Supply Chain Network Research Articles

Does government food demonstration interventioninfluence household dietary diversity in the Upper West Region of Ghana?

Dietary diversity is crucial in ensuring food and nutrition security. In low-middle-income countries, people frequently prioritize the quantity of food they consume over its quality due to a lack of availability and financial limitations. As a result, achieving dietary diversity is often overlooked in favor of ensuring adequate caloric intake. Through a social cognitive theory perspective, our study examines the relationship between food demonstrations and household dietary diversity in Ghana's Upper West Region utilizing cross-sectional survey data from 517 smallholder farmer households. The results from ordered logistic regression presented in odds ratio (OR) show that participating in food demonstrations (OR: 2.585, p<0.01), engaging in home gardening (OR: 1.932, p<0.001), having access to credit (OR: 1.609, p<0.01), self-rated good nutritional status (OR: 1.747, p<0.01), and Waala ethnicity (OR: 3.686, p<0.001) were all positively associated with high household dietary diversity. Conversely, living in the Wa West district was associated with lower dietary diversity (OR: 0.326, p<0.001). Our research findings suggest that policymakers may want to consider implementing community-based educational programs, such as home and school visits for food demonstrations and sensitizations, promoting mother-to-mother support groups for dietary diversity education, nutrition counseling services, and using role-play and local media. In addition, strengthening local agricultural policies through food banks, indigenous seed development, and mobile food markets and enhancing public-private partnerships like the Ghana Schools Feeding Programme and National Food Buffer Stock company could improve the supply chain and distribution networks for diverse food items. Implementing these interventions in the Upper West Region of Ghana could improve health, well-being, food security, and nutritional outcomes.

Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network

The escalating frequency and severity of disasters on a global scale have sparked inquiries into the efficacy of current disaster planning strategies in various scenarios. Despite the pivotal role of humanitarian supply chain planning in aiding impacted populations, much of the existing research is grounded in simplistic assumptions that limit their practicality. Addressing this gap, our proposed bi-objective model aligns response time and total cost, while also accommodating the collaboration between non-governmental organizations and governmental organizations to mirror real-world intricacies. This study comprehensively delves into various logistics aspects, encompassing pre- and post-disaster phases, including location, allocation, supplier selection, fleet size, supply contract, inventory, distribution, and transportation. This multifaceted approach enhances the model's suitability for managing genuine real-world emergencies. To mitigate disruption risks and unforeseen events, the model introduces pre-positioning, quantity flexibility contract, backup suppliers, and a multi-sourcing policy, thus enhancing the resilience and reliability of the logistics network. We present solutions for diverse scenarios through a scaled weighted sum method, while tackling uncertainty via a heuristic approach known as the backward scenario reduction method. Furthermore, to manage large-scale problems within an acceptable time frame, we propose an advanced hybrid algorithm. This algorithm synergizes a parallel differential evolution framework with reinforcement learning-enhanced local search mechanisms, aiming to improve both computational efficiency and solution accuracy. Finally, we validate the model's applicability through a real case study focusing on a flood scenario in Iran.

Effective hydrogen supply chain management framework considering nonlinear multi-stage process uncertainties

The large amount of carbon emitted due to industrial development is posing serious climate change. Under these circumstances, hydrogen energy is being used as a major energy source and is attracting attention as an alternative energy to fossil fuels, characterized by high carbon emissions. While the use of hydrogen has the obvious benefit of a low carbon footprint, the hydrogen production process possesses a high carbon footprint difference depending on the source of utilized electricity. This study considers several situations arising in an electrolysis-based hydrogen supply chain network (HSCN). A multi-stage stochastic programming (SP) is modeled to consider the uncertainty of demand and transportation capacity generated in HSCN. Moreover, a typical stochastic model has a chain rule between the decision variables resulting from nonlinearity and uncertainty, and these features make it difficult to derive an effective solution. Thus, in this study, a new methodology, Weighted Scenario Sample Average Approximation (WSSAA), is proposed to derive an effective solution for the multistage SP model considering various scenarios under the hydrogen supply chain. While a general stochastic programming approach fails to generate feasible solutions with volatile HSCN uncertainties, the proposed WSSAA framework provides feasible decisions considering multiple scenarios. The effectiveness of the proposed model applied with WSSAA is demonstrated through comparative experiments over the models used in existing studies.

