Prioritization of lean tools using gap analysis and analytic network process [ANP]

Authors

  • Hung-da Wan University of Texas at San Antonio
  • Aniket Mohan Sahasrabudhe University of Texas at San Antonio
  • Leonardo Rivera Universidad Icesi

DOI:

https://doi.org/10.18046/syt.v12i28.1746

Keywords:

Lean Manufacturing, Analytic Network Process, Analytic Hierarchy Process, prioritized list of lean tools

Abstract

Decision making can be a complicated process, especially when we need to consider a large number of factors. In the case of companies implementing Lean Manufacturing, the vast variety of lean tools makes it harder to select the right tools for the right system at the right time. In this paper, a systematic approach is proposed to assist the decision making process in two steps. In the first step, a Gap Analysis is carried out to compare the current state of the system with a benchmark to identify the deficiency of performance in various categories. The second step employs the Analytic Network Process (ANP) to prioritize lean tools by evaluating the needs and urgency of improvements. We use ANP to prioritize a list of lean tools that need to be implemented urgently considering the current status of a manufacturing firm. A hypothetical case study demonstrates that the proposed decision making approach is capable in selecting lean tools that are applicable, suitable, and urgently needed according to user’s inputs.

Author Biographies

  • Hung-da Wan, University of Texas at San Antonio
    Assistant Professor, Department of Mechanical Engineering (University of Texas at San Antonio). Received a Ph.D. in Industrial & Systems Engineering (Manufacturing Systems Engineering Option) from Virginia Polytechnic Institute and State University, Virginia Tech (2006), a M.Sc. in Industrial Engineering (2006) and a B.S. in Mechanical Engineering (1994), both from National Taiwan University, His areas of interest are: Sustainability of manufacturing systems; Lean Manufacturing Systems: assessment, value stream mapping and engineering, performance measurement systems, simulation and training programs, lean and six sigma integration; and computer integrated manufacturing and flexible automation.
  • Aniket Mohan Sahasrabudhe, University of Texas at San Antonio
    Master of Science in Mechanical Engineering, University of Texas at San Antonio; B.S. in Production Engineering of University of Pune (India). As a graduate student, he was a member of the Sustainable Manufacturing Systems Laboratory (SMS Lab), developed by the Mechanical Engineering Department at UTSA. His professional skills are in the following areas: lean manufacturing, six sigma, design of machine elements, production planning and scheduling, design for manufacturing and assembly, and inventory management. He is currently an Asst Project Manager at Micro-Supreme Auto Industry, an Engine Precision Parts and Gauges Manufacturing Company in India.
  • Leonardo Rivera, Universidad Icesi
    Industrial Engineer (Universidad del Valle, 1994); Master of Science in Industrial Engineering (Georgia Institute of Technology, 1996); Ph.D in Industrial and Systems Engineering (Virginia Polytechnic Institute and State University, 2006). Former Head of the Industrial Engineering Department and Director of the Industrial Engineering Program at Universidad Icesi, where he worked from May, 1998 to January, 2014. Presently affiliated with the School of Industrial Engineering at Universidad del Valle (Cali - Colombia), and their research Group Logística y Producción.

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Published

2014-03-30

Issue

Vol. 12 No. 28 (2014)

Section

Original Research

License

This journal is licensed under the terms of the CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/legalcode).