Applying Bayesian Network Techniques to Prioritize Lean Six Sigma Efforts

Yanzhen Li, Rapinder S. Sawhne and Joseph H. Wilck
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Yanzhen Li: Continuous Improvement Manager, H&V Collision Center, Colonie, NY, USA
Rapinder S. Sawhne: Department of Industrial and Information Engineering, The University of Tennessee, Knoxville, TN, USA
Joseph H. Wilck: Department of Engineering, East Carolina University, Greenville, NC, USA

International Journal of Strategic Decision Sciences (IJSDS), 2013, vol. 4, issue 2, 1-15

Abstract: In order to retain competitive advantages, many manufacturing organizations have applied Lean Six Sigma techniques to improve production processes. The general approach for implementing Lean Six Sigma is to perform various projects to tackle specific problems or areas. However, with the manufacturing system and its external environment becoming more and more complex, it is simply not possible to solve all the problems given the limited resources. The purpose of this study is to develop a model that provides a systematic evaluation for potential opportunities to enhance the effectiveness of Lean Six Sigma. Deriving from the Bayesian Network methodology, the proposed model combines a graphical approach to represent cause-and-effect relationships between events of interests and probabilistic inference to estimate their likelihoods in the area of process improvement. The developed model can be used for assessing the problems associated with Lean Six Sigma initiatives and prioritizing efforts to solve these problems.

Date: 2013
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