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Interest in supply chain (SC) resilience has increased in the wake of the pandemic and other crises, including those related to political and environmental instability. The literature offers some contributions to proactive indicators to assess the resilience of a system before a disruption occurs. Other studies provide metrics to assess resilience from the reactive perspective after the onset or end of a disruption. This paper examines the application of some proactive indicators from network science to some post-disruption measure of resilience, especially how these measure evolves as a function of time. We examine this by testing different supply chain designs against disrupted scenarios and using data from a real-life industry. The focus is on service level as a performance metric. The tested indicators correlate well with performance loss but show a limited ability to correlate with metrics representing SC dynamics. The practical contribution of this paper is an approach to measure SC resilience as an inherent property of the system, which can aid in designing future SCs, rather than measuring resilience as a response to a disruptive event. The paper also provides theoretical contributions, including the further validation of certain indicators from the literature and the identification of research areas in need of new metrics.

One of the substantial concerns in any large scale enterprises is the process of evaluating, selecting and improving suppliers due to the involvement of many uncontrollable and unavoidable factors. To address this issue in industry, suppliers are selected in the context of a resilient supply chain since it is one way to overcome disruptions. Selection of suppliers involves a variety of both qualitative and quantitative attributes. This research proposes the five main criteria of supplier selection which are chosen based on vulnerable areas and production requirements to develop flexibility in procuring material. In order to select appropriate suppliers for optimal order allocation the fuzzy multi-objective linear programming (FMOLP) is proposed which overcomes the vagueness of the information. Also to calculate the importance of each criterion fuzzy analytic hierarchy process (FAHP) is used. Finally, the practical usefulness of the proposed method is ascertained through a case study in steel industry.