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Purpose: A supply chain consists of raw material suppliers, manufacturers and retailers where inventory of raw materials and finished goods are involved, respectively. Therefore, it is important to find optimal solutions, which are beneficial for both supplier, manufacturer and retailer. Design/methodology/approach: This research focuses on a semi-continuous manufacturing facility by assuming that the production of succeeding cycle starts immediately after the production of preceding cycle. In reality, the inventory of a supply chain system may not be completely empty. A number of products may be left over after the deliveries are made. These leftover inventories are added to the next shipment after the production of required amount to makeup a complete batch for shipment. Therefore, it is extremely important to search for an optimal strategies for these types production facilities where leftover finished goods inventory remains after the final shipment in a production cycle. Considering these scenarios, an inventory model is developed for an imperfect matching condition where some finished goods remains after the shipments. Findings: Based on the previous observation, this research also considers a single facility that follows JIT delivery and produces multiple products to satisfy customers' demand. For this problem a rotational cycle model is developed to optimize the facility operations. Both problems are categorized as mixed integer non-linear programming problems which are to be solved to find optimum number of orders, shipments and rotational cycle policy for multiple products. Also, this solution will lead to estimate the optimum production quantity and minimum total system cost. Research limitations: This research considers the supply chain based on manufacturers point of view and it does not consider the transportation cost associated with supply chain. Next study will be focused on issues with joint decision making, information sharing, and transportation decision. Practical implications: This study will help the managers of refinery and paper industries in making their operation smooth by applying optimizing techniques and robust decision making. Originality/value: Based on the literature, no research was found on continuous production system supply chain and its optimization with JIT delivery. This research will definitely provide a direction for such problem to the researchers.

Cold-chain supply systems depend on a sequence of linked production and logistics decisions. In prepared-food cold chains, quality may deteriorate not because one visible failure occurs, but because testing, traceability records, temperature monitoring, and abnormal-condition reporting are gradually weakened under cost pressure. Once such hidden effort reduction accumulates, external disturbances may push the system from strict assurance to weakened governance. To explain this nonlinear process, an asymmetric evolutionary game is built between prepared-food producers and cold-chain logistics providers, each choosing between strict and weakened quality assurance. White Gaussian noise is introduced to represent random operating shocks, and the two-population strategy system is projected onto a system-level quality-governance coordinate, q. This projection is used as a transparent baseline coordinate rather than as an assumption of linear system evolution. The reduced system is then transformed into a stochastic cusp catastrophe model, with a resilience indicator used to measure the distance from critical transition conditions. Numerical simulations show that quality assurance costs and short-term cost-saving benefits move the system toward a weakened-governance basin, whereas external incentives, coordination degree, and credible accountability mechanisms support recovery toward strict collaboration. The framework offers a scenario-based resilience diagnosis approach for identifying threshold effects in cold-chain quality governance. Digital traceability, temperature-data sharing, incentive alignment, and accountability rules are further interpreted as operational innovations that improve resilience and reduce avoidable quality losses in sustainable cold-chain operations.

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects.Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited.Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging field.Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging field.

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

PurposeThe aim of this study is to examine the influence of consumer preferences for overseas green products and the implementation of blockchain technology on the performance of a supply chain, which comprises an overseas manufacturer and a domestic e-commerce platform. This research endeavors to identify the optimal pricing decisions and strategies for both the manufacturer and the platform in the context of the expanding e-commerce and globalization of the economy.Design/methodology/approachThe authors propose and analyze four distinct models based on the selection of selling contracts by the manufacturer and the adoption strategy of blockchain by the platform, using game theory to obtain the optimal solutions for these models.FindingsThe authors show that consumer migration promotes the manufacturer's green inputs, while the expansion of green consumer proportion is not conducive to it. They also show that blockchain technology has the potential to effectively limit manufacturer cannibalization. Interestingly, the study reveals a cascading effect of advantage where the manufacturer's profit variation trend changes only with the integration of pricing power advantage and blockchain technology inputs. This effect suggests that the equilibrium strategy is achievable under the agency contract with blockchain adoption, while Pareto improvement can be obtained with blockchain technology under both selling contracts.Research limitations/implicationsThis research could be extended in several possible directions. First, future work could explore outsourcing strategies for overseas manufacturers. Second, more types of consumer heterogeneity and different risk preferences could be considered. Third, this study can be extended by further exploring the design of mechanisms under asymmetric demand information to make the model more realistic.Originality/valueThe authors examine the impact of market segmentation and consumer preferences on green supply chain decisions, and analyze supply chain members' strategic choices for selling contracts and blockchain adoptions. The research also sheds light on the theoretical underpinnings and practical applications of green supply chain development and blockchain applications.

