Department of Management
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Item Development of an automobile industry risk index(Inder Science, 2019-11) Sharma, Satyendra KumarThe aim of this research is to construct a Bayesian belief network (BBN) model, which encompasses all the risk factors relevant to the Indian automotive sector that can give a fair, empirical idea as to how much the risk factors drive down the gross turnover of the industry. The BBN model is used to gauge business, economic and external risks and evaluate its impact on gross turnover of the industry. Empirical model draws a lot of implications to streamline the risk effects in the industry, but it clearly shows that the three factors - business risks, economic risks and external risks are not entirely independent and are positively correlated with each other. Bayesian networks provide a very useful risk assessment tool that takes into account the advantages of both quantitative and qualitative risk assessment methods. This is a novel, empirical effort to provide a generalised model to integrate all risks - domestic, global, economic, legal - relevant to the automotive industry.Item Identification and assessment of supply chain risk: development of AHP model for supply chain risk prioritisation(Inder Science, 2012-11) Sharma, Satyendra Kumar; Bhat, Anil KumarIn modern times, competition has shifted from companies to supply chains and risk management has become one of the greatest challenge for supply chain managers. Risks affect supply chain performance hence supply chain risk assessment needs more attention. For this reason, this paper presents a general risks taxonomy and it gathers together, specifically the risks affecting the performance of an automotive supply chain. Further, the authors use analytic hierarchy process (AHP) methodology to analyse the risk factors. It is hoped, that this analysis will help managers, vendors, consultants and auditors to manage supply chain risks better. All risks that a supply chain might face have been identified, classified and ranked based on their risk exposure on supply chain. Results suggest that the most critical risks originate from supply side of the chain. The most important risks in automotive supply chain turn out to be volatile demand, financial failure of suppliers, supplier’s quality problems and failure to generate cost reduction.Item Supply Chain Risk Assessment Tools and Techniques in the Automobile Industry: A Survey(IUP, 2014-10) Bhat, Anil Kumar; Sharma, Satyendra KumarThe importance of managing risk beyond the boundaries of a firm is widely recognized, and the new concept, Supply Chain Risk Management (SCRM) has attracted the attention of practitioners and academicians across the globe. Supply chain risk assessment is a critical step in SCRM. Companies use different tools and techniques for risk assessment in supply chain. The purpose of this paper is to investigate SCRM practices and identify the tools and techniques used by the Indian automobile companies. A survey was carried out to address the above-mentioned research questions. The results showed that SCM managers rely on still checklist, likelihood/impact matrix and scenario analysis. Supply chain risk assessment practices can be improved by using techniques like Failure Mode Effect Analysis (FMEA), simulations and others like HaZop.Item Developing a Bayesian belief network model for prediction of R&D project success(Taylor & Francis, 2017-03) Sharma, Satyendra Kumar; Chanda, UdayanThe project success is critical to the business performance in the era of fierce competition and globalization. The basis for project success lies in the capabilities of managing risks effectively. Innovation has always been considerably risky; however, managing Research and Development (R&D) project risks has become even more important given today’s tight schedules and limited resources. Risk management has to be an integral part of the development process. The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project. A risk quantification model based on the Bayesian belief network is proposed, which is effective in capturing the interaction between various risk factors. The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.