Pandemics, geopolitical strife, and regional tensions in recent years have intensified the disruption risks faced by global supply chains. To address the growing need for resilience evaluation, a quantitative indicator is proposed for measuring supply chain susceptibility to disruptions. A supply chain network is represented as a multistate resilience supply chain network (MRSCN), where nodes denote suppliers, assemblers, and markets, and arcs represent logistics connections. Due to shared resources, production and transportation capacities may be limited. System reliability is defined as the probability that the MRSCN successfully delivers goods to markets. In contrast to previous studies, an algorithm is proposed that considers uncertainties in both transportation and production capacities. A quantitative metric, based on system reliability, is computed to evaluate the impact of newly added suppliers during disruption scenarios. By comparing reliability before and after supplier integration, the contribution of new suppliers to overall supply chain resilience is quantified. The algorithm further enables the identification and analysis of supply chain vulnerabilities, offering valuable insights for decision-makers.

The United Nations’ sustainable development goals have highlighted the significance of improving supply chain sustainability and ensuring the proper distribution of orders. This study proposes a novel framework involving Z-number, game theory, an indifference threshold-based attribute ratio analysis (ITARA), and a combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations and enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are used to determine the objective weights of criteria and to rank the evaluated order allocations, respectively. A case study of a China company is then presented to demonstrate the usefulness of the proposed framework and to inform their decision-making process regarding which suppliers the orders should be assigned to.