A Green Supply Chain with Sales Effort under a Cost-Sharing Contract

Due to social and psychological factors, the COVID-19 pandemic has impacted international trade, dampened consumption globally, and resulted in conservative investment and spending. To stimulate economic recovery while promoting the establishment of a positive consumption awareness among people, enterprises endeavor to enhance competitiveness and expand market share through various means, such as advertising and discounting. With more attention paid to environmentally friendly products, there are greater challenges encountered by green supply chain management. In this study, a green supply chain network problem is analyzed, involving a manufacturer and a retailer. In addition to the construction of centralized and decentralized decision models, two cost-sharing contracts are adopted to reduce promotion costs for the retailer and improve the level of greenery in products. With the help of game theory, equilibrium decisions can be made by solving the models. According to the results of numerical experiment, cost-sharing contracts can effectively improve the level of greenery in products and the profitability of the supply chain, despite the inability to achieve a win–win situation.

Unlocking the potential of blockchain technology in enhancing the fisheries supply chain: an exploration of critical adoption barriers in China

The application of blockchain technology holds significant potential for improving efficiency, resilience, and transparency within the Fisheries Supply Chain (FSC). This study addresses the critical barriers hindering the adoption of blockchain technology (BT) in the Chinese FSC, recognizing the unique challenges posed by its intricacies. Through a comprehensive literature review, fourteen Critical Barrier Factors (CBFs) were identified, and a grey Delphi method was employed to distill this set. Five pivotal CBFs emerged, including "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network". A subsequent grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis revealed the causal relationships among these factors, categorizing them into effect and cause groups. "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network" were identified as primary influencing factors demanding attention for effective BT integration in the FSC. The findings serve as a valuable resource for FSC stakeholders, assisting in prioritizing efforts to address these barriers. The discerned causal relationships provide guidance for managers in optimizing resource allocation. Ultimately, this research advocates for the adoption of blockchain technology in the fisheries supply chain to enhance overall performance and operational efficiency.

An efficient bi-objective optimization approach for the optimal design of omnichannel supply chain distribution networks with sustainability

ABSTRACT This article studies a bi-objective omnichannel supply chain network design (SCND) problem, while simultaneously minimizing the overall supply chain (SC) cost and associated environmental emissions. A novel bi-objective mixed integer linear programming model is formulated, then an efficient optimization approach is proposed for the problem. Experimental studies on a case study and 210 random testing instances were conducted to evaluate the performance of the proposed model and approach. For the case study, 10 Pareto-optimal solutions were obtained within 1.9 s. For the random testing instances, the average number of Pareto-optimal solutions and computational time were 10.19 and 18.85 s, respectively. The overall results show that the proposed method is efficient and outperforms the adapted non-dominated sorting genetic algorithm II on test instances. Computational results indicate that this approach could assist decision makers in making good decisions, with considerable SC cost saving and carbon emission reductions in different situations.

Stochastic home care transportation with dynamically prioritized patients: An integrated facility location, fleet sizing, and routing approach

We study a home health care (HHC) problem that is characterized by prioritized patients and uncertain demands. In practice, HHC supply chain networks often struggle to meet high demand due to a shortage of service vehicles. Additionally, disruptions caused by natural calamities and pandemics (e.g., COVID-19) further compound these challenges, necessitating the consideration of real-life characteristics such as patient priorities, infrastructure locations, and transportation of medical supplies with uncertain demands. To formulate the problem, we propose a multi-depot and multi-period chance-constrained optimization model with precedence constraints, assuming that the demand quantities for medical supplies are random variables. Since patients’ medical conditions vary in severity, the priority of each patient is translated into a time-dependent potential healthcare cost that changes dynamically over the planning horizon. The solution to the proposed model determines the optimal locations for the base Mobile Health Facilities (MHFs) and the fleet size of HHC vehicles, and generates scheduling and routing plans to visit patients within specified time windows. We propose a unique three-phase solution approach, integrated with stochastic simulation, to address the problem. We then assess the robustness of the proposed model based on a realistic case of HHC service provision in Hong Kong and explore the optimal values for two model parameters, namely the Vehicle Threshold Index and the MHF Threshold Index. The performance evaluation tests show that the proposed solution method is efficient and effective for solving real-world problems.

ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING INVENTORY MANAGEMENT ACROSS GLOBAL INDUSTRIAL MANUFACTURING &amp; SUPPLY CHAIN: A MULTI-COUNTRY REVIEW

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.

Application of IoT technology for enhancing the consumer willingness to return E-waste for achieving circular economy: A Lagrangian relaxation approach

Poor management of waste electronic and electrical equipment (e-waste) has resulted in serious challenges to the environment and, consequently, to living organisms. Increasing consumer willingness to return household e-waste is a practical solution for better e-waste management. In this paper, for the first time, a circular supply chain network is structured by applying internet of things (IoT) technology to increase consumer willingness to return household e-waste and to manage the network more effectively. For this purpose, we formulate a mixed-integer linear programming model whose objective is to minimize strategic costs (setup costs and IoT deployment costs) and operational costs (transportation costs, costs associated with IoT, and processing costs in centers), simultaneously. In addition, we developed a heuristic algorithm based on Lagrangian relaxation to solve the proposed MILP model. The performance of the proposed algorithm is evaluated by comparing its results with GAMS results using eight small-sized simulated problems. Furthermore, eight large-size problems are simulated and solved by the proposed algorithm. Finally, the data of an Iranian knowledge-based company is employed to validate the presented mathematical model and algorithm, and the sensitivity analysis process is used to examine the accuracy of the obtained results.

Risk Management for the Petrodiesel-Biodiesel Fuel Blend Supply Chain in Indonesia

This research addresses the management of risks within inter-business unit networks, focusing on two key levels of risk mitigation: strategic and operational. Prior research primarily explored risk management within specific fuel industry units supplied by a single source. This study represents a novel approach, delving into dual-source fuel strategies and engaging various supply chain stakeholders to identify and mitigate operational risks accordingly. The research findings demonstrate that stakeholders can reduce risks through integrated planning and collaboration among business units, with government support further reducing inter-unit risks. This study’s main contribution is establishing a basis for future research in eco-friendly fuel supply chains, specifically examining the long-term viability of blending petrofuel with biofuel. This extends risk analysis to supply chain networks involving multiple business units. Moreover, this research emphasizes the significance of case studies covering end-to-end product flows in fuel blend supply, encompassing various supply chain functions. Additionally, it investigates stakeholder perceptions of operational risks associated with different business unit types, addressing potential domino effects. It highlights the importance of information technology in risk reduction through collaborative information exchange within the supply chain.

Credit risk contagion in complex companies network–Empirical research based on listed agricultural companies

This study investigates credit risk contagion among listed agricultural companies using data from China's A-share market from 2002 to 2021, examining credit risk contagion mechanisms in the supply chain, investment, and information networks. Furthermore, we examine the moderating effect of network structure on credit risk contagion within networks. The results show that credit risk in the supply chain network has the greatest contagion effect, followed by investment and information networks. Credit risk is mainly transmitted through the capital path, product path, and information path. The network structure plays a crucial role in facilitating the contagion process. State-owned companies and companies with high customer concentration have higher risk resistance capacity. Research also shows a one-year time effect, indicating that the impact of credit risk contagion decreases over time. Finally, this study proposes suggestions for financial institutions, companies, and governments to enhance the risk-resisting abilities of the agricultural industry.

Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems

Purpose This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model. Design/methodology/approach In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts. Findings The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management. Research limitations/implications This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management. Originality/value This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

Resilience of interdependent supply chain networks design and protection under the ripple effect

Firms painstakingly assemble vast supply networks of cyber and physical interdependence. Wars, pandemics, and cyber-attacks have disrupted and rippled recently through these networks. The ripple effect in interdependent networks can be caused by functional and structural failures simultaneously, known as mixed ripple effects, which have been studied less in the past. To help managers enhance network resilience against mixed ripple effects, we propose a design and protection strategy from a network structure perspective. This paper is the first to propose a growth-maturity-decline (GMD) model for designing an interdependent network structure. Then, a protection strategy based on node collaboration and redundancy is proposed. Considering that a single resilience metric makes it difficult to distinguish the performance of different strategies, we develop a three-dimensional quantitative framework to evaluate network resilience by conducting case studies on synthetic and real-life supply chain networks. The simulation results indicate that (i) the GMD model enhances network resilience at different phases; (ii) the threshold of the upper capacity limit parameter for cyber-supply networks is 2.7; and (iii) the more uniform the role distribution of firms, the more resilient the network is. This study can provide more informed management decision support for improving network resilience.

Identifying the influence of land logistic driver cognitive energy impact on supply chain performance through agent-based simulation

Logistic transports link demand generators, distributors, and producers in a supply chain network (SCN). The existence of logistic transports is critical to ensure whole nodes’ economic sustainability. This study explores the impact of human factors on SCN performance through cognitive energy expenditure (CEE) tracking. Agent-based model (ABM) simulation was used to analyze the impact of CEE from truck driver’s electroencephalography (EEG) data to obtain the postsynaptic potential values, which were then transformed to calorific energy. The fleet agents, retailers and distributor models were built based on the East Java, Indonesia, logistic transport route around Karanglo, Gempol, Bungurasih, and Gubeng. The frequency and the peak value of the EEG data, postsynaptic potential, and energy data indicate the same information. All data indicate that more challenging routes have higher frequency and higher peak values. The ABM simulation of the fleet agents shows balanced CEE throughout entire routes due to the precise rest period and eat scheduling. The average delivery success rate was 8 out of 30 or 26.7 % in each simulation time step. Hence, most goods delivery tasks can be completed by fleet agents in a balanced system. As a consequence, the SCN performance is also balanced due to the fluid inventory shift without overstock and stockouts. The rest and eat periods of a fleet agent were scheduled after the CEE has been peaked. The time lag between rest periods and transport operations has to be maintained to overcome fleet agents task buildup. Task buildup has a potential to decay both transport safety and inventory shift rates. Therefore, the upgrade in SCN performance is possible through proper fleet agents scheduling

IDENTIFICATION AND EVALUATION OF DISTRIBUTION RISK IN FOOD SUPPLY CHAIN USING AHP METHOD

The study aims to present a structured approach for identifying and assessing distribution risk within the food supply chain by employing the Analytic Hierarchy Process (AHP) method. Through a comprehensive literature review, various risk factors impacting the distribution network of food supply chains are identified, facilitating the development of a hierarchical framework. This framework integrates multiple criteria and sub-criteria to enable a systematic evaluation of distribution risks, considering aspects such as transportation delays, inventory management, quality control, and supplier reliability. The AHP method is then applied to assign relative weights to these criteria, allowing for a quantitative analysis of risk levels and prioritization of mitigation strategies. The findings reveal that the AHP model offers a valuable decision-making tool for stakeholders in the food industry to proactively manage and mitigate distribution risks, thereby enhancing operational efficiency and ensuring food safety and security. Keywords: identification, evaluation, distribution risk, food supply chain, Analytic Hierarchy Process (AHP), criteria, mitigation strategies, operational efficiency, food safety.

A Distributed Software Agent-centric Framework for Supply Chain Networks Empowered by Blockchain: Insights into Smart Contracts

A Distributed Software Agent-centric Framework for Supply Chain Networks Empowered by Blockchain: Insights into Smart Contracts

Supply chain network optimization for emergency materials considering demand satisfaction

AbstractIn order to effectively guarantee the smooth supply of emergency materials under public health emergencies, first, we predict the number of daily infected people based on the modified SEIRD infectious disease dynamics model and accordingly construct the dynamic demand prediction model of emergency materials so as to catch the change rule of the demand for emergency materials with greater precision. Subsequently, under the premise of demand satisfaction, we optimize the supply chain network of emergency materials and embed two insurance strategies in the supply system in advance: multi‐source purchase and buffer production capacity, to enhance the stability of supply chain by sacrificing part of the cost. Next, a bi‐objective linear programming model based on minimizing system cost and maximizing demand satisfaction is proposed with the intention of realizing the design of emergency materials supply chain network, which balances the risk and cost. Finally, through the analysis of the example, we find that compared with the traditional supply chain network of emergency materials, the total satisfaction rate of the supply chain network optimized by balancing cost and risk is increased by 11.70%, while the cost is increased by only 2.04%, which fully verifies the validity of the optimized design of supply chain network of emergency materials.

