Applying Analytic Network Process for Optimal Lean Tool Selection: an Empirical Case Study

Abstract

The purpose of this research was to select an optimal lean tool by analytic network process (ANP) based on decision-making elements, namely eight decision criteria and seven different alternative lean tools. These decision-making elements have influences from other decision-making elements in external clusters, and receive impacts from decision-making elements in the same groups. The proposed method was applied with an empirical case study, which is a small and medium-sized enterprise in the electronic component manufacturing industry in Thailand. From the research result, it was found that ANP can be used to solve the problem of optimal lean tool selection effectively and rank the lean tools based on weights of decision-making elements in alternative cluster of limit matrix. Decision makers can compare decision-making elements in pairs and analyze relationships between the decision criteria and lean tools simultaneously. By applying the proposed method with the case study company, it found that the visual control was the most optimal in improving works based on the lean concept, and can increase productivity 12 percent as compared to the productivity before the improvement. Meanwhile, the other lean tools were less important based on their weights in descending order. The benefits of this study are to guide entrepreneurs in solving lean tool selection problems in order to reduce wastes during works and increase productivity continuously.