The enterprises and their supply chain (SC) have undergone significant changes because of the highly complex business environment, dynamism, environmental change, ideas like globalization, and increased rivalry of enterprises in the national and worldwide arena. Additionally, pandemics and crises caused SC disruptions for enterprises. Thus, an enterprise’s SC must constantly be ready to face various obstacles and unpredictable environmental changes. In an era of growing technological advancement, enterprises and their strategies are transforming toward sustainable and resilient SC. For this reason, this study embraces the notion of utilizing technologies such as Artificial intelligence (AI) and big data analytics (BDA) as branches of intelligence techniques of Industry 4.0 (Ind 4.0) and, thereafter, Industry 5.0 (Ind 5.0). Thus, the study contributes to constructing an appraiser model for appraising the enterprises that employ these technologies and techniques in their SC to be sustainable resilience in another meaning resilience supply chain (ReSSC). This model utilized best worst method (BWM) under the governing of Single-valued triangular neutrosophic sets (SVTNSs) to generate an appraiser model. Whereas SVNSs applied in the comparative analysis as a comparative model with the cooperation of AHP, TOPSIS, and WSM to validate our constructed model. The findings of the appraiser model based on MCDM merging with SVTNSs and the comparative model based on MCDM integrated with SVNSs agreed that the optimal key indicator six is securing of data (KI6); otherwise, Key Indicator three is transparency (KI3). Also, these models agreed to recommend enterprises from optimal to worst as En1> En4> En2> En3. From the results of the two models, En1 is the most sustainable and resilient. Contrary, En 3 is the least.