Using graph neural network to conduct supplier recommendation based on large-scale supply chain

Driven by economic globalisation, various industries have developed a trend towards high specialisation and vertical division of labor, resulting in vast and intricate supply chain networks. However, unforeseen disasters can cause supply chain disruptions, subsequently impacting the regular production and operations of both upstream and downstream enterprises. To tackle this challenge, this study utilises Graph Neural Networks (GNNs) to synthesise graph structural data within the supply chain network, aiming to identify alternative suppliers to mitigate the impact of disruptions. We construct a large-scale knowledge graph to represent the realistic automotive supply chain network in China. Additionally, we propose a GNN-based framework that utilises information about interactions between buyers and suppliers to recommend alternative suppliers from the knowledge graph. Experimental results show that our approach significantly outperforms state-of-the-art GNN-based models, including Light-GCN and NGCF. Our research provides an intelligent and efficient perspective on supplier selection for the Chinese automobile industry.

Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks

In today's globalized environment, supply chain (SC) and manufacturing operations are intrinsically linked to satisfy consumer demand. Faced with rising preferences for personalized goods, convenience, and price competitiveness, many companies pivot towards e-commerce strategies and product family approaches to offer wider product varieties and shorten delivery times. This is evident in the fast-moving consumer goods (FMCG) industry, where multi-echelon networks are often utilized to optimize inventory and lower costs. While these methods can fulfill consumer expectations, decreased network resilience is a key obstacle, leaving supply networks vulnerable to supply and demand disruptions. Hence, a holistic approach is required to mitigate disruptions impacts in multi-echelon networks. As a digital enabler, digital twin (DT) technology can manage disruptions in SC networks and manufacturing shop floors. However, these solutions typically operate in silos without context considerations, resulting in illogical solutions. To overcome these challenges, this study proposes a novel supply and production (S&P) DT system to enhance resilience and disruption management in multi-echelon networks. A four-tier technology stack is introduced first, then resilience evaluation, SC replanning, and shop floor rescheduling methods are explored. Based on this, a DT-enabled disruption mitigation mechanism is proposed and featured in an F&B-oriented demand spike disruption case study. Results show the role of the hybrid S&P DT system in improving demand fulfillment rate and reducing production make span to enhance operational continuity.

Innovative supply chain network design with two-step authentication and environmentally-friendly blockchain technology

AbstractBlockchain Technology (BT) has the potential to revolutionize supply chain management by providing transparency, but it also poses significant environmental and security challenges. BT consumes energy and emits carbon gases, affecting its adoption in Supply Chains (SCs). The substantial energy demand of blockchain networks contributes to carbon emissions and sustainability risks. Moreover, for secure and reliable transactions, mutual authentication needs to be established to address security concerns raised by SC managers. This paper proposes a tri-objective optimization model for the simultaneous design of the SC-BT network, considering a two-step authentication process. The model considers transparency caused by BT members, emissions of BT, and costs related to BT and SC design. It also takes into account uncertainty conditions for participating BT members in the SC and the range of transparency, cost, and emission targets. To solve the model, a Branch and Efficiency (B&amp;E) algorithm equipped with BT-related criteria is developed. The algorithm is implemented in a three-level SC and produces cost-effective and environmentally friendly outcomes. However, the adoption of BT in the SC can be costly and harmful to the environment under uncertain conditions. It is worth mentioning that implementing the proposed algorithm from our article in a three-level SC case study can result in a significant cost reduction of over 16% and an emission reduction of over 13%. The iterative nature of this algorithm plays a vital role in achieving these positive outcomes.