Supply Chain Network Research Articles

Assessing resiliency in scale-free supply chain networks: a stress testing approach based on entropy measurements and value-at-risk analysis

Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a novel approach to assess the resiliency of scale-free SC networks based on entropy measurements. By considering various sources of disruptions, including demand fluctuations, capacity constraints, and transportation disruptions, we create a stress test that measures the network's resiliency. Our method categorises the SC into plants, ports, and customers and analyses the impact of disruptions on individual nodes within similar categories. We utilise real-world SC logistics data and map it into a scale-free network representation. By calculating the global vulnerability of the entire SC network and the localised impact of node vulnerability, we gain insights into how disruptions propagate and affect interconnected nodes. We use value-at-risk (VaR) and conditional-VaR (CVaR) to identify high-risk nodes, highlighting nodes at risk of extreme performance fluctuations or failures. The analytical results have led to developing six propositions, which serve as references for practitioners and help generalise the findings.

Carbon emissions and network spillover effects along the supply chain—Evidence from China

Complying with the multi-tier supply chain network relationship and carbon emissions data of China's listed companies, this study shows that focal firms can significantly motivate entities to align carbon neutrality targets along the supply chain, especially in inter-firm collaborative relationships.

Biomass supply chain network design under uncertainty, risk and resilience: A systematic literature review

Biomass supply chain faces numerous challenges, uncertainties and risks. Resilience holds the key for effectively addressing these dynamic challenges and uncertainties and risks inherent in biomass supply chain management. This paper aims to highlight the uncertainty, risk, and resilience, which are significant concepts of biomass supply chain network design, with a systematic literature review. An innovative classification scheme is developed to combine these concepts from an operational research perspective. First, the articles are investigated according to the sources of uncertainty, risk management, resilient network designs, and detailed subcategories. Then, the problem characteristics, modeling uncertainty, and solution methods are analyzed. This systematic literature review serves as a guide for further studies by analyzing uncertainty, risk, and resilience in the biomass supply chain network design, and identifies a list of potential issues for future research directions.

From Aspiration to Public Policy: Imprinting UNGP-Aligned Footprints of Corporate Responsibility and Accountability into the Shifting Sands of International Arbitration Practice

Abstract Endorsed more than a decade ago by the UN Human Rights Council, the UN Guiding Principles for Business and Human Rights (UNGPs) articulate the responsibilities of States and corporations for human rights. Since then, the issue of corporate responsibility for human rights, and also the environment, and how it ought to be recognized and enforced, has attracted increasing attention. In more recent years, States have promulgated a flurried storm of ESG regulations requiring disclosure of human rights or climate risks or mandating human rights due diligence across global supply chain networks, intensified their transition to greener economies, and continued to negotiate treaties infused with dreams of more sustainable investment and development. Against this backdrop, in October 2023, a UN Special Rapporteur presented a report that condemned the international arbitration system for prioritizing the protection of corporate property rights over the protection of international human rights and the environment. In tracing the development of a ‘soft law’ aspiration of corporate responsibility to inscriptions into ‘hard law’ international investment law treaty instruments and then considering how those inscriptions have been interpreted and applied by tribunals, this article finds that despite the nascent recognition of corporate responsibility for human rights and the environment, the enforcement of accountability for that responsibility in international arbitral practice is currently misaligned with the UNGPs and with other fundamental tenets of international law. This article subsequently explores the opportunity, and implications, for imprinting stronger footprints of responsibility and accountability in international arbitral practice, including through the potential recognition of new international public policy upholding corporate responsibility for human rights and the environment, and in doing so, highlights the need for greater cross-industry, multidisciplinary and multilateral dialogue on an issue of not only global significance, but with global impact.

Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy

Since renewable energy systems have a limited lifespan and cannot be used after a while, it seems imperative to pay attention to recycling and sustainability in the supply chain network design of these systems. Also, modern power systems have high complexity and a large number of problems that affect the reliability of electricity supply to consumers. Therefore, organizations should consider broader goals, including the maximum use of resources and the minimization of waste, which is known as lean economy. In this research, a multi-objective non-linear forward optimization model has been considered for the design of the supply chain network of photovoltaic and wind systems based on the aspects of stability and taking into account the reliability. Also, adding the reverse flow, in addition to the forward flow, is also considered, which, in addition to economic advantages, also pays attention to environmental and social issues. The objective function includes minimizing all costs and negative environmental effects as well as increasing reliability. In order to convert the multi-objective planning model into a single-objective one, the epsilon method of the enhanced constraint is used. Uncertainty has been considered for all variables (except binary variables) and parameters of demand, intensity of sun radiation and amount of wind speed. Then, the efficiency of the model has been investigated through its implementation on a case study and the results have been obtained. Eventually, the model's efficiency is illustrated by discussing a real-world case, through which prominent managerial results are acquired.

