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This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

This research identifies the enablers for implementing circular supply chains (CSCEs) and analyzes interrelationships between them to quantify their driving and dependence power to understand the critical CSCEs.

Initially, 10 CSCEs are identified for the Indian apparel industries through an extant literature review and validated using the Delphi method by seeking experts' opinions. The identified CSCEs are subjected to a novel neutrosophic interpretive structural modeling (N-ISM) method to capture the interrelationships between CSCEs and compute the driving and dependence power of CSCEs.

The findings of the present research work revealed that “supportive legislative framework, awareness of circular economy's potential for revenue gain and availability of trained research and development (R&D) team” are the critical CSCEs that need to be considered while implementing a circular supply chain in apparel industries.

This study offers insightful implications to guide practitioners in implementing the circular economy in apparel supply chains.

This research work is one of the earlier studies to analyze the enablers for implementing circular supply chains. This study has explored CSCEs in the context of apparel industries. From a methodological perspective, the novel N-ISM method is worth highlighting as the originality of the work.

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

This study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption.

Initially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19.

This study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study.

This study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm.

The present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic.

This study attempts to identify the supply chain risks (SCRs) induced during the COVID-19 disruption in an Indian handloom saree industry and determine suitable risk mitigation strategies (RMSs) to overcome the impact of the epidemic disruption.

This work determined 11 SCRs through an extensive literature review in the context of the handloom apparel industry and validated through the experts. Further, a multiple case-based approach is used in this research. Within case and cross-case analyses of four relevant Indian handloom “make-to-order” saree manufacturing firms are conducted to determine the severity of the SCRs considering the pandemic situations to identify appropriate strategies to mitigate the shock of SCRs.

This study identified the critical SCRs in the context of the Indian handloom “make-to-order” saree industries that emerged during the COVID-19 and proposed a risk mitigation strategy matrix (RMSM) to address the SCRs based on their criticality and predictability dimensions.

The study provides a novel contribution to the body of knowledge on supply chain risk management (SCRM) in the form of the RMSM tool. Supply chain managers from the different sectors can extend the proposed RMSM to overcome the SCRs. Multiple case analyses facilitate supply chain professionals working in handloom apparel industries to benchmark and adopt the proposed RMSs in their firm.

This research is one of its kind that carried exploratory investigation of the handloom apparel industry cases to assess and determine the strategies for mitigating the SCRs caused during a pandemic outbreak.