More Accurate Knowledge Search in Technological Development for Robust Parameter Design
Abstract
Purpose: The causality search Taguchi (CS-T) method was proposed to support system selection in a robust parameter design. However, the target of the analysis is likely to be quasi-experimental data. This can be difficult to analyse with the CS-T method. Therefore, this study proposes a new analysis approach that can perform a more accurate knowledge search by applying the instrumental variable.
Methodology/Approach: Using the CS-T method, appropriate knowledge search is difficult with quasi-experimental data, including endogeneity. We examined an analytical process that addresses the endogeneity between mechanism and output by utilizing the control and noise factors that constitute the mechanism as instrumental variables.
Findings: The results show that 1) the proposed method has sufficient practical accuracy, even for quasi-experimental data including endogeneity; and 2) the extracted mechanism is less likely to fluctuate depending on the number of experimental conditions used. Moreover, we can clarify the position of the CS-T and proposed methods in system selection.
Research Limitation/Implication: We perform estimation under the assumption that the threshold is known. However, the extracted mechanism may change depending on the threshold; this requires discussing how to determine them.
Originality/Value of paper: Technological development requires a high degree of engineer sophistication. However, this study’s analytical process allows conducting more accurate knowledge search in a realistic and systematic way without requiring a high level of engineer input.
References
Brito, C. and Pearl, J., 2002. Generalized instrumental variables. Uncertainty in Artificial Intelligence, 18, pp.85-93.
Gamage, P., Jayamaha, N.P. and Grigg, N.P., 2017. Acceptance of Taguchi’s Quality Philosophy and Practice by Lean practitioners in apparel manufacturing. Total Quality Management & Business Excellence, 28(11-12), pp.1322-1338. DOI: 10.1080/14783363.2015.1135729.
Göhler, S.M., Ebro, M. and Howard, T.J., 2018. Mechanism and coherence of robust design methodology: A robust design process proposal. Total Quality Management & Business Excellence, 29(3-4), pp.239-259. DOI: 10.1080/14783363.2016.1180952.
Hosokawa, T. and Miyagi, Z., 2019. Quality engineering-based management: a proposal for achieving total optimisation of large systems. Total Quality Management & Business Excellence, 30(1), pp.182-194. DOI: 10.1080/14783363.2019.1665843.
Hosokawa, T., 2020. Taguchi Method for Technology Development CS-T Method for Exploring Generic Functions. Tokyo, Japan: Nikkagiren.
Hosokawa, T., Okamuro, A., Sasaki, Y. and Tada, K., 2015. A Proposal of Development Methodology Integrating Parameter design and T-Method. Journal of the Japanese Society for Quality Control, 45(2), pp.64-72. DOI: 10.20684/quality.45.2_194.
Inoh, J., Nagata, Y., Horita, K. and Mori, A., 2012. Prediction Accuracies of Improved Taguchi's T Methods Compared to those of Multiple Regression Analysis. Journal of the Japanese Society for Quality Control, 42(2), pp.265-277. DOI: 10.20684/quality.42.2_265.
Kawada, N., 2013. Application of Quality Engineering in Research and Development. J-TREC TECHNICAL REVIEW, 1, pp.38-45.
Kuroki, M., Miyakawa, M. and Kawata, R., 2003. Instrumental Variable(IV)Selection for Estimating Total Effects in Conditional IV Method. Japanese Journal of Applied Statistics, 32(2), pp.89-100. DOI: 10.5023/jappstat.32.89.
Lauritzen, S.L., 1996. Graphical models. Oxford: Oxford University Press.
Mashhadi, A.F., Alänge, S., Gustafsson, G. and Roos, L.U., 2016. The Volvo Robust Engineering system: How to make robust design work in an industrial context. Total Quality Management & Business Excellence, 27(5-6), pp.647-665. DOI: 10.1080/14783363.2015.1039938.
Meinshausen, N. and Bühlmann, P., 2010. Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), pp.417-473. DOI: 10.1111/j.1467-9868.2010.00740.x.
Myers, R.H., Khuri, A.I. and Vining, G., 1992. Response Surface Alternative to Taguchi Robust Parameter Design Approach. The American Statistician, 46(2), pp.131-139. DOI: 10.2307/2684183.
Rosenbaum, P.R. and Rubin, D.B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), pp.41-55. DOI: 10.2307/2335942.
Shimizu, S., Hoyer, P.O., Hyvärinen, A. and Kerminen, A., 2006. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research: J.M.L.R, 7, pp.2003-2030. DOI: 10.5555/1248547.1248619#sec-terms.
Taguchi, G., 1993. System Selection. Journal of Quality Engineering Society, 1(5), pp.2-8. DOI: 10.18890/qes.1.5_2.
Taguchi, G., 2004. Objective Function and Generic Function (2):―Product Development and Technology Development―. Journal of Quality Engineering Society, 12(4), pp.6-13. DOI: 10.18890/qes.12.5_6.
Authors
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