No abstract

No abstract

The modelling of supply chain risk management (SCRM) has attracted increasing attention from researchers and professionals. However, a systematic network analysis of the literature to understand the development of research over time is lacking. Therefore, this study reviews SCRM modelling and its evolution as a scientific field. We collected 566 papers published in the Scopus database and shortlisted 120 for review. We have analysed the field's performance, mapped the most influential studies, as well as the generative and evolutionary research areas, and derived future research directions. Using bibliometric methods and tools for citation network analysis to understand the field's dynamic development, we find that five generative research areas provide the fundamental knowledge for four evolutionary research areas. The interpretation of gaps and trends in these areas provides an SCRM modelling timeline with 14 future research directions, which should consider adopting a holistic SCRM approach and developing prescriptive and normative risk models. The holistic approach enables more research on key factors—like process integration, design, information risk, visibility and risk coordination—that directly impact industry, decision-makers and sustainability needs. Risk models with evolved prescriptive and normative typology should respect both business model strategies and actual supply chain performance.

Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the ‘Fuzzy Extended Analytic Hierarchy Process (FEAHP)’ method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.