Designing a Multi-Objective Optimization Framework for Global Supply Chains Using Genetic Algorithm and NSGA II

Supply chain optimization is crucial for firms to boost efficiency and competitiveness amidst increasing complexity and risks in global markets. This study provides light into the optimization of a global supply chain network using genetic algorithms, concentrated on avoiding disruptions and decreasing total supply chain costs. The main objectives comprise decreasing total supply chain costs, managing disruptions in product quantity and quality, and optimizing operating parameters and features. The study employs genetic algorithms for optimization, explaining the approach and providing final interpretations to boost supply chain efficiency and resilience against disturbances.

Contagious Stock Price Crashes Along the Supply Chain

Although risk management is widely regarded as an important topic within supply chain (SC) management, little research studies the contagious effect of risk factors along SC networks. By using stock price crashes as an indicator of risk factors, our study reveals that stock price crash risk can be transmitted from major customers to suppliers with a delay of up to two weeks. We show that the information opacity of suppliers is a factor that potentially contributes to the delayed crash contagion. We also find that the contagion effect becomes more pronounced as the importance of customers increases. Moreover, customer operational incidents have a more pronounced contagion effect on suppliers compared to customer financial incidents. Additionally, we find that suppliers tend to take strategic measures following the stock price crashes of their major customers, including diversifying their customer base, enhancing operational efficiency, and improving product quality. However, among these actions, only the improvement of operational efficiency effectively mitigates the adverse impact of customer stock price crashes on suppliers. Overall, our findings provide new insight into the distribution of risk factors across SC networks, highlighting the critical role of operational improvements in bolstering the resilience of firms to SC risks.

Effectiveness of Value-Added Input-Output Method in Upstream and Midstream Supply Chain Network of Sunflower Agro-industry

Abstract Sunflower (Helianthus annuus L) is an annual shrub that grows up to three meters high. Sunflower agro-industry has high potential economic value. Sunflower seeds produce oil that can be extracted and used as a raw material in the food, pharmaceutical, bioenergy, and cosmetic industries. The problem is the low availability of superior local seeds, so it must be imported. The impact of imports is the low value-added. Therefore, this study aims to determine and measure the value-added of sunflowers in the upstream and midstream networks. The methods are Hayami and BPS input-output, also aims to identify the effectiveness of these two methods.. The results of value-added on sunflower seeds, cooking oil, herbal oil, and animal feed by the Hayami and the BPS input-output each are IDR6,000 (40%) and IDR24,005,528 (20%), IDR4,900 (26%) and IDR41,377,013 (23%), IDR21,000 (60%) and IDR117,653,333 (38%), IDR3,500 (86%) and IDR23,996,000 (100%). it is better to calculate using the BPS method because it is more detailed and clear in all aspects of the sunflower agroindustry, while using the hayami method only focuses on sales and labor. In the future, this research will be developed by designing sustainability improvement to reduce environmental impacts; performing value-added calculations for derivative products in the downstream supply chain network; also developing a decision support system.

Analysis Effect of Parameters of Genetic Algorithm on a Model for Optimization Design of Sustainable Supply Chain Network Under Disruption Risks

Abstract Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

Financial Institution's Role in Supply Chain Risk Management: Strengthening Resilience and Mitigating Financial Risks

