All India Council for Technical Education

In the post-pandemic period, global supply chain (SC) operations are increasingly affected by geopolitical tensions, rising inflation, and logistical difficulties. Organizations located in different regions continue to struggle with SC disruptions, facing challenges related to planning and operational stability. One of the major challenges is developing an SC that remains stable during disruptions, fulfills customer demands, and contributes to overall business expansion. But, at the same time, SC faces increasing disruptions due to global complexities, demand volatility, and sustainability pressures. Therefore, organizations are adopting digital technologies in their operations to enhance resilience by anticipating risks, responding quickly, and ensuring continuity of operations. The knowledge of SC is still limited in the Industry 4.0 (I4.0) context. Therefore, this study initially investigates the role of I4.0 technologies to reshape supply chain resiliency (SCR), with a focus on enhancing agility and ensuring long-term sustainability within evolving market conditions. The study highlights key factors required to develop SCR. Further, the study uncovers the inter-dependencies, relationships, and priority of factors by using a hybrid approach. It is found that top management support is the most critical factor in building SCR. The present study contributes to the existing literature on SCR by providing novel insights into the theory, managerial implications, practical applications, and policy considerations for developing SCR.

Purpose This study aims to develop a human-centric Generative AI (GenAI)-enabled decision-support framework for sustainable agroecology supply chains under the Industry 5.0 paradigm. The framework integrates fuzzy Analytic Hierarchy Process (FAHP) and fuzzy C-Means (FCM) clustering to bridge digital innovation with ecological and social sustainability, ensuring participatory and adaptive decision-making in organic and community-based farming systems. Design/methodology/approach The research employs a hybrid FAHP-FCM model supported by ChatGPT as an interpretive reasoning layer. ChatGPT translates qualitative expert judgments and field narratives into structured fuzzy inputs, enhancing contextual adaptability and interpretability. The model is validated through a case study of a long-established agroecological community in India, where ten organic farming alternatives are evaluated across Functional-, Biological-, and Practice-Based Knowledge domains. Findings The integrated framework identified a set of bridging practices that effectively connect scientific innovation with farmers' experiential ecological wisdom. Through interpretive reasoning, ChatGPT uncovered overlaps among diverse knowledge domains, transforming complex numerical outputs into transparent, context-sensitive insights accessible to both farmers and decision-makers. These outcomes highlight how human-AI collaboration can foster adaptive, transparent, and socially inclusive agroecological transformation. Practical implications The framework enables decision-makers to prioritize sustainable agroecological practices by integrating digital, biological, and experiential knowledge. It enhances transparency, adaptability, and participatory governance within sustainable supply chains. Social implications By combining human reasoning with AI interpretability, the framework promotes inclusivity, ecological resilience, and community empowerment. It supports farmers in making informed, context-aware decisions aligned with Industry 5.0 values. Originality/value This research establishes a conceptualization of a GenAI-integrated fuzzy decision-support system that unites scientific, digital, and indigenous knowledge bases. It advances the Industry 5.0 vision of human-centric, participatory, and knowledge-driven management for sustainable agroecology supply chains.

This study presents a hybrid approach for enhancing solar drying performance through the integration of phase change material (PCM)-based thermal energy storage and machine learning-driven predictive modeling. Conventional solar drying systems are often limited by intermittent solar radiation and temperature fluctuations, resulting in non-uniform drying and reduced efficiency. To overcome these limitations, PCM is employed to store excess thermal energy during peak solar hours and release it during off-sun periods, thereby stabilizing the drying environment and extending operational duration. Experimental investigations on red chilli drying demonstrate a clear performance improvement across system configurations, where drying efficiency increased from 41.76% under natural convection to 60.28% with forced convection, further rising to 70.04% with PCM integration and reaching 77.12% with fin-enhanced PCM trays, representing an overall improvement of approximately 85% compared to the baseline system. In addition, inter-tray moisture variation was reduced from 15 to 20% (without PCM) to 7-8% with PCM and further to 3-5% with finned PCM, indicating significantly improved temperature uniformity and drying consistency. The incorporation of PCM also enabled an extension of drying time by 2-3 h beyond peak solar availability. Machine learning models were applied to capture the nonlinear drying behaviour, with the Support Vector Machine (SVM) achieving superior predictive performance (R2 = 0.88) with reduced error metrics compared to Decision Tree and K-Nearest Neighbor models. The novelty of this work lies in the synergistic coupling of latent heat storage and data-driven modeling to simultaneously enhance thermal efficiency and predictive capability, providing a robust framework for optimizing solar drying systems. This integrated approach offers a significant advancement toward energy-efficient and intelligent agricultural drying technologies.

The application of Gen AI (Generative AI) across multiple sectors like manufacturing and service domains, shows transformative effects to improve socially responsible decision-making and collaborative efforts. Yet it remains insufficiently investigated in the context of a socially responsible supply chain (SRSC) towards sustainable supply chain management (SSCM) in a wider context. Gen AI enables faster reporting and adaptive responses to enhance decision-making, which together improve supply chain flexibility while promoting social responsibility. Although previous research recognizes Gen AI's contribution to social functionality within a supply chain, it does not provide a full theoretical structure for analyzing how Gen AI solutions develop and function in SSCM. Prior research stresses the importance of making people and communities central elements in SSCM from the outset. To address this gap, this research conducts a rigorous qualitative study by analyzing 82 exemplary SSCM cases from manufacturing and service sectors through content analysis. The research explores how organizations can leverage dynamic capability theory (DCT) to adopt and integrate Gen AI systems. The findings demonstrate the stakeholder role in SSCM: 1) NGOs and universities provide essential knowledge and skills together with resources which support sustainable practices; 2) active collaboration with external stakeholders creates competitive benefits while promoting wider implementation of sustainability efforts through imitation. This research delivers a conceptual framework, showing how dynamic supply chain capabilities enabled by Gen AI affect stakeholder alignment towards sustainability goals while mobilizing stakeholders towards SSCM practices; this creates positive effects for wider communities in dynamically evolving Gen AI based SC systems. Our study utilizes micro-foundations of dynamic capabilities to deliver actionable recommendations for managers and outlines future research paths for expanding sustainability practices across multiple dimensions using Gen AI. This study provides helpful insights for professionals, researchers, and leaders to achieve Sustainable Development Goals (SDGs).