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An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer

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  • Kavilal, E.G.
  • Prasanna Venkatesan, S.
  • Harsh Kumar, K.D.

Abstract

The complexities in present day supply chain are numerous and are evolving due to globalization, customisation, innovation, flexibility, sustainability and uncertainties. The growing supply chain complexity results in negative consequences on cost, customer service and reputation. Managing supply chain complexity without compromising the profitability is a challenging task. Supply chain complexity (SCC) management involves identifying, prioritizing, measuring, analysing and controlling/eliminating the drivers of complexity. The SCC drivers denote number and variety of suppliers, customers, products, processes and uncertainties which are highly interdependent. Firms need to prioritize the drivers in order to manage and simplify SCC. Models and methods to prioritize the complexity drivers considering their interdependence are limited in literature. Prioritizing the complexity drivers requires a subjective approach and it is a multi criteria decision making (MCDM) problem. In this research, at first a fuzzy ISM (Fuzzy Interpretive Structural Modelling) is used to establish the interdependence of SCC drivers. A fuzzy AHP (Analytic Hierarchy Process) and fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) are then used to quantify and prioritize the complexity drivers considering the strength of interdependence obtained using the fuzzy ISM. A case example of a mining equipment manufacturer located in India is presented to illustrate the proposed approach. From the results it is identified that unreliability of suppliers, forecast inaccuracy, lack of visibility /information sharing and number/variety of processes are the significant drivers.

Suggested Citation

  • Kavilal, E.G. & Prasanna Venkatesan, S. & Harsh Kumar, K.D., 2017. "An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer," Resources Policy, Elsevier, vol. 51(C), pages 204-218.
    • Handle: RePEc:eee:jrpoli:v:51:y:2017:i:c:p:204-218
    DOI: 10.1016/j.resourpol.2016.12.008
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    References listed on IDEAS

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    2. Madhuri Saripalle, 2018. "Determinants of profitability in the Indian logistics industry," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 7(1), pages 13-27.
    3. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2020. "Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model," Resources Policy, Elsevier, vol. 68(C).
    4. Ahmed Mohammed & Morteza Yazdani & Amar Oukil & Ernesto D. R. Santibanez Gonzalez, 2021. "A Hybrid MCDM Approach towards Resilient Sourcing," Sustainability, MDPI, vol. 13(5), pages 1-30, March.
    5. Khorramshahgol, Reza & Al-Husain, Raed, 2021. "A GP-AHP approach to Design Responsive Supply Chains for Pareto Customers," Operations Research Perspectives, Elsevier, vol. 8(C).
    6. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2018. "Analysis of supply chain complexity drivers for Indian mining equipment manufacturing companies combining SAP-LAP and AHP," Resources Policy, Elsevier, vol. 59(C), pages 389-410.
    7. Zhexuan Zhou & Yajie Dou & Tianjun Liao & Yuejin Tan, 2018. "A Preference Model for Supplier Selection Based on Hesitant Fuzzy Sets," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    8. Soleimani, Hamed, 2021. "A new sustainable closed-loop supply chain model for mining industry considering fixed-charged transportation: A case study in a travertine quarry," Resources Policy, Elsevier, vol. 74(C).
    9. 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).

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