Search Results (109)

Rebranding has emerged as a complex organizational phenomenon, particularly in service contexts where value is created through interactive and process-based relationships. Despite extensive research in branding, the role of core service characteristics in shaping the rebranding process remains insufficiently examined. This study therefore investigates how intangibility, inseparability of production and consumption, heterogeneity, and perishability influence the rebranding process in service organizations, with particular attention to variations in the patterns and intensity of their influence across different service contexts. A mixed-methods research design was employed, combining semi-structured interviews with twelve purposively selected senior branding professionals and quantitative analysis of expert assessments using nonparametric statistical techniques appropriate for exploratory research. The findings indicate that core service characteristics are relevant to the rebranding process and contribute to its qualitative distinctiveness in comparison with the rebranding of physical goods. However, their influence is not uniform: intangibility and heterogeneity emerge as consistently strong and stable factors, whereas inseparability and perishability exhibit more variable and context-dependent patterns. Furthermore, the type of service industry is identified as an analytical condition systematically associated with these differentiated forms of influence. The study contributes by conceptualizing rebranding in service organizations as a context-sensitive and process-oriented phenomenon. It shows that core service characteristics do not influence rebranding uniformly but through differentiated patterns that vary across service-industry contexts. These findings extend the theoretical understanding of rebranding and support a more nuanced approach to strategic decision-making in service-based brand management. Full article

Across production and processing systems, biological residuals are inconsistently defined, with the same materials treated as waste, by-products, or resources depending on context. This ambiguity constrains the identification of valorization pathways and limits the design of sustainable and resource-efficient operational strategies. This study addresses the issue by compiling a sector-resolved inventory of 94 bioresiduals across 12 bioeconomy-related activities. Analyzing 1763 firm–bioresidual observations from a national survey in Estonia using binomial logistic regression. The results show that bioresidual use is primarily shaped by operational and data-handling practices, particularly collection and accounting, rather than by structural firm characteristics. Separate collection emerges as a key precondition for higher-value use, while accounting practices are associated with external and energy-related pathways by increasing visibility and traceability. In contrast, irregular or seasonal bioresiduals tend to default to waste handling due to variability and perishability. The findings also indicate that many effective uses remain internal to production systems and are under-documented. Improving the definitions and monitoring practices of bioresiduals could support more efficient and sustainable resource management by reducing biowaste generation and enhancing coordination across value chains, thereby fostering the development of a circular bioeconomy. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital twin along with an agentic AI-based dynamic goal reset mechanism. The main methodological innovation of this study is not in the separate development of each of these components but in their structured integration in the form of a self-regulating decision-making loop in which the priority of goals is dynamically adjusted based on the current state of the system. Computational results based on real and simulated data show that the proposed framework reduces the total logistics cost by about 4–5% and reduces product waste by about 13% while simultaneously improving the service level by about 4%. Resilience analysis shows faster performance recovery in the face of operational disruptions, and scalability results confirm the controlled growth of computational time with increasing problem size. These findings demonstrate the effectiveness of integrating adaptive digital twins and agentic AI in a multi-objective fuzzy optimization environment for intelligent and resilient food logistics management. Full article

A considerable proportion of perishable goods, including fruits and vegetables, deteriorate prior to reaching customers. Inadequate refrigeration infrastructure, particularly in developing nations with arid climates and markets distant from agricultural sources, accounts for most of these losses. A food cold chain has three primary phases: pre-cooling, cold storage, and refrigerated transportation. All phases of the cold chain rely fundamentally on refrigeration to preserve perishable products at designated temperatures, relative humidity, and CO₂ concentrations, thus prolonging their shelf life. Solar-driven or aided refrigeration systems use solar energy to power cooling systems and preserve the food in the cold chain. These systems are especially beneficial in off-grid or developing areas for preserving perishable goods such as fruits, vegetables, and other food items, mitigating postharvest losses that can exceed 30–50% in areas with inconsistent energy supplies. Despite progress in efficiency and scalability, numerous research gaps remain across technological, economic, social, policy, and regional dimensions, including technical aspects, optimization, and integration. There is a need to enhance energy-efficient designs, particularly by managing solar intermittency to address non-uniform cooling, which leads to inconsistent ripening and spoilage, and by integrating sustainable refrigerants to mitigate environmental impact. Further development is necessary for micro-scale, transportable, or decentralized systems designed for small farms, while economic and financing obstacles include high upfront costs and limited financial accessibility. Substantial deficiencies exist in creating affordable models and funding channels for small-scale agriculturalists. Addressing these deficiencies could expedite adoption, thereby reducing global food loss and waste (accounting for 8–10% of GHG emissions) while improving food security. Future research must emphasize multidisciplinary methodologies that amalgamate engineering, economics, and social sciences to provide comprehensive solutions. Full article

