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Manufacturing wastes analysis in lean environment: an integrated ISM-fuzzy MICMAC approach

Author

Listed:
  • Lakhan Patidar

    (Maulana Azad National Institute of Technology)

  • Vimlesh Kumar Soni

    (Maulana Azad National Institute of Technology)

  • Pradeep Kumar Soni

    (Maulana Azad National Institute of Technology)

Abstract

Value stream mapping (VSM) is an important improvement initiative in the lean environment that can be used to boost manufacturing performance by eliminating wastes (Ws). In this study, authors have identified manufacturing related critical wastes for enhancing the manufacturing competitiveness with the help of industry experts and literature. For the effective VSM implementation, these wastes have categorised into three groups named as; seven deadly waste; knowledge waste and administrative waste. The goal of present work is to understand the mutual interaction between these wastes and to identify the ‘driving wastes’ (i.e. which influence the other wastes) and the ‘dependent wastes’ (i.e. which are influenced by others). The research theme has divided into three segments; (1) identified the wastes from literature and industry experts, and conducted interviews with experts through a questionnaire in the form of waste relationship matrix (2) prepared an ISM based framework and finally, (3) cluster analysis has done through fuzzy MICMAC. Interpretive structural modeling has been used to analyse the relationships among these manufacturing wastes. Fuzzy MICMAC (cross-impact matrix multiplication applied to classification) has been used to find out driving and the dependence power of identified waste. To determine the driving and the dependence power of various wastes the final results of interpretive structural modeling are used as input to the fuzzy MICMAC analysis. The finding of this study reveals that overproduction (W4) and unclear customer (W11) are a matter of concern and need maximum attention for enhancing manufacturing performance. The enterprises and the decision-makers can be benefited from this model to identify which manufacturing wastes are performing as the most deleterious.

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  • Lakhan Patidar & Vimlesh Kumar Soni & Pradeep Kumar Soni, 2017. "Manufacturing wastes analysis in lean environment: an integrated ISM-fuzzy MICMAC approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1783-1809, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0669-6
    DOI: 10.1007/s13198-017-0669-6
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    References listed on IDEAS

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    1. V.G. Venkatesh & Ratna Paluri & Sonali Bhattacharya, 2016. "Sustenance of Indian apparel manufacturing industry in post MFA period - a strategic analysis," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 6(3), pages 343-365.
    2. Vishal Ashok Bhosale & Ravi Kant, 2016. "An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers," International Journal of Production Research, Taylor & Francis Journals, vol. 54(24), pages 7374-7399, December.
    3. Bernardo Villarreal, 2012. "The transportation value stream map (TVSM)," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(2), pages 216-233.
    4. Herron, Colin & Braiden, Paul M., 2006. "A methodology for developing sustainable quantifiable productivity improvement in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 104(1), pages 143-153, November.
    5. Sindhu, Sonal & Nehra, Vijay & Luthra, Sunil, 2016. "Identification and analysis of barriers in implementation of solar energy in Indian rural sector using integrated ISM and fuzzy MICMAC approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 70-88.
    6. Sainath Gopinath & Theodor Freiheit, 2012. "A waste relationship model and center point tracking metric for lean manufacturing systems," IISE Transactions, Taylor & Francis Journals, vol. 44(2), pages 136-154.
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    Cited by:

    1. M. Suresh & G. Mahadevan & R. Dev Abhishek, 2019. "Modelling the factors influencing the service quality in supermarkets," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1474-1486, December.
    2. Vineet Jain & Puneeta Ajmera, 2019. "Modelling of the factors affecting lean implementation in healthcare using structural equation modelling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 563-575, August.
    3. Videsh Desingh & Baskaran R, 2022. "Internet of Things adoption barriers in the Indian healthcare supply chain: An ISM‐fuzzy MICMAC approach," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 318-351, January.
    4. Lusia Permata Sari Hartanti & Ivan Gunawan & Ig. Jaka Mulyana & Herwinarso Herwinarso, 2022. "Identification of Waste Based on Lean Principles as the Way towards Sustainability of a Higher Education Institution: A Case Study from Indonesia," Sustainability, MDPI, vol. 14(7), pages 1-18, April.

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