A hybrid approach for quantifying supply chain risk and prioritizing the risk drivers

Abstract

Purpose The purpose of this paper is to identify various risk and sub-risk drivers that affect the supply chain (SC) performance and to propose a framework to quantify the overall SC risk index by considering the importance of each risk and sub-risk drivers and their mutual interactions. Design/methodology/approach A hybrid method based on decision-making trial and evaluation laboratory and analytical network process has been proposed to develop the risk quantification framework. A case study of Indian petroleum supply chain (PSC) has been illustrated to explain the proposed method. Findings The results of this study found that transportation/logistics (delivery system), quality of the petroleum products, crude supply, customer’s order and legal/political regulations are the most significant risk drivers of a typical PSC. It is also found that the Indian PSC possesses a risk score of 34 percent. Research limitations/implications The quantification of risk in operational measure provides an unblemished representation of the overall SC risk. Unlike the existing financial measure, it takes complex subjective operational effectiveness like product quality, customer satisfaction, etc., into consideration. Identifying the high-prioritized risks helps the decision and policy makers to merely focus on the most prominent risk drivers, and reduce the impact of overall SC risk. Planning a risk mitigation strategy at a given level of risk is however beyond the scope of this research. Originality/value The paper develops a risk quantification framework in the context of a PSC.

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP, artificial neural network: A case study

“…First, by reviewing the literature related to the research subject and using the theoretical principles of the research, the criteria of the project risks are identified and categorized. Accordingly, these criteria are categorized as follows: 1Project management criteria: estimation, planning, control, and communications (Mavi & Standing, ). 2Organizational criteria: project dependence, such as resources, budget, and prioritization (Ajayi & Oyedele, ). 3External criteria: suppliers, regulations, market, customers (Ershadi & Omidzadeh, ), and climate (in the environmental sense; Acharya et al., ). 4Technical criteria: requirements, technology, complexity, performance and reliability, and quality (Tarei, Thakkar, & Nag, ). …”

Benchmarking the relationship between supply chain risk mitigation strategies and practices: an integrated approach

“…Various stages of SCRM process consist of identification, assessment, mitigation and control of risks (Hallikas et al, 2004). Risk classification and identification have been exhaustively discussed in the SCRM literature (Christopher and Peck, 2004;Wagner and Bode, 2008;Guertler and Spinler, 2015;Tarei et al, 2018). There is relatively less research that examines the effectiveness of alternative SCRM strategies in the different contextual environment (Chang et al, 2015).…”

“…The aforementioned challenges and issues involved in the PSC have attracted the global research attention in the past decade (Gardas et al, 2019;Tarei et al, 2018;Al-Qubaisi and Ajmal, 2018;Hussain and Khorramshahgol, 2016;Modarress et al, 2016;Lima et al, 2016;Shah et al, 2011). To the best of our knowledge, the existing literature is yet to deliver any published work focusing on the SC RM phase in the petroleum/oil and gas sector.…”

Toward a supply chain risk identification and filtering framework using systems theory

“…In the literature, supply chain risks are classified from various perspectives. Some researchers developed qualitative, quantitative or hybrid methods for identifying potential supply chain risks, such as supply chain vulnerability map (Trkman and McCormack, 2009;Malakouti et al, 2017;Tarei et al, 2018;Ramesh et al, 2019). Others focused on risk factor identification using the analytic hierarchy process method (Gaudenzi and Borghesi, 2006) and knowledge-based system approach.…”