Department of Management

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Author: search.filters.author.Sharma, Satyendra KumarSubject: search.filters.subject.Bayesian networks

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Now showing 1 - 3 of 3
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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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    Developing a Bayesian Network Model for Supply Chain Risk Assessment
    (Taylor & Francis, 2016-05) Sharma, Satyendra Kumar
    The Bayesian network based probability model is very new to the field of business management. Its use for risk assessment to predict the supply chain disruption and their consequences on the supply chain goals is very limited. The purpose of this research, is to develop a risk assessment tool to assess and to determine the risk exposure faced by a supply chain. In a global economy with ever-growing competition the firms are facing uncertain disruptions in their supply chains that further dent their brand value. The proposed probabilistic model that updates itself in the light of new evidences and calculates marginal probabilities for all risk variables and supply chain goals through conditional probability tables. The proposed model empowers the supply chain managers to predict the chances of any disruptive risk factors in the supply chain.
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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.