Search Results (289)

Entropy is a foundational concept across scientific domains, playing a role in understanding disorder, randomness, and uncertainty within systems. This study applies Shannon’s entropy in information theory to evaluate and manage complexity in industrial supply chain management. The purpose of the study is to propose a quantitative modeling method, employing Shannon’s entropy model as a proxy to assess the complexity in SCs. The underlying assumption is that information entropy serves as a proxy for the complexity of the SC. The research method is quantitative modeling, which is applied to four focal companies from the agrifood and metalworking industries in Southern Brazil. The results showed that companies prioritizing cost and quality exhibit lower complexity compared to those emphasizing flexibility and dependability. Additionally, information flows related to specially engineered products and deliveries show significant differences in average entropies, indicating that organizational complexities vary according to competitive priorities. The implications of this suggest that a focus on cost and quality in SCM may lead to lower complexity, in opposition to a focus on flexibility and dependability, influencing strategic decision making in industrial contexts. This research introduces the novel application of information entropy to assess and control complexity within industrial SCs. Future studies can explore and validate these insights, contributing to the evolving field of supply chain management. Full article

The increasing scale of honey adulteration poses a significant challenge for modern food quality and safety management systems. Honey authenticity, defined as the conformity of products with their declared botanical and geographical origin, is challenging to verify solely through documentation and conventional physicochemical analyses. This study presents an integrated, process-oriented approach for digital honey authentication, building on initial findings from an interdisciplinary research and development project. The approach includes the creation of a comprehensive digital pollen database and the application of AI-driven image segmentation and classification methods. The developed system is designed to support decision-making processes in quality assessment and VACCP (Vulnerability Assessment and Critical Control Points) risk evaluation, enhancing the operational resilience of honey supply chains against fraudulent practices. This study aligns with current trends in the digitization of food quality management and the use of Industry 4.0 technologies in the agri-food sector, demonstrating the practical feasibility of integrating AI-supported palynological analysis into industrial workflows. The results indicate that the proposed approach can significantly improve the accuracy and efficiency of honey authenticity assessments, supporting the integrity and transparency of global honey markets. Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product integrity, leading to deterioration and an increased risk of foodborne illness. Monitoring agrifood supply chains is essential, from packaging to last-mile delivery. Distribution methods that rely on non-automated monitoring systems, such as manual temperature measurements, are error-prone due to the failure of manual treatments and increase the likelihood of product deterioration. Emerging sensor technologies and the rapid development of Information and Communication Technologies offer new possibilities for real-time tracking, enabling stakeholders to maintain optimal conditions and monitor aesthetic, physicochemical, and nutritional quality. This paper proposes a cost-effective temperature and humidity traceability system that utilises wireless sensor networks (WSN) and Internet of Things (IoΤ) services to monitor perishable products within the agrifood supply chain ecosystem. It also provides an overview of recent innovations in sensor technologies, along with food quality indicators relevant to real-time monitoring of food quality. The proposed research examines the available sensor technologies and methodologies that enable continuous monitoring of agrifood supply chains. Moreover, the paper presents a pilot full-scale project from both functional and technological perspectives. Full article

Addressing the challenges of Industry 4.0 in Thailand’s agri-food logistics (AFL), this study develops a structural logistics management model grounded in the Technology–Organization–Environment (TOE) framework, Resource-Based View (RBV), and Dynamic Capabilities (DC) theory. The model integrates four key constructs: standardization, operations management, smart technology, and wastage management targeting cold chain logistics (CCL) systems. Using a mixed-methods design, the study combines in-depth expert interviews with a quantitative survey of 300 logistics firms certified under the Q Cold Chain standard. Structural equation modeling (SEM) analysis confirms the robustness of the model (CMIN/DF = 1.151; GFI = 0.928; RMSEA = 0.022), supporting all five hypotheses. The findings show that standardization significantly enhances both operational performance and the adoption of digital technology, while waste reduction acts as a key mediator linking organizational processes to technological transformation. By highlighting institutional certification as a policy instrument, this research addresses existing gaps in logistics innovation literature. The results inform both theory and practice, supporting Thailand’s strategic transition toward sustainable, digitally enabled agri-logistics ecosystems. Full article

This paper analyses how strategic interactions between actors influence the development of circular economy (CE) initiatives in food systems. Using a case study from Saint-Hyacinthe, a mid-sized and agri-food technopole in Québec (Canada), we investigate how cooperation, competition, and power asymmetries shape CE adoption across the supply chain. Drawing on game theory and a typology of management dynamics, the study identifies four patterns: negotiated management, constrained leadership, hierarchical relationships, and competitive behaviour. Empirical data were collected through two collaborative workshops involving public, private, and community-based actors, resulting in 244 coded entries across 12 boards. These allowed us to assess actors’ interests, attitudes, and capacities in relation to CE strategies at upstream, midstream, and downstream stages. The results show that strategies aligned with dominant interests and existing capacities are more likely to be supported, while those requiring structural change are tolerated or marginalized. Findings highlight the role of incentive mechanisms, institutional flexibility, and coordination in enabling more transformative circular initiatives. By adopting a stage-sensitive perspective, this study also fills a gap in the literature by examining how actor dynamics differ across upstream, midstream, and downstream segments of the food system, contributing to CE research by applying game theory to actor configurations and interaction dynamics in food systems. It calls for further exploration of interdependencies and contextual conditions that either facilitate or hinder the emergence of effective, inclusive, and systemic CE transitions. Full article

