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This study aims to analyse the resilience of customer demand management post-coronavirus disease 2019, using fast fashion as an example. The paper provides insights for potential applications to micro-, small and medium enterprises (MSMEs).

Based on the qualitative analysis and an integrated Plan-Do-Check-Act (PDCA)-decision making trial and evaluation laboratory (DEMATEL)-fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) methodology of fuzzy multi-criteria decision-making, we explored and prioritised the enablers of resilience management for fast-fashion MSMEs.

The results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, a customer-focussed assortment of items and flexible store operations.

The study findings will enable fast-fashion MSMEs to develop effective actions and priorities in operations efforts to promote post-pandemic recovery.

Despite the importance of the resilience project and the changing fast-fashion customer patterns, only a handful of studies have explored how resilience can be managed in this field. Thus, the findings can contribute to closing this gap in the context of operations resilience research as well as MSME operations.

The authors analysed the operations of two synchronised channels by focusing on “buy online and return in store” (BORS) strategies in fast-fashion retail by investigating internal and external factors affecting this omnichannel strategy.

The authors apply a combination of techniques to identify the BORS factors. Firstly, a strengths, weaknesses, opportunities and threats (SWOT) analysis was used to define the operational factors of BORS adoption. The authors then apply analytic hierarchy process (AHP) to evaluate the factors under four SWOT categories for kids, male and female consumer groups. The factors of BORS were then ranked using the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) approach.

Combining the SWOT, AHP and fuzzy VIKOR techniques, the authors identified 21 factors in this study. The opportunity that BORS provides for trying in the fitting room for a better convenient shopping experience was ranked as the most important factor, followed by the opportunity to create a loyal customer profile with an easy and well-organised return process. Furthermore, the results reveal that the child consumer group is the most critical of the stated operations factors, followed by male and female consumers.

The authors described the operational factors and supported the decision-making system of BORS for each consumer group with a priority ranking to realise effective managerial management for fast-fashion retailers and practitioners.

The study contributes to the growing literature on the BORS omnichannel strategy, specifically for fast-fashion retail based on consumer needs.

Amid the outbreak of coronavirus disease 2019 (COVID-19), supply chains have faltered. This has influenced operational and financial performance and lead to uncertainty in supply and distribution. Therefore, systems measuring supply chain risk and disruption management performance have gained interest. This study explores barriers to supply chain performance measurement during disruptions such as COVID-19.

Based on a survey and literature review, the authors formalise the barriers and rank them using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) fuzzy Višekriterijumsko kompromisno rangiranje (VIKOR) methodology. A total of 14 experts in 11 countries were surveyed. Performance measurement is based on a balanced scorecard.

Fifteen barriers are identified. Based on DEMATEL analysis, the cause–effect relationships among the four scorecard aspects are explored. The customer axis is revealed as the cause, while the financial, internal business and learning/development are identified as the effect of the supply chain performance measurement. Fuzzy VIKOR calculations show that uncertainty of investment, disrupted cash flows and the bullwhip effect are the most critical barriers to measure supply chain performance during outbreaks such as the COVID-19 pandemic.

The study identifies and ranks general barriers; additional research is required to differentiate barriers in specific industrial sectors.

The findings may help develop proactive, resilient supply chain performance strategies to overcome disruptions.

Policy-makers and decision-makers in industrial and service firms can explore these findings to inform strategies for robust supply chains that can resist disruption in risky environments.

This research addresses a knowledge gap in barriers to measure supply chain performance in post-pandemic areas. It is unclear how far firms will measure supply chain performance in terms of learning from disruption patterns, managing financial and customer demand processes in light of COVID-19. This study contributes by explaining the influences among the barriers and exploring them, offering insights from multiple stakeholders.

The purpose of the study is to propose a framework for fleet management and make suitable distribution solution choices in the food industry.

This study reviews the literature to examine food distribution criteria. These criteria are used in the analytic hierarchy process (AHP) assessment and combined with discrete events simulation in a structured framework, which is validated through an empirical study.

The empirical case results demonstrate that both the AHP and discrete events simulation converge toward the same solution in most cases.

This study contributes to the literature on distribution management and develops a framework that can both guide future research and aid logistics practitioners in analysing distribution decision-making systems in dynamic environments.

This paper aims to present a simple and innovative fuzzy methodology-based lean performance measurement system (L-PMS) for an Indian automotive supply chain. The paper also enlightens the influence of coronavirus disease 2019 (COVID-19) on supply chains and the practical implications of the unprecedented disruptions on the performance measurement systems.

The L-PMS is divided into three phases. In the first phase, the key performance indicator (KPI) list, as deemed fit by the organization, is prepared using literature and suggestions from the case organization. The list contains 61 KPIs measuring 24 performance dimensions in seven functional areas of the supply chain. In the second phase, the KPI performance data (actual, best and worst) are collected using the enterprise resource planning (ERP) system. In the last phase, the leanness score of the case organization is calculated at four levels – KPI, dimension, functional area and overall organization.

The overall leanness score of the case organization is 60%. The case organization uses KPIs from all seven functional areas, but it needs to improve the number of KPIs in administration and supplier management functional areas. The case organization uses only quantitative KPIs. However, the performance dimensions at the middle level are adequate. The leanness level of the case organization in different areas is highly variable (ranges from 45% to 91%).

The major limitation of the study is that the case study is done at a single organization.

The managers at the different levels of the hierarchy can use the lean performance measurement score to leverage the better performing areas/dimensions/KPIs and improve poor performing areas/dimensions/KPIs. The lean performance measurement at functional area level can help leadership to give responsibility to different people for the improvement of leanness with respect to different dimensions/functional areas. The disruptive impact of COVID-19 should clearly be understood by the managers to make appropriate decisions based on the severity as measured at different levels.

According to the authors' best knowledge, this is the first lean performance measurement application at the four hierarchical levels (KPI, performance dimension, functional area and overall organization).

The circular economy is a production and consumption model that encourages people to share, lease, reuse, repair, refurbish and recycle existing materials and products for as long as possible. The blockchain-based circular economy is being used in many industries worldwide, but Indian electronic MSMEs face many problems in adopting a blockchain-based circular economy. The research aims to discover the barriers the electronic MSMEs face in adopting a blockchain-based circular economy and pull back from achieving environmental sustainability in their operations.

Fifteen barriers are identified from the literature review and finalized with experts' opinions. These barriers are evaluated by using interpretive structural modeling (ISM), MICMAC analysis and fuzzy TOPSIS method.

Lack of support from distribution channels, lack of traceability mechanism and customer attitudes toward purchasing remanufactured goods are identified as the most critical barriers.

The study will benchmark the electronic MSMEs in achieving environmental sustainability in the blockchain-based circular economy.

It is a study that not only establishes a hierarchical relationship among the barriers of blockchain adoption in Indian electronic MSMEs but also verifies the results with fuzzy TOPSIS method.

The purpose of this study is to investigate the sustainability performances of apparel, footwear and accessory (AFA) B Corps, providing companies, especially micro, small and medium-sized enterprises, with reasonable suggestions on how to incorporate the concept of sustainability efficiently.

This study focused on 117 AFA B Corps. B Corps’ overall sustainability performances consist of their performances in the five areas of governance, workers, community, environment and customers. First, the 117 B Corps’ performances in these areas were compared. Second, multiple regression models were built to predict the B Corps’ sustainability performances based on their inherent characteristics (headquarter location, age, size and industry sector). Third, according to the B Corps’ performances in the five areas, the B Corps were clustered using the hieratical clustering method.

This study found that the B Corps’ performances in different areas were significantly different and their performances in the area of the community were better than in the other four areas. The B Corps’ characteristics were correlated to their sustainability performances. For example, company size was positively related to the B Corps’ performances in the area of workers. Additionally, Clusters 1, 2 and 3 were identified and characterized by their competitive performances in the areas of governance, workers and community, respectively.

This study contributes to the knowledge of AFA B Corps’ sustainability performances, identifying the weakness and strongness of the sustainable practices accepted by existing AFA B Corps and lending insights regarding how to predict and improve sustainability performances.