One of the most important problems that multinational organizations are dealing with in risk-oriented supply chains is efficiency assessment and choosing the best low-risk financial institution for financial assistance. We provide an integrated approach for evaluating the unpredictable, dangerous, and fragile arrangement of the financial sector, which directly affects the supply chain network's processing stages. This model is supported by fuzzy-soft tools. The real economy is currently under pressure from both the domestic economic crisis and the decline in international commerce. President Xi Jinping has often suggested stepping up initiatives to assist the real economy's high-quality growth. In this regard, the development of the model for supply chain financing offers a fresh approach to resolving the funding issues faced by businesses. To tackle the financial issues facing the marketplace, we should use the chain of custody finance model, but we also need to thoroughly understand the risks associated with it and how to manage them. False trade orders and insufficient control over core businesses are difficulties that the present logistics finance risk management must deal with. The World Wide Web, big data, and block chain technologies are all blooming, opening new avenues for risk management in block chain finance. As a result, the goal of this essay is to examine the features of supply chain finance based on the technology of block chain, examine the risks associated with financing supply chains, and debate how to use block chain innovation for supply chain banking risk management.[4] Risk Financing: To assist organizations in transferring or reducing supply chain risks, financial institutions offer risk financing solutions including insurance & alternative risk transfer methods. The creation of novel risk financing products, evaluation of their performance, and comprehension of the effects on the resilience of supply chains can all be the subject of research. Financial institutions provide supply chain financing options like factoring, discounted invoices, and supply link finance programmed that help businesses manage their working capital, improve cash flow, and lower the risk of supplier failure to pay or insolvency. The effects of different financing choices on supplier relationships, supply chain stability, and overall risk reduction may be studied. Financial institutions collaborate with businesses to detect and classify different supply chain risks. An extensive investigation of the supply network environment, encompassing suppliers, transportation, logistics, and market dynamics, is required in this phase. Data gathering techniques used may include questionnaires, interviews, & site visits. In the above rank one is axis, and the last one is Punjab national bank. Financial Institution in Supply Chain Risk Management In the above rank one is axis, and the last one is Punjab national bank.

Designing a resilient-sustainable integrated broiler supply chain network using multiple sourcing and backup facility strategies dealing with uncertainties in a disruptive network: A real case of a chicken meat network

Increasing supply and demand uncertainty, coupled with unforeseen disruptions, pose challenges to the resilience of today's critical sectors in the global food industry, including the broiler supply chain. This study introduces a resilient model to enhance the sustainability and resilience of the broiler supply chain in the face of uncertainties and disruptions. The model integrates backup facilities and employs multiple sourcing strategies to reinforce resilience. Using mixed integer linear programming with bi-objective, multi-period, and multi-product features, the model aims to minimize carbon dioxide (CO2) emissions from transportation while maximizing overall supply chain profit. The goal programming, and the ε-constraint methods optimize decision-making and yield Pareto solutions, achieving a balanced approach to conflicting objectives. Also, robust optimization and stochastic programming provide practical solutions for handling uncertainties. Validation and sensitivity analysis confirm that the proposed model optimizes the broiler supply chain, enhancing resilience, sustainability, and profitability.

Beyond traditional supply chain management: Addressing sociopolitical challenges in increasingly turbulent global trade landscape

AbstractAn increasing number of sociopolitical issues, including international wars, armed conflicts, decoupling of economic powers, deglobalization, nationalism, protectionism, sanctions, tariff wars, political tensions, and other sociopolitical forces affecting global supply chains (GSCs) and supply chain management (SCM) strategies. Amidst these sociopolitical factors, the global trade landscape is undergoing a seismic shift. This paper examines the complex web of these sociopolitical aspects, their impact on SCM strategies, and their implications for GSCs. The systematic literature review approach is used for this research, which entails the following steps: selecting and assessing pertinent sources, summarizing and synthesizing the literature, examining challenges and opportunities, articulating conclusions, and pinpointing prospective directions for future research. The findings of this study reveal that these sociopolitical factors have significant impacts on multiple facets of SCM, such as sourcing strategies, supplier relationships, risk management, and overall supply chain resilience. In addition, the study presents a roadmap for businesses to effectively navigate these difficulties, which involves diversifying supply chain networks, integrating corporate sustainability strategies, and implementing environmental, social, and governance factors into business operations. The originality of this research lies in its integrated and holistic approach to the integration of sociopolitical factors into SCM theory and practice.

Reputation-Based Evidential Reasoning Approach Improves Cooperative Evolution of Supply Chain Network Under Disruption Risk

Reputation-Based Evidential Reasoning Approach Improves Cooperative Evolution of Supply Chain Network Under Disruption Risk

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

다계층 공급사슬 네트워크에서 영향력의 차이를 고려한 공급사슬 조정 방안에 관한 연구

다계층 공급사슬 네트워크에서 영향력의 차이를 고려한 공급사슬 조정 방안에 관한 연구

Towards building clean hydrogen supply chain network in Iran for future domestic demand and exports. Part I: Prioritization of scenarios

The first part of the current study highlights the importance of developing a clean hydrogen supply chain (CHSC) in Iran to enhance energy security and contribute to global efforts towards sustainable energy transition. Depending on whether the focus is on domestic consumption or export of hydrogen, the study provides valuable insights into the prioritization of CHSC development pathways via multi-criteria decision-making tools (AHP, AHOP-TOPSIS, and AHP-VIKOR). The results show that for domestic hydrogen consumption (internal scenario), the best path of the supply chain includes blue hydrogen from steam methane reforming coupled with carbon capture and storage, H2-carriers (ammonia and methanol), natural gas grid for transportation and distribution, and industry sector as the end user. In the external scenario, however, the best path of the CHSC consists of green hydrogen production based on solar energy, and the other parts of the path include liquid hydrogen as the storage method, ship as the distribution and transportation, and industrial sector as the end user. By identifying the most suitable methods and technologies for each stage of the supply chain, this paper offers a comprehensive analysis that can guide decision-making in CHSC development.