This paper introduces a novel chaotic three-tier supply chain system (CSCS) that integrates both absolute function and sinusoidal nonlinearities into the classical Hamidzadeh model to enhance its dynamic complexity. The key improvement in the proposed model is that it exhibits higher Lyapunov exponent values ( l 1  = 0.2121) compared to the existing models, conforming stringer chaotic dynamics. Further, amplitude and location of the chaotic signal can be controlled in the proposed model. The proposed model captures the interactions among manufacturers, distributors, and retailers while exhibiting rich chaotic behaviors characterized through Lyapunov exponents, Lyapunov dimensions, and bifurcation analysis. Numerical simulations reveal improved chaotic intensity compared to existing CSCS models, with clear transitions between fixed points, periodic orbits, and chaos under parameter variations. To improve practical applicability, two control strategies are implemented: amplitude control, enabling systematic scaling of state variables without altering the chaotic nature, and offset boosting control, which shifts attractors in phase space while preserving system dynamics. Comparative analysis demonstrates the superior dynamic range and flexibility of the proposed model, offering valuable insights for designing resilient and adaptive supply chain networks under uncertainty.

Background The health supply chain system is essential for the optimum performance of the healthcare system. Despite increased investments in the health supply chain system, access to quality Essential Medicines and Health Supplies remain a big challenge in Uganda. This article discusses the structure, performance, and challenges of the health supply chain system in Uganda. It provides reflections and implications for ongoing interventions for system strengthening. Discussions The findings highlight several issues and challenges affecting the health supply chain system from functioning optimally across all levels of the health system. The challenges identified include an ineffective structure to support planning, coordination and management, inadequate funding, shortage of skilled staff, weak regulatory and governance structures at national and sub-national levels, and slow adoption and use of Electronic Logistics Information Systems to support supply chain processes and functions. Overcoming these challenges will require greater investments to improve policy development and implementation, infrastructure, equipment and support systems, knowledge and skills of supply chain personnel, increased funding and improving governance and accountability.

Aiming at the problem that the upstream manufacturer cannot accurately formulate the production plan after the link of the nonlinear supply chain system changes under emergencies, an optimization model of production change in a nonlinear supply chain system under emergencies is designed. Firstly, based on the structural characteristics of the supply chain system and the logical relationship between production, sales, and storage parameters, a three-level single-chain nonlinear supply chain dynamic system model containing producers, sellers, and retailers was established based on the introduction of nonlinear parameters. Secondly, the radial basis function (RBF) neural network and improved fast variable power convergence law were introduced to improve the traditional sliding mode control, and the improved adaptive sliding mode control is proposed so that it can have a good control effect on the unknown nonlinear supply chain system. Finally, based on the numerical assumptions, the constructed optimization model was parameterized and simulated for comparison experiments. The simulation results show that the optimized model can reduce the adjustment time by 37.50% and inventory fluctuation by 42.97%, respectively, compared with the traditional sliding mode control, while helping the supply chain system to return the smooth operation after the change within 5 days.

Amidst the intertwined complexity and fragility of global supply chain networks, there is an urgent need to break through the limitations of traditional risk management’s static modeling. This study innovatively integrates dynamic Bayesian networks with lightweight convolutional neural networks to construct a full-chain intelligent management system encompassing risk identification, prediction and early warning, and decision-making response. The core contribution lies in the pioneering time-delay parameterized dynamic modeling method, which addresses the challenge of accurately characterizing the propagation of multi-level interruption events by quantifying the lag effects and cascading paths of risk conduction between nodes. A lightweight feature extraction architecture based on channel compression and integer quantization is designed, compressing the model size to the order of 0.5 MB, enabling millisecond-level real-time response from edge devices. An event-driven cross-modal attention mechanism is developed, dynamically integrating multi-source heterogeneous information such as logistics monitoring images, inventory time-series data, and policy texts, strengthening the decision-making weight allocation of key risk signals. Empirical research shows that this framework exhibits significant advantages in the manufacturing, retail, and healthcare industries: it significantly improves the accuracy of interruption prediction compared to traditional methods, demonstrates robust performance under noise interference, and reduces the false alarm rate to industry-leading levels; through a dynamic strategy linkage mechanism, it effectively controls the loss caused by supply chain interruptions, and the edge deployment solution supports full-domain coverage from cloud servers to mobile terminals, achieving 28ms-level risk early warning response on devices such as Huawei Mate30. This achievement provides a technical paradigm for building resilient supply chain systems that combines theoretical rigor with engineering feasibility, significantly enhancing the digital risk prevention and control capabilities in the manufacturing and logistics industries.

During the research with respect to the traditional supply chain, Enterprise autonomy is ignored. Within this thesis, from the perspective of CAS, the dynamic growth of supply chain was inspected by using CAS versus evolutionary game theory. Within supply chain, the corporate was viewed as agent. In the same way, the variety branches of the corporate were viewed as sub-agents. The agent-based model of supply chain was set up in this thesis. At the same time, supply chain dynamic evolution was simulated through swarm. The effective solution to coordinate the supply chain main body behaviour was explored too. Within this thesis, the evolutionary trait of supply chain is explored, and a reference for supply chain governance is provided.