Applying Bayesian Network Techniques to Prioritize Lean Six Sigma Efforts

Applying Bayesian Network Techniques to Prioritize Lean Six Sigma Efforts

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) and Joseph H. Wilck (Department of Engineering, East Carolina University, Greenville, NC, USA)
Copyright: © 2013 |Pages: 15
DOI: 10.4018/jsds.2013040101
OnDemand PDF Download:


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.
Article Preview

Literature Review

Lean Six Sigma

The term “Lean Production” was coined by Womack, Jones, and Roos in their book, The Machine That Changed the World (Womack et al., 1990), based on a five-year study of automobile industry. In summary, Lean promotes a constant identification and elimination of any activities that add no value and cause extra costs within the manufacturing system. Lean is a methodology to help identify and reduce non-value added activities based on the definition of value to customer. According to Lean manufacturing, value is the change for which a customer is willing to pay. These include the function of actual product and any additional delighters such as fast delivery and satisfactory customer service, but not the time spend on transportation, motion, storage, excessive inventory, and any defects and rework. There are a variety of tools utilized in the Lean Production system which attempt to address the concern of manufacturing firms that are under pressure to emphasize the improvement on delivery, quality, and cost reduction. The framework of Lean philosophy wan summarized by Womack and Jones (1996) and more recent work includes Pettersen (2009), Nicholas (1998), Lamming (1996), Sawhney and Chason (2005), and Ellis et al. (2010). Lean techniques can also contribute to the performance improvements of other industries such as service sector (Arbós, 2002; Ali et al., 2012), healthcare (Chalice, 2007; Jenab & Staub, 2012; Huang et al., 2012), petroleum (Al-Husain et al., 2008), and construction (Alarcón, 1997).

Six Sigma is a collection of technical and managerial tools originally developed by Motorola to reduce variation and eliminate defects in electronic manufacturing processes. This philosophy then received huge successes at industry-leading companies such as General Electric (GE) and Allied Signal. Recent Six Sigma material includes Tennant (2001) and Thomas et al. (2009). The cornerstone of Six Sigma techniques is the application of scientific principles to manage business processes. Examples of the most widely used methods are Statistical Quality Control (SQC), and Design of Experiments (DOE).

Lean and Six Sigma are two major principles that address the efficiency and quality respectively and together they serve as fundamentals of the process improvement strategies. However, these two methodologies cannot be taken out of two packages and simply thrown into a single toolbox. Recent work has been published in the areas of Lean and Six Sigma integration; such as Thomas et al. (2009), Sawhney and Ehie (2006), Shah et al. (2008), and Hu et al. (2008).

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing