Food loss and waste are major issues in the food industry, and they affect all stages of the food supply chain (FSC). Food loss and waste are linked to environmental deterioration, economic loss, and an increase in hunger. Therefore, the food industry requires sustainable consumption and production (SCP) to reduce losses and waste. The circular economy (CE) concept has become a popular strategy for reducing food waste and boosting sustainability. Therefore, with efficient reverse logistics in the FSC, food producers can help achieve sustainable development goals (SDGs) like SCP and zero hunger. In literature, minimal research has been available in identifying the CE adoption challenges in FSC. This research identifies 15 critical challenges from the literature and discussion with the panel of experts. The relationship between the challenges has been established through an interpretive structural modeling (ISM) technique. The challenges were characterized in cause–effect according to their relational intensity obtained using the grey decision‐making trial and evaluation laboratory (DEMATEL) technique. Grey's relational theory is applied in DEMATEL to minimize uncertainty and vagueness of the expert judgment. The findings of this study suggest that creating policy from the government, providing incentives, and strictly enforcing environmental regulations are the most critical challenge. Hence, by focusing on the above, the effective adoption of the CE principle is achieved. This result also suggests that by addressing the challenges of CE, corporate social responsibility (CSR) can be performed. This study provides some recommendations for the practitioners to adopt CE towards sustainable development targets.

Artificial intelligence (AI) has been used in various industries to provide innovative and intelligent features in micro, small and medium enterprises (MSMEs). Big industries started adopting AI for their HR and workforce management (WFM) activities, however, past literature suggests a lack of AI adoption in MSMEs. In the ongoing pandemic, a large number of job loss is reported in the literature. Thus, artificial intelligence-powered intelligent workforce management (WFM) may be critical during or post-pandemic to manage the huge employment in MSMEs. In this study, we develop and test a conceptual framework based on three areas where AI-powered WFM adoption for MSMEs revenue development has been highlighted. These are (a) risk management (workforce), (b) business and marketing, and (c) information exchange. Six hypotheses have been proposed and tested using structural equation modelling (SEM) with responses from 307 employees. According to the research findings, all of the offered hypotheses are significant. The findings suggest to MSME decision-makers that AI-powered WFM may help revenue growth, workforce risk reduction, intelligent business and marketing, and thoughtful, innovative, and safe information exchange. MSMEs are required to use AI in the information sharing that help in workforce risk management, business and marketing, and intelligent workforce management that scale-up the economic growth.

The recent COVID-19 pandemic has caused enormous disruptions to supply chain (SCs). Border restrictions forced countless businesses to close either permanently or temporarily. However, the food industry is an essential sector that needs to be operational during a pandemic. Although the food industry has proactively worked towards fulfilling human needs, the food supply chain (FSC) faced numerous challenges, forcing SC managers to rethink their business strategy to cater to consumer demands effectively. In a pandemic situation, manufacturing operations need to repurpose and adapt to produce different high-demand products. Resilience initiatives help fight disruption phases in an uncertain environment by building capacity to resist and recover to a better position. This study identifies 14 key enablers to develop a resilient FSC and reveals the most significant enablers in India. We used a hybrid Delphi-interpretive structural modeling (ISM) and Fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) methodology to achieve these goals. The Delphi technique identified essential enablers, while the ISM analyzed the interrelationship among enablers and level of importance in a hierarchical structural model. Finally, the Fuzzy-DEMATEL categorized the enablers into the cause-effect group. This study helps SC decision-makers recognize the enablers and the contextual and causal relationships to improve resilience initiatives. It also helps them repurpose their manufacturing operations and shift to other highly required and high-demand production.