The agri-food supply chain (AFSC) needs resilience beyond organizational and dyadic levels due to its complex adaptive nature and increasing number of disruptions. Based on the investigation of expert perceptions, this study aims to explore the influence structure of social-ecological resilience capabilities to aid in understanding the complexity of resilience and informing decision making. Moreover, the prioritization of big data analytics (BDA) practices can support agri-food entities in adopting best practices for resilience improvement by using a fuzzy hybrid multiple-criteria decision analysis approach. To contribute to the two aims, first, responses from 26 distinguished supply chain resilience scientists were analyzed using a fuzzy DANP approach to uncover the influence relationships and priority weights of 19 organization, supply chain, and industry level social-ecological resilience capabilities. Second, 14 BDA practices categorized into three groups of sensing, seizing, and transforming―based on the dynamic capabilities perspective―were prioritized as judged by a total of 19 managers in three large food retailers using a fuzzy TOPSIS model, considering their assessed contribution to strengthening resilience capabilities. These capabilities have also been triangulated with secondary data to contextualize and corroborate case descriptions. The findings suggest the high prominence and net influence of adaptability and agility, alongside the centrality of collaboration, supply flexibility, and risk-aware culture within the elicited influence structure in AFSCs. Production and supply chain managers and policymakers in AFSCs can use the results to assess organizational, supply chain, and industry resilience, guiding strategic planning based on identified capability interdependencies and priority weights. In addition, retail managers can use the evaluation method to reach a consensus in their organization to better understand and implement the critical BDA practices that are prioritized for resilience enhancement in their specific context. • Adopting the theoretical angles of social-ecological resilience and dynamic capabilities • Analyzing data from 26 supply chain resilience scientists and 19 managers in three large food retailers • Uncovering the influence structures between social-ecological resilience capabilities • Developing an empirical ranking model of Big Data Analytics practices to support resilience at different levels in agri-food supply chains • Guiding future theory testing in supply chain resilience and Big Data Analytics

In supply chain management (SCM), a pivotal challenge is minimising inventory costs alongside enhancing efficiency among supply chain members. This paper proposes a two-phase supply chain planning model that leverages a centralised strategy and collaborative mechanisms. Phase I develops an inventory model based on the traditional EOQ framework, incorporating additional factors, including traffic congestion, sustainability, price, and shortage costs. The optimal solutions from Phase I are utilised in the inverse data envelopment analysis (InDEA) in Phase II to analyse the merging processes within a supply chain. The InDEA framework is further extended through a scenario-based robust model within the framework of multi-choice goal programming approach, incorporating decision-maker preferences and handling uncertainties. Our study demonstrates the effectiveness and applicability of our approach through a numerical experiment and an in-depth case study, revealing a significant reduction in costs and an enhanced overall customer experience, thus validating the proposed methodology's impact on supply chain efficiency.

Network-Driven carbon attribution and mitigation in biopharmaceutical supply Chains: Integrating life cycle assessment and social network analysis

Hybrid Simulation–expertise approach to balancing cost and resilience in global herbal supplement supply chains

A hybrid metaheuristic model for coordinating sustainable supply chains with subsidy-induced profit structures

Research on environmental cost sharing mechanism of power battery supply chain considering blockchain technology investment

Can carbon insetting deliver credible sustainability outcomes in forest-risk supply chains? A critical perspective from soy and cattle production in Argentina’s Gran Chaco

• Local and circular initiatives are often small and fragmented. • Little is known about how to effectively scale local circular supply chains. • We present 17 factors for scaling across 8 established amplification processes. • We contribute factors to increase the size and transformative impact of initiatives. The fashion and textiles industry operates a global supply chain that causes harm to the environment, emitting pollution and generating waste across all stages of production, consumption and disposal. The implementation of a local circular economy would reduce impact, shorten import and export distances while retaining the value of materials through practices like reuse and recycling. However, current local and circular initiatives operate in a system that is incomplete to provide sufficient support structures for their operations, meaning they are often small, fragmented, and at risk of cessation. To address this, five focus groups with fashion industry stakeholders were conducted where factors for scaling at a local and national level were discussed. Data analysis revealed 17 factors for scaling, two of which were unique to scaling nationally. These offer ways for local circular clothing supply chains to stabilise, speed up, grow, replicate, transfer, spread, scale up and scale deep. The results provide a new insight on how to increase the scale and transformative impact of local circular initiatives emerging in towns and cities across the UK. The research is useful for the industry and policymakers in informing the development of purposive interventions that seek to step beyond current niche and fragmented solutions and build circular systems that can overtake the current linear, energy intensive, pollutive and waste generating system. Significantly, this will facilitate a shift away from global production and distribution, to grow more locally and systemically robust initiatives at the local and national scale.