Firms increasingly develop dynamic pricing policies to maximize revenue for perishable products with limited inventory over a finite selling horizon. This trend is enabled by the growing availability of sales data and is observed across industries such as airlines, hotels, cruise lines, fashion, and seasonal retail. Given customer heterogeneity, firms may further adopt discriminatory pricing across customer groups. However, excessive price disparities can trigger legal risks and consumer backlash, motivating price fairness constraints that bound inter-group price differences in each selling period. We formulate this problem as an action-constrained Markov decision process (ACMDP) with unknown demand functions and adopt a model-free deep reinforcement learning (DRL) framework. However, standard DRL algorithms for unconstrained MDPs cannot directly handle these fairness constraints. Therefore, we introduce an optimization-based shielding mechanism. From the DRL pricing agent’s perspective, this mechanism converts the ACMDP into a shield-induced unconstrained MDP. Meanwhile, it guarantees constraint satisfaction for all executed prices. Building on this framework, we propose the Shield Soft Actor-Critic (Shield-SAC) algorithm. This is the first Shield-SAC method for fairness-aware pricing under instantaneous and hard price fairness constraints. We test it in two simulated markets of different scales and validate that Shield-SAC achieves strong revenue performance while consistently enforcing the price fairness constraints during both training and deployment. Full article

Remote Indigenous communities experience persistent inequities in access to fresh and nutritious foods due to the fragility of perishable food supply chains (PFSCs). Disruptions across procurement, transportation, storage, retail, and limited local production restrict access to perishable foods, contributing to food insecurity and diet-related health risks. This scoping literature review synthesizes evidence from 84 peer-reviewed, grey, and unpublished sources across fourteen countries to map PFSC management (PFSCM) challenges affecting food access in remote Indigenous communities worldwide and to synthesize reported practices implemented to address these challenges. PFSCM challenges were identified across all supply chain levels, and five categories of reported practices emerged: PFSC redesign strategies, forecasting and decision-support models, technological innovations, collaboration and coordination mechanisms, and targeted investments. These findings informed the development of a multi-scalar conceptual framework comprising seven interconnected PFSCM clusters that organize how reported practices are associated with multiple food access dimensions, including quantity, affordability, quality, safety, variety, and cultural acceptability. This review contributes an integrative, system-oriented synthesis of PFSCM research and provides a conceptual basis to support future scholarly inquiry, comparative inquiry, and policy-relevant discussion of food access and health equity in remote Indigenous communities. Full article

Food loss and waste (FLW) is a chronic problem across food systems worldwide, with meat being one of the most resource-intensive and perishable categories. The perishable character of meat, combined with complex cold chain requirements and consumer behavior, makes the sector particularly sensitive to inefficiencies and loss across all stages from production to consumption. This review synthesizes the latest advancements in new preservation technologies and supply chain efficiency strategies to minimize meat wastage and also outlines current challenges and future directions. New preservation technologies, such as high-pressure processing, cold plasma, pulsed electric fields, and modified atmosphere packaging, have substantial potential to extend shelf life while preserving nutritional and sensory quality. Active and intelligent packaging, bio-preservatives, and nanomaterials act as complementary solutions to enhance safety and quality control. At the same time, blockchain, IoT sensors, AI, and predictive analytics-driven digitalization of the supply chain are opening new opportunities in traceability, demand forecasting, and cold chain management. Nevertheless, regulatory uncertainty, high capital investment requirements, heterogeneity among meat types, and consumer hesitancy towards novel technologies remain significant barriers. Furthermore, the scalability of advanced solutions is limited in emerging nations due to digital inequalities. Convergent approaches that combine technical innovation with policy harmonization, stakeholder capacity building, and consumer education are essential to address these challenges. System-level strategies based on circular economy principles can further reduce meat loss and waste, while enabling by-product valorization and improving climate resilience. By integrating preservation innovations and digital tools within the framework of UN Sustainable Development Goal 12.3, the meat sector can make meaningful progress towards sustainable food systems, improved food safety, and enhanced environmental outcomes. Full article

Berries—including strawberries, blueberries, raspberries, blackberries, cranberries, and several less commonly cultivated berry species—are highly valued for their sensory quality and rich content of bioactive compounds, yet they are among the most perishable horticultural products. Their soft texture, high respiration rate, and susceptibility to fungal pathogens lead to rapid postharvest deterioration and significant economic losses. This review synthesizes advances in berry postharvest management reported between 2010 and 2025. Conventional strategies such as rapid precooling, cold-chain optimization, controlled and modified atmospheres, and edible coatings are discussed alongside emerging non-thermal technologies, including UV-C light, ozone, cold plasma, ultrasound, biocontrol agents, and intelligent packaging systems. Particular emphasis is placed on the instability of anthocyanins and other phenolic compounds, microbial spoilage dynamics, and the influence of cultivar genetics and preharvest factors on postharvest performance. The review also highlights opportunities for circular-economy applications, as berry pomace, seeds, and skins represent valuable sources of polyphenols, dietary fiber, and seed oils for use in food, nutraceutical, cosmetic, and bio-based packaging sectors. Looking ahead, future research should prioritize integrated, multi-hurdle, low-residue postharvest strategies, the scale-up of non-thermal technologies, and data-driven cold-chain management. Overall, coordinated physiological, technological, and sustainability-oriented approaches are essential to maintain berry quality, reduce postharvest losses, and strengthen the resilience of berry value chains. Full article

