Developing a Bayesian Network risk model to enhance Lean Six Sigma

In today's global market, manufacturing organizations are striving to improve their pro- duction performance in order to remain competitive advantages. For the past few decades, many efforts have been conducted by both researchers and practitioners to develop managerial and technical approaches to improve manufacturing processes. Among them, Lean and Six Sigma have become the two most recognized methodologies and together they comprise the primary components of process improvement strategies. However, with the manufacturing system and its external environment becoming more and more complex, a great range of risk factors can affect the results of the Lean Six Sigma initiatives. Consequently, the organization is constantly exposed to risks of not being able to generate a quality product to meet the customer's requirements. The existence of risk is often neglected because there is no easy way to perform the risk analysis for Lean Six Sigma activities due to their complexity. The purpose of this study is to develop a risk-informed model that provides a systematic evaluation for potential risks to enhance the implementation of Lean Six Sigma initiatives. The methodology derives from the Bayesian Network methodology and is incorporated with other risk management techniques. Combining graphical approach to represent cause-and-effect relationships between events of interests and probabilistic inference to estimate their likelihoods, Bayesian Network provides an effective method to evaluate the reliability of Lean Six Sigma. The developed model can be used for assessing the potential risks associated with Lean Six Sigma initiatives and prioritizing efforts to minimize their impacts. The model can serve as a primary component of the decision-making toolbox for maximizing the effectiveness of Lean Six Sigma initiatives and subsequently increasing the competitiveness of a manufacturing firm.