Growing concern over food safety and adulteration has thrust milk traceability technologies to the forefront of agrifood supply chains. This qualitative study explores the technological, organisational, and environmental (TOE) determinants of traceability technology adoption in Saudi Arabia’s dairy sector. In-depth semi-structured interviews with nine senior managers from small-, medium-, and large-scale dairy farms were analysed thematically in NVivo. Thematic analysis revealed that technological cost and compatibility played crucial role, while contrary to the prior literature, respondents downplayed technological complexity, arguing that training could offset it. Organisational culture and employee resistance were the primary inhibitors within dairy firms. Saudi Vision 2030, post COVID-19 consumer pressure and competitor pressure emerged as the dominant environmental factors. The findings offer insights for managers and policymakers on how to improve supply chain transparency, operational efficiency, product quality, and consumer trust while advancing several UN SDGs. Full article

Background: The agri-food sector is a major contributor to environmental degradation and emissions, highlighting the need for sustainable practices to mitigate its impact. Within this sector, perishable food crops require targeted efforts to reduce their environmental footprint. Vertical integration is crucial for ensuring alignment between strategic and tactical decision making in supply chain management. This article presents a multi-objective mathematical model that integrates both economic and environmental considerations within the perishable food supply chain, aiming to determine optimal solutions for conflicting objectives. Methods: In this research, we employed combining goal programming with the epsilon constraint approach; this comprehensive methodology reveals optimal solutions by discretizing the values derived from the payoff table. Results: The model is applied to a real case study of the tomato paste supply chain in Ethiopia. To identify Pareto-efficient points, the results are presented in two scenarios: Case I and Case II. Conclusions: The findings emphasize the significant influence of the geographical location of manufacturing centers in supplier selection, which helps optimize the trade-off between environmental impact and total cost. The proposed solution provides decision makers with an effective strategy to optimize both total cost and eco-costs in the design of perishable food supply chain networks. Full article

Food loss and waste are critical challenges in Taiwan’s agri-food supply chain, deteriorating security and resource efficiency. By employing the best–worst method (BWM), a multi-criteria decision-making model was developed in this study to evaluate the severity of losses and wastes. Combining literature review results with expert survey analysis results, key loss points, and mitigation strategies were identified to enhance sustainability and efficiency in Taiwan’s agricultural food system. Among the seven stages of the agricultural food supply chain, supermarket waste (16.95%) was identified as the severest, followed by government policies (16.63%), restaurant waste (15.35%), processing loss (14.71%), production site loss (13.64%), household waste (11.93%), and logistics/storage/distribution loss (10.79%). In the subcategories of each supply chain stage, the eight severe issues were identified as “Inadequate planning and control of overall production and marketing policies” under government policies, “Adverse climate conditions” and “Imbalance in production and marketing” under production site loss, “Inaccurate market demand forecasting” and “Poor inventory management at supermarkets” under supermarket waste, and “Improper storage management of ingredients leading to spoilage” as well as “Inability to accurately forecast demand due to menu diversity” under restaurant waste. The least severe issues included “Poor production techniques” under production site loss. Other minor issues included “Inefficient use of ingredients due to poor cooking skills”, “Festive culture and traditional customs”, and “Suboptimal food labeling design”, all of which contributed to household waste. Based on these findings, we proposed recommendations to mitigate food loss and waste in Taiwan’s agricultural food supply chain from practical, policy, and academic perspectives. The results of this study serve as a reference for relevant organizations and stakeholders. Full article

Under the context of increasing demand for transparency, efficiency, and trust in food systems, digital traceability is emerging as a key strategy for improving value creation across agri-food supply chains. This study investigates how different governance structures influence the design and effectiveness of digital traceability systems. We develop an analytical framework linking four guiding questions (why, where, how, and who) to traceability performance and apply it to five Italian supply chains (wine, olive oil, cheese, pasta, and dairy) through 28 semi-structured interviews with companies, cooperatives, and technology providers. The results show that governance models shape traceability adoption and function. In captive systems (e.g., wine), traceability ensures compliance but limits flexibility, while in modular or relational systems (e.g., pasta and cheese), it fosters product differentiation and decentralized coordination. Across cases, digital traceability improved certification processes, enhanced consumer communication (e.g., via QR codes), and supported premium positioning. However, upstream–downstream integration remains weak, especially in agricultural stages, due to technical fragmentation and limited interoperability. The diverse experience data from company interviews reveal that only 30% of firms had fully integrated systems, and fixed costs remained largely unaffected, though variable cost reductions and quality improvements were reported in the olive oil and cheese sectors. The study concludes that digital traceability is not only a technical solution but a governance innovation whose success depends on the alignment between technology, actor roles, and institutional arrangements. Future research should explore consumer-side impacts and the role of public policy in fostering inclusive and effective traceability adoption. Full article

Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article

Agricultural supply chains face growing scrutiny due to rising concerns over food authenticity, safety, and sustainability. These challenges stem from issues such as contamination risks, fraudulent labelling, and the absence of reliable, real-time tracking systems. Existing systems often rely on centralised databases and fragmented data flows, limiting traceability, data integrity, and end-to-end visibility. While blockchain technology offers potential, most research focuses narrowly on traceability, overlooking its role in establishing secure product identity and its integration with emerging tools. This review investigates how Decentralised Identifiers (DIDs), digital twins, and smart contracts—in conjunction with blockchain—can create verifiable digital representations of agricultural products and automate trust mechanisms. Through an analysis of over sixty recent sources and leading standards (e.g., W3C DIDs, Hyperledger Aries), the study identifies key gaps in interoperability, governance, and system maturity. A layered system architecture is proposed, and its application is demonstrated in a cold-chain case scenario. The paper concludes with a roadmap for empirical validation and policy alignment, contributing a practical and scalable framework for researchers, practitioners, and regulators advancing blockchain-enabled traceability systems. Full article

Background: Growing environmental concerns and the vulnerability of global supply chains to disruptions, such as pandemics, natural disasters, and logistical failures, necessitate the design of sustainable and resilient supply chains. Methods: A novel multi-period mixed-integer linear programming model is developed with the objective of maximizing supply chain profit to design a complete citrus supply chain, which incorporates the production of citrus fruit and juice, and accommodates resilience and sustainability perspectives. Results: A comprehensive citrus supply chain scenario is presented to support the applicability of the proposed model, leveraging real data from citrus supply chain stakeholders in Egypt. Moreover, an actual case study involving a citrus processing company in Egypt is demonstrated. Gurobi software is used to solve the developed model. To build a resilient supply chain which can cope with different disruptions, different scenarios are modeled and strategies for having multiple suppliers, backup capacity, and alternative logistics routes are evaluated. Conclusions: The findings underscore the critical role of resilience in supply chain management, particularly in the agri-food sector. Moreover, the proposed model not only maximizes supply chain profitability but also equips stakeholders with the tools necessary to navigate challenges effectively. Full article

The concept of sustainability in agri-food supply chains (AFSCs) is gaining traction among researchers and practitioners. There has been a considerable effort to define and identify frameworks for assessing, monitoring, and improving sustainability practices within systems and organizations. The multi-capital approach presents an alternative tool for sustainability that integrates various types of capital to provide a deeper understanding and assessment of the impacts across different facets of the system. This review systematically examines existing research on AFSC sustainability indicators and assessment from a multi-capital perspective. Based on 106 articles, 116 indicators from various databases (Web of Science, Scopus, and others) are identified. These indicators are assigned to specific AFSC actors and forms of capital. Their role in evaluating the AFSCs through a sustainability lens is examined. The analysis also identifies the most significant capital associated with each actor. This analysis leads to the development of a structured framework that helps actors assess their resources and pinpoint sustainability challenges. Following this review, a theoretical framework is derived, connecting sustainability capital, key themes, and relevant Sustainable Development Goals. This provides a comprehensive tool for evaluating assets and guiding strategic actions for AFSC. Full article

The agri-food supply chain and other industries that convert agricultural raw materials into various consumer goods generate large quantities of by-products, most of which end up in landfills. This waste, rich in cellulose, provides a significant opportunity for the conversion of agricultural residues into valuable products. In this paper, soybean hulls and hemp waste were subjected to chemical treatments with alkaline (NaOH 2% w/v) and acidic solutions (HCl 1 M) to remove non-cellulosic components and isolate cellulose. The biomass was characterized after each chemical process through FTIR, SEM, EDX, elemental analysis, TGA, and XRD. Lignin was determined following two different procedures, a conventional TAPPI protocol and a method recently proposed in the literature (CASA method). The results indicated that the chemical treatments favored the removal of organic compounds and minerals, increasing the cellulose content in biomass after each step. The purified product of soybean hulls consists of fibers 35–50 µm long and 5–11 µm thick, containing nearly pure cellulose arranged in crystalline domains. Fibers of variable sizes, rich in crystalline cellulose, were isolated from hemp waste. These fibers have diameters ranging between 2 and 60 µm and lengths from 40 to 800 µm and contain considerable amounts of lignin (~14%). Full article