Due to the highly perishable nature of fresh products, consumers often worry about product quality, which significantly dampens their purchase intentions. To address this and boost consumers’ willingness to pay, many fresh foods enterprises resort to sales manipulation tactics as a way to attract customers and gain a competitive edge. To further explore whether such yields net benefits to competing firms and the fresh product market, this research develops a game model involving two competing members selling fresh products. The model analyzes how sales manipulation affects the product prices, market shares, and profits of both firms across four scenarios. Finally, numerical analysis supports the robustness of findings and provides insights for establishing management guidelines for fresh product enterprises and the market. Full article

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article

Fresh agricultural products have significant seasonality and perishability, and their cross-regional sales often face differences in market demand, price, and sales volume. In the context of government quality regulation, competition among retailers in different regions drives supply chain members to improve product quality, expand sales, and reduce losses. However, conflicts of interest under decentralized decision-making may lead to overall inefficiency. This article constructs a supply chain model consisting of a single Manufacturer and two regional Retailers to study the quality competition and coordination mechanism of cross-regional fresh food supply chains under government supervision. By comparing centralized and decentralized decision-making, it is found that although quality improvement in decentralized mode helps enhance competitiveness and sales performance, it is difficult to effectively increase profits and may even lead to a decline in profits. Therefore, this article proposes a cost-sharing contract to achieve supply chain coordination. Research has shown that this contract can effectively improve the overall profit of the supply chain and achieve Pareto improvement; under high market demand and strict regulatory penalties, the total profit of the supply chain increases, but the dominant Retailer benefits more, which can easily trigger the “Matthew effect”. The research results reveal the comprehensive impact of quality investment, contract coordination, market demand fluctuations, and regulatory intensity on supply chain performance, providing theoretical basis and management insights for improving the collaborative efficiency and policy design of cross-regional fresh food supply chains. Full article

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

China’s National Famous, Special, Excellent, and New Agricultural Products are key rural economic assets, yet their spatial patterns and sustainability drivers remain underexplored. Based on the geospatial data of 1932 National Famous, Special, Excellent and New Agricultural Products in China, this study systematically analyzes their spatial distribution pattern by using GIS spatial analysis techniques, including the standard deviation ellipse, kernel density estimation, geographic concentration index and Lorenz curve, and quantitatively explores the driving factors of sustainable development by using geographic detectors. The research results of this paper are as follows. (1) The spatial distribution shows a significant non-equilibrium characteristic of “high-density concentration in the central and eastern part of the country and low-density sparseness in the western part of the country” and the geographic concentration index (G = 22.95) and the standard deviation ellipse indicate that the center of gravity of the distribution is located in the North China Plain (115° E–35° N), and the main direction extends along the longitude of 110° E–120° E. (2) Driving factor analysis showed that railroad mileage (X10) (q = 0.5028, p = 0.0025 < 0.01), highway mileage (X11) (q = 0.4633, p = 0.0158 < 0.05), and population size (X3) (q = 0.4469, p = 0.0202 < 0.05) are the core drivers. (3) Three-dimensional kernel density mapping reveals that the eastern coast and central plains (kernel density > 0.08) form high-density clusters due to the advantages of the transportation network and market, while the western part shows a gradient decline due to the limitation of topography and transportation conditions. The study suggests that the sustainable development of National Famous, Special, Excellent, and New Agricultural Products should be promoted by strengthening transportation and digital logistics systems, enhancing cold-chain distribution for perishable goods, tailoring regional branding strategies, and improving synergy among local governments, thereby providing actionable guidance for policymakers and producers to increase market competitiveness and income stability. The study provides a quantitative, policy-oriented assessment of China’s branded agricultural resource allocation and its sustainability drivers, offering specific recommendations to guide infrastructure investment, e-commerce logistics enhancement, and targeted subsidy design for balanced regional development. The study highlights three key contributions: (1) an innovative integration of geospatial analytics and geographical detectors to reveal spatial patterns; (2) clear empirical evidence for policymakers to prioritize transport and digital logistics investments; and (3) practical guidance for producers and brand managers to enhance product market reach, optimize supply chains, and strengthen regional competitiveness in line with sustainable development goals. Full article