss functions and process capability indices are established. A numerical example is given to compare the economic specification limits established by using the three loss functions. The results suggest that the revised inverted normal loss function be used in determining economic specification limits

An Empirical Study for Exploring the Relationship between Balanced Scorecard and Six Sigma Programs

Abstract The purpose of this study is to explore the relationship between Balanced Scorecard (BSC) and Six Sigma programs and to point out the critical issues for implementing a BSC program. The Delphi method and Analytical Hierarchy Process (AHP) are used to conduct the questionnaire survey. Having experience and knowledge in implementing both BSC and Six Sigma programs, seven well-known companies in Taiwan were selected to participate in two rounds of survey. The analysis results suggest that the implementation experience of a Six Sigma program assists in launching a BSC program, while the number of years in implementing a Six Sigma program has no significant impact in launching a BSC program. Moreover, the ranking of six perspectives and eighteen critical decision criteria highlighted in this research can be useful for implementing a successful BSC initiative