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Inclusive risk modeling for manufacturing firms: a Bayesian network approach

Author

Listed:
  • Yash Daultani

    (Atal Bihari Vajpayee Indian Institute of Information Technology and Management)

  • Mohit Goswami

    (Indian Institute of Management)

  • Omkarprasad S. Vaidya

    (Indian Institute of Management)

  • Sushil Kumar

    (Indian Institute of Management)

Abstract

This paper focuses on modelling the enterprise level risks from the perspective of an original equipment manufacturer. We intend to converge on an overall risk measure that is representative of the cumulative effect of risks emanating from considerations pertaining to respective functional divisions within the enterprise. Further, due to multitude of interplays between the core objectives of various functional divisions, modeling the cumulative risk pertaining to any project within a firm presents significant challenges. This paper proposes a systematic risk assessment methodology considering various enterprise specific risk characteristics (primarily technical, commercial, and operational in nature) related to multiple functional divisions of an enterprise. Specifically, we consider six different functional divisions i.e. planning, sourcing, operations, marketing, logistics and service. A Bayesian network model is then evolved by mapping the risk parameters related to various functional divisions and their interdependencies. Further, each of these risk parameters are represented in terms of parent and root nodes. In order to determine the probabilities of existing nodes in a Bayesian network, a methodical approach is developed that focuses on obtaining the conditional probabilities of the nodes with multiple parents. Thereafter, an enterprise level value chain risk measure is proposed that evaluates the feasible risk states in terms of an aggregate risk number. Employing an example of a typical automotive company, the methodology is illustrated.

Suggested Citation

  • Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-017-1374-7
    DOI: 10.1007/s10845-017-1374-7
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    References listed on IDEAS

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    Cited by:

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    3. Dhirendra Prajapati & Arjun R Harish & Yash Daultani & Harpreet Singh & Saurabh Pratap, 2023. "A Clustering Based Routing Heuristic for Last-Mile Logistics in Fresh Food E-Commerce," Global Business Review, International Management Institute, vol. 24(1), pages 7-20, February.
    4. Tamie Takeda Yokoyama & Satie Ledoux Takeda-Berger & Marco Aurélio Oliveira & Andre Hideto Futami & Luiz Veriano Oliveira Dalla Valentina & Enzo Morosini Frazzon, 2023. "Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework," Operations Management Research, Springer, vol. 16(1), pages 49-79, March.

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