Supply chain network design based on Big Data Analytics: heuristic-simulation method in a pharmaceutical case study

Digital technology is a pivotal aspect of the ongoing digital transformation upon which supply chain networks (SCNs) heavily rely. Through the utilization of Big Data Analytics (BDA), organizations can gain insights into sales, production and formulate decision support systems. The pharmaceutical supply chain emerges as a crucial player in producing and distributing medications vital for treating COVID-19 patients. In this study, we have innovatively designed a multi-echelon Pharmaceutical Supply Chain Network (PSCN) using BDA during the COVID-19 pandemic. We have developed a novel Mixed-Integer Non-Linear Programming model addressing allocation, production planning, and inventory control decisions, known as the LAPI problem. A transformation method is employed to convert the non-linear model into a linear one to enhance computational efficiency. The primary objective of our mathematical model is to minimize the total cost and CO2 emissions of the transportation system and established centres while maximizing the shelf life of medicines. Our experiments reveal that medium- and large-scale problems, characterized by the three main attributes of BDA – variety, velocity and volume (3 V‟s), pose challenges for optimal solutions. We present a case study of Iran to validate the mathematical model for large-scale problems. Employing a simulation approach, we estimate the demand for required pharmaceuticals, underscoring our proposed model’s real-world applicability and effectiveness. This research contributes to the growing necessity of eco-friendly and efficient supply chain strategies, especially in the critical domain of pharmaceuticals during pandemic situations.

Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount

The advanced information technology has enabled supply chain to make centralized optimal decision, allowing to make a global optimal solution. However, dealing with uncertainty is important in inventory management. Besides demand and supply uncertainties, supplier discounts also often arise unexpectedly. Further, suppliers or third-parties typically offer various delivery options in which trade-off occurs between cost and lead time. Thus, this study introduces new problem namely Multi-Echelon Inventory Optimization with Delivery Options and Uncertain Discount (MEIO-DO-UD). As a solution, Neuroevolution Reinforcement Learning (NERL) framework is developed for minimizing total system cost. The environment is modeled via System Dynamics (SD) and actor is presented by integration of Artificial Neural Network and Evolutionary Algorithm (EA), creating an effective decision-making model under dynamic uncertainty. The experimental study has been conducted where two different supply chain networks are given namely serial and divergence. Three EA algorithms are compared namely Differential Evolution (DE), Memetic Algorithm (MA), and Evolution Strategy (ES). Furthermore, NERL is also compared with the EA-optimized classical continuous review model namely (s,Q). The result shows that regardless what EA type is used, the proposed NERL always outperforms EA-optimized (s,Q) model. The more complex the problem, the further improvement can be made i.e. cost reduction up to 58%, followed by the fill rate improvement. The result also shows that NERL can avoid overfitting. Managerial implications are highlighted where NERL provides the more stable inventory level among all supply chain partners and bull-whip effect can be damped.

Modelling medical oxygen supply chain network under demand uncertainty using stochastic programming

Modelling medical oxygen supply chain network under demand uncertainty using stochastic programming

Revenue sharing contract in competitive supply chain network coordination: location based on VMI policy and bullwhip effect

Online shopping advances have made the timely delivery of goods to the place of the buyer a necessity. This study considers a significant and rival supplier with a retailer network, and we use Stackelberg game to solve the problem. The major supplier (the leader) is connected to retailers and covers their orders through a Vendor Management Inventory policy and revenue-sharing contract. The major supplier pays fines in case of any shortage, and minimises the loss of retailers in the network through good warehouse locations. The rival supplier (the follower) signs contract with some retailers encouraging them to receive their orders to achieve higher utility. Hence, the expected utility function of both suppliers is calculated, and their strategies are measured at the equilibrium point in a zero-one model. Furthermore, a better view of major decisions by using bullwhip effect (BWE) analysis in the competition between main and rival suppliers is done. Results indicate a lower BWE when the actual order of the retailer equals the retailer's order from the supplier's perspective, and the demand variance ratio is less than the contracts’ profit variance ratio. Moreover, it is possible to control the demands variance ratio by controlling the ratio of contracts’ profit.