Department of Management

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1930

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Author: search.filters.author.Routroy, SrikantaSubject: search.filters.subject.Bayesian networks

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    Modeling information risk in supply chain using Bayesian networks
    (Emerald, 2016-03) Routroy, Srikanta; Sharma, Satyendra Kumar
    Information sharing enhances the supply chain profitability significantly, but it may result in adverse impacts also (e.g. leakages of secret information to competitors, sharing of wrong information that result into losses). So, it is important to understand the various risk factors that lead to distortion in information sharing and results in negative consequences. Information risk identification and assessment in supply chain would help in choosing right mitigation strategies. The purpose of this paper is to identify various information risks that could impact a supply chain, and develop a conceptual framework to quantify them.
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    Supply-side risk modelling using Bayesian network approach
    (Taylor & Francis, 2022-02) Chanda, Udayan; Sharma, Satyendra Kumar; Routroy, Srikanta
    Organisations’ vulnerability to risks exponentially increased in the past decade, thereby highlighting the need to develop additional effective risk management strategies. This research uses a systematic literature review as a foundation for designing a supply risk model that uses a Bayesian belief network. The proposed model aims to identify the most critical objective and subjective risk factors influencing supply chain networks. Moreover, the proposed methodology has been demonstrated through a case study conducted in an Indian manufacturing, in which inputs were taken from supply chain and risk management experts. Hugin Expert software was used to design and run simultaneous simulations on the Bayesian network. The top three factors found to influence business profitability were delays, product technology, and fuel price. The proposed model can be reengineered as conditions change and new information becomes available, thereby ensuring that risk analysis remains current and relevant along the timeline of the any disruption.