The complexity of sustainable supply chains (SSCs) requires the integration of risk management across risk assessment and mitigation processes. However, most previous studies have discussed these two aspects separately, thereby failing to provide structured, effective mitigation priorities. This study aims to develop a hybrid framework that integrates Interpretive Structural Modeling (ISM), MICMAC analysis, Risk Priority Number (RPN), and Risk Mitigation Number (RMN) to identify, evaluate, and mitigate risks in the carrageenan-based agri-food industry supply chain. The ISM and MICMAC methods are used to map risk relationships and hierarchies based on driving power and dependence power, while RPN is applied to measure the severity, frequency of occurrence, and detectability of risks. Furthermore, RMN is used to determine the most effective priorities for mitigation strategies. The study identified 11 priority risks, with the main risks being delays in raw material delivery, suppliers’ inability to meet demand, and fluctuations in raw material prices. The most effective mitigation strategies are strengthening cooperation, collaboration, and information sharing with suppliers (RMN = 822) and implementing better inventory management (RMN = 738). This study contributes theoretically through the development of an integrative framework that links risk interdependencies, quantitative risk prioritization, and mitigation strategy selection. Practically, this framework provides evidence-based guidance for decision-makers in the carrageenan industry to improve operational sustainability and supply chain resilience.

The growing complexity and uncertainty of sustainable supply chain environments demand advanced digital capabilities that sustain competitiveness through effective decision-making. This study investigates how Generative AI (GenAI)-enabled digital dexterity strengthens decision-making performance and, in turn, contributes to organizational future performance. Integrating dynamic capabilities theory with institutional theory, we develop and test a model in which decision-making quality and decision-making efficiency represent distinct dimensions of decision-making performance, while ethical identity and regulatory governance effectiveness shape the strength of relationships. Survey data were collected from 296 Chinese firms across multiple industries and analysed using partial least squares structural equation modeling. The results show that GenAI-enabled digital dexterity significantly enhances both decision-making quality and decision-making efficiency, and that both dimensions are positively associated with organizational future performance. Ethical identity further amplifies the positive effects of GenAI-enabled digital dexterity on decision-making outcomes, highlighting the importance of normative commitments for responsible and credible use of GenAI. Regulatory governance effectiveness exhibits asymmetric boundary effects, dampening the performance value of decision-making quality while strengthening the contribution of decision-making efficiency to future performance. This study advances research on digital transformation and sustainable operations by clarifying the distinctive role of GenAI-enabled digital dexterity, identifying decision-making performance as a central mechanism, and explaining how institutional governance conditions shape value realization. It also offers practical guidance on how firms can build and govern GenAI-enabled capabilities to support future-oriented performance in sustainability-focused supply chains. • Shows how GenAI-enabled digital dexterity improves decision-making in supply chains. • Identifies ethical identity as a normative amplifier of GenAI decision benefits. • Reveals regulatory governance as a boundary condition shaping GenAI decision outcomes. • Conceptualises and validates GenAI-enabled digital dexterity as an organizational capability. • Offers managers guidance on extracting value from GenAI in complex environments.

Critical factors for sustainable perishable food supply chains: Insights from Malawi banana supply chain

Trust-centered digital supply chain finance adoption model: mitigating opportunistic behavior and enhancing collaboration for Industry 5.0 and social sustainability

The smart manufacturing revolution: how industrial robotics reshape supplier networks

The hidden environmental footprint of fashion’s smallest parts

The impact of artificial intelligence and group effects on supply chain resilience in enterprises

Impact of power structure on probabilistic selling in supply chains

Drug supply chain disruptions and outpatient medication shortages in the Veterans Health Administration, 2017-2020.

Digital green synergies: linking green cyber-physical systems, green supply chains integration, and green business intelligence to boost SMEs sustainability

Porous carbon from lignocellulosic biomass with emphasis on corn plant waste residue for energy storage

Integrating advanced imaging techniques with Industry 4.0 technologies for real-time quality monitoring in the agri-food sector: A review