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A hybrid approach to enhancing the performance of manufacturing organizations by optimal sequencing of value stream mapping tools

Sameer Kumar (Department of Operations and Supply Chain Management, Opus College of Business, University of St. Thomas, Minneapolis, Minnesota, USA)
Yogesh Marawar (Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, India)
Gunjan Soni (Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, India)
Vipul Jain (School of Management, Victoria Business School, Victoria University of Wellington, Wellington, New Zealand)
Anand Gurumurthy (Department of Quantitative Methods and Operations Management (QM and OM) Area, Indian Institute of Management Kozhikode (IIMK), Calicut, India)
Rambabu Kodali (Department of Mechanical Engineering, National Institute of Technology, Rourkela, India)

International Journal of Lean Six Sigma

ISSN: 2040-4166

Article publication date: 27 February 2023

Issue publication date: 7 November 2023

296

Abstract

Purpose

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

Design/methodology/approach

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Findings

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

Originality/value

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

Keywords

Citation

Kumar, S., Marawar, Y., Soni, G., Jain, V., Gurumurthy, A. and Kodali, R. (2023), "A hybrid approach to enhancing the performance of manufacturing organizations by optimal sequencing of value stream mapping tools", International Journal of Lean Six Sigma, Vol. 14 No. 7, pp. 1403-1430. https://doi.org/10.1108/IJLSS-03-2022-0069

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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