Title: Big data analytics for investigation of lean and green concepts in medium scale manufacturing industries

Authors: S.N. Sathiya Narayana; T.G. Arul; P. Parthiban; N. Anbuchezhian

Addresses: Department of Mechanical Engineering, Jayaram College of Engineering and Technology, Tiruchirappalli, 621014, India ' Department of Mechanical Engineering, Muthayammal Engineering College, Rasipuram, 637408, Tamil Nadu, India ' Department of Production Engineering, National Institute of Technology, Tiruchirappalli, 620015, India ' Department of Mechanical Engineering, R.M.D. Engineering College, Kavaraipettai, 601206, India

Abstract: Lean manufacturing is a tool, which is used to cut down waste and to improve the efficiency of an organisation and helps the sustainability of an organisation in the competitive environment. Implementation of green systems in organisations results in reduce energy consumption, waste generation, and hazardous materials used while also building the companies' images as socially responsible organisations. Lean and green systems are associated with waste reduction techniques. In foreign countries, many industries have started implementing these concepts and they are getting good results. In India, companies are facing problems in implementing lean and green concept. This paper investigates the critical success factors for implementation of lean and green concept in Indian medium scale manufacturing industries. The factors are grouped into different levels by interpretive structural modelling (ISM). The analytic network process (ANP) method has been used to determine the extent to which the main principles of lean and green manufacturing have been carried out in the six Indian medium scale manufacturing industries.

Keywords: lean manufacturing; green manufacturing; interpretive structural modelling; ISM; analytic network process; ANP.

DOI: 10.1504/IJENM.2022.10050732

International Journal of Enterprise Network Management, 2022 Vol.13 No.3, pp.216 - 236

Accepted: 02 Sep 2020
Published online: 29 Sep 2022 *

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