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Finding a probabilistic approach to analyze lean manufacturing

Hosseini Nasab, H., Aliheidari bioki, T., Khademi Zare, H.
Journal of cleaner production 2012 v.29-30 pp. 73-81
automobiles, decision making, manufacturing, neural networks, teams
Lean production is a multi-dimensional approach composed of management activities, just-in-time, cellular manufacturing, supplier management, work teams, and quality systems, hence implementing lean production is time consuming and costly. Organizations must be able to measure the success possibility of implementing lean production before embarking on it. In this paper, artificial neural network model has been designed and trained using analytical hierarchy process named A² which results in measuring the success of lean production implementation in an automobile company by determining the level of leanness. This approach leads to fewer calculations, faster and more accurate decision-making, less complexity, and the ability to analyze a lot of scenarios with only one or few judgments of decision makers while the effect of the subjective opinion of one single decision maker will be avoided. This proposed method is compared with the adaptive analytical hierarchy process approach which is suggested by Lin et al. in 2008 and is named A³. The implementation results show that these two methods are significantly valid for measuring the success of lean manufacturing by determining the level of leanness. Comparison of the two methods shows that although A3 has benefits, it also suffers from limitations, which can be avoided by the A² model, A2 also improves the time and cost needed for implementing in comparison.