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Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices

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  • C. Vasanthakumar
  • S. Vinodh
  • K. Ramesh

Abstract

The contemporary manufacturing scenario witnesses the adoption of lean remanufacturing concepts in a concerted manner. Lean remanufacturing is a newly evolved manufacturing process concerned with manufacturing and remanufacturing of products to effectively utilise available energy and resources, while reducing wastes in the process and thereby increasing efficiency. The advantages include process streamlining coupled with end-of-life decisions. A structural model needs to be developed to clarify the interrelationships among factors influencing lean remanufacturing practices. In this study, interpretive structural modelling method has been used to develop the structural model depicting interrelationships and most dominant and least dominant factors. Twenty factors are being identified based on expert opinion from 35 Indian automotive component remanufacturing organisations. The identified most dominant factors include a strong top management commitment with proper strategy selection, long-term vision and participation and a strong understanding of the current product and process designs. MICMAC analysis has been conducted to categorise the factors. The inferences based on the study have been derived. The novel aspect of this study is that it presents the development of structural model to identify the most dominant factors influencing the implementation of lean remanufacturing principles.

Suggested Citation

  • C. Vasanthakumar & S. Vinodh & K. Ramesh, 2016. "Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices," International Journal of Production Research, Taylor & Francis Journals, vol. 54(24), pages 7439-7452, December.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:24:p:7439-7452
    DOI: 10.1080/00207543.2016.1192300
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    2. Geandra Alves Queiroz & Ivete Delai & Alceu Gomes Alves Filho & Luis Antonio de Santa-Eulalia & Ana Lúcia Vitale Torkomian, 2023. "Synergies and Trade-Offs between Lean-Green Practices from the Perspective of Operations Strategy: A Systematic Literature Review," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    3. Xiaohong Jiang & Huiying Wang & Xiucheng Guo & Xiaolin Gong, 2019. "Using the FAHP, ISM, and MICMAC Approaches to Study the Sustainability Influencing Factors of the Last Mile Delivery of Rural E-Commerce Logistics," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
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    7. Godinho Filho, Moacir & Marchesini, Antonio Gilberto & Riezebos, Jan & Vandaele, Nico & Ganga, Gilberto Miller Devós, 2017. "The application of Quick Response Manufacturing practices in Brazil, Europe, and the USA: An exploratory study," International Journal of Production Economics, Elsevier, vol. 193(C), pages 437-448.
    8. Harshad Chandrakant Sonar & Vivek Khanzode & Milind Akarte, 2020. "A Conceptual Framework on Implementing Additive Manufacturing Technology Towards Firm Competitiveness," International Journal of Global Business and Competitiveness, Springer, vol. 15(2), pages 121-135, December.
    9. Abdul Aziz Khan Niazi & Tehmina Fiaz Qazi & Abdul Basit, 2019. "Expounding the Structure of Slyer Ways of Tunneling in Pakistan," Global Regional Review, Humanity Only, vol. 4(2), pages 329-343, June.
    10. S. Maryam Masoumi & Nima Kazemi & Salwa Hanim Abdul-Rashid, 2019. "Sustainable Supply Chain Management in the Automotive Industry: A Process-Oriented Review," Sustainability, MDPI, vol. 11(14), pages 1-30, July.
    11. Abdul Aziz Khan Niazi & Tehmina Fiaz Qazi & Abdul Basit, 2019. "An Interpretive Structural Model of Barriers in Implementing Corporate Governance (CG) in Pakistan," Global Regional Review, Humanity Only, vol. 4(1), pages 359-375, March.
    12. Nitin S. Solke & T. P. Singh, 2018. "Analysis of Relationship Between Manufacturing Flexibility and Lean Manufacturing Using Structural Equation Modelling," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(2), pages 139-157, June.
    13. Manavalan Ethirajan & Thanigai Arasu M & Jayakrishna Kandasamy & Vimal K.E.K & Simon Peter Nadeem & Anil Kumar, 2021. "Analysing the risks of adopting circular economy initiatives in manufacturing supply chains," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 204-236, January.
    14. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2020. "Analysis of supply chain sustainability with supply chain complexity, inter-relationship study using delphi and interpretive structural modeling for Indian mining and earthmoving machinery industry," Resources Policy, Elsevier, vol. 68(C).
    15. Shelly Gupta & Sanjay Dhingra, 2022. "Modeling the key factors influencing the adoption of mobile financial services: an interpretive structural modeling approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(2), pages 96-110, June.
    16. Eduard Gabriel Ceptureanu & Sebastian Ion Ceptureanu & Razvan Bologa & Ramona Bologa, 2018. "Impact of Competitive Capabilities on Sustainable Manufacturing Applications in Romanian SMEs from the Textile Industry," Sustainability, MDPI, vol. 10(4), pages 1-16, March.
    17. Muhammad Zeeshan Shaukat & Madiha Saleem & Muhammad Usman Ajmal Mirza & Abdul Basit & Abdul Aziz Khan Niazi, 2023. "Using Interpretive Structural Modelling (ISM) to Impose Hierarchy on Critical Issues of Contractual Bargaining: A Study of Construction Industry of Pakistan," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(3), pages 69-84.
    18. M. Suguna & Bhavin Shah & B. U. Sivakami & M. Suresh, 2022. "Factors affecting repurposing operations in Micro Small and Medium Enterprises during Covid-19 emergency," Operations Management Research, Springer, vol. 15(3), pages 1181-1197, December.
    19. Avinash, A. & Sasikumar, P. & Murugesan, A., 2018. "Understanding the interaction among the barriers of biodiesel production from waste cooking oil in India- an interpretive structural modeling approach," Renewable Energy, Elsevier, vol. 127(C), pages 678-684.
    20. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.

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