Complex Supply Chain Network Research Articles

Optimal Risk Control of Complex Supply Chain Networks under Public Emergencies

Public emergency is an event that occurs suddenly, has a wide range of influence, and brings great impact to the society. It has the characteristics of suddenness, publicity, urgency and uncertainty. Frequent public emergencies pose a great threat to supply chain security, causing serious economic losses and social impacts. In order to improve the ability of supply chain network to cope with public emergencies, it is of great significance to study the law of supply chain risk transmission and risk control measures. Through the review of the relevant theories of supply chain sudden risk and infectious disease model, the existing problems and research prospects are pointed out. Then, based on the SEIR model, a complex supply chain network risk control model under government intervention is constructed to explore the transmission mechanism of supply chain risk and the optimal control rate of the government. Finally, the simulation verifies that by controlling the basic reproduction number, the government can effectively curb the further spread and diffusion of risks, and achieve the goal of controlling risks and balancing cost control.

Risk Control in Complex Supply Chain Networks Under Sudden Public Emergencies

After the occurrence of sudden public emergencies, risks propagate throughout the entire supply chain network, triggering a domino effect. Therefore, determining appropriate strategies to intervene in and control risks, thereby blocking or mitigating their spread, becomes critically important. This study constructs a risk control model for complex supply chain networks under sudden public emergencies based on the SEIR system dynamics propagation model, incorporating risk immunization strategies and government participation in risk control. The proposed model adopts a target control approach to manage risk dissemination, and the effectiveness of this approach is verified through simulation experiments. The findings reveal that target control can effectively contain risk propagation, and enhancing the risk immunity of supply chain networks during sudden public emergencies significantly reduces the extent of risk diffusion while lowering the cost of government intervention.

The role of power in market control in supplier-buyer relations

This study investigates the role of power dynamics in market control within supplier-buyer relationships, with a specific focus on blockchain technology adoption in supply chain networks. Using five focus group discussions, comprising of diverse participants categorized by sector, location, gender, and company size, the research explores how power influences decision-making processes. Findings reveal that larger companies often prioritize stability over innovation, limiting their willingness to adopt disruptive technologies like blockchain. In contrast, smaller firms demonstrate greater flexibility and openness to change. Beyond company size, factors such as product uniqueness, strategic network positions, and the personalities of decision-makers play critical roles in shaping blockchain adoption. Participants also reported persistent pressure from both suppliers and buyers to accept unfavorable terms, underscoring power imbalances that constrain smaller players. The analysis is framed within the conceptual structure of a model with a focus on the nuanced interplay of power and decision-making in the context of supply networks. Qualitative analysis of the discussions, conducted with Atlas.ti software, highlights the influence of sectoral differences, geographical variations, and gender dynamics in supply chain negotiations. This study provides valuable insights into the barriers and enablers of blockchain adoption within complex supply chain networks, emphasizing the critical interplay of power and communication in shaping market control.

Managing financial investments in supply chain networks: The roles of network power and control

The financialization of firms refers to the phenomenon of non-financial firms increasingly engaging in financial activities rather than traditional production and operational investments. In today's competitive and complex supply chain networks (SCNs), firms do not operate in isolation but heavily rely on their partners. This interdependence may shape their investment behaviours. However, the mechanism of how the network structure affects the financialization of firms in SCNs is yet unclear. Therefore, this study draws on the resource dependence theory to investigate the role of network structure—encompassing network power and control—in shaping corporate financialization in SCNs. We construct SCNs for the focal firms in a sample of 2307 Chinese listed enterprises from 2009 to 2021 and find significantly negative relationships between corporate financialization and both network power and control. This may be because network power and control diminish enterprises' precautionary saving and speculative motivations through financing and information channels. Moreover, their effect on corporate financialization was found to be more pronounced for non-state-owned enterprises (non-SOEs) and firms with low market share, high customer concentration, and distant supply chain geographic proximity. Furthermore, our results indicate that enterprises exhibiting greater network power and control in SCNs tend to reduce their financial investments, which benefits their innovation and total factor productivity. By exploring the factors that influence corporate financialization through a network lens, this study offers valuable insight into the issues regarding financialization, thus promoting the sustainable development of firms and the development of countries' real economies.

Optimizing supply chain networks using mixed integer linear programming (MILP)

Mixed Integer Linear Programming (MILP) has emerged as a powerful tool for optimizing complex supply chain networks. This paper explores the theoretical foundations of MILP, including the integration of integer variables and advanced solution techniques such as branch-and-bound and branch-and-cut algorithms. Through detailed modeling of production planning, network design, and transportation logistics, MILP enables companies to achieve significant cost reductions and operational efficiencies. We present case studies from retail, manufacturing, and pharmaceutical sectors to illustrate the practical applications of MILP. These examples demonstrate how MILP optimization can lead to reductions in production and inventory costs, improved customer satisfaction, and enhanced service levels. The findings underscore the value of MILP in addressing the multifaceted challenges of modern supply chain management.

Capturing key interdependences among supply chain disruptions and mitigation strategies to enhance firm performance

PurposeThis paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.Design/methodology/approachThis study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).FindingsThe results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.Originality/valueOur results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

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.

WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems

Supply Chain (SC) Optimization is a key activity in today’s industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors.

Hybrid Tabu-Grey wolf optimizer algorithm for enhancing fresh cold-chain logistics distribution.

The increasing public demand for fresh products has catalyzed the requirement for cold chain logistics distribution systems. However, challenges such as temperature control and delivery delays have led a significant product loss and increased costs. To improve the current situation, a novel approach to optimize cold chain logistics distribution for fresh products will be presented in the paper, utilizing a hybrid Tabu-Grey wolf optimizer (TGWO) algorithm. The proposed hybrid approach combines Tabu search (TS) and Grey wolf optimizer (GWO), employing TS for exploration and GWO for exploitation, aiming to minimize distribution costs in total and establish efficient vehicle scheduling schemes considering various constraints. The effectiveness of the TGWO algorithm is demonstrated through experiments and case studies compared to other heuristic algorithms. Comparative analysis against traditional optimization methods, including Particle swarm optimization (PSO), Whale optimization algorithm (WOA), and original GWO, highlights its superior efficiency and solution quality. This study contributes theories by demonstrating the efficacy of hybrid optimization techniques in complex supply chain networks and dynamic market environments. The practical implication lies in the implementation of TGWO to bolster distribution efficiency, cost reduction, and product quality maintenance throughout the logistics process, offering valuable insights for operational and strategic improvements by decision-makers. However, the study has limitations in generalizability and assumptions, suggesting future research areas including exploring new search operators, applying additional parameters, and using the algorithm in diverse real-life scenarios to improve its effectiveness and applicability.

Drop Trailer Forecasting in Volatile Networks

This paper delves into the critical aspect of demand forecasting within the broader context of optimizing drop trailer management in volatile networks, with a specific focus on a large pallet manufacturer’s supply chain operation. The study underscores the importance of accurate demand forecasting as a foundational element for informing subsequent optimization models. The main objective is to enhance our sponsor company’s supply chain efficiency by accurately predicting future trailer requirements, which is crucial for the subsequent development of an effective inventory control and optimization model. This research utilizes forecasting methods like Gradient Boosted Trees and highlights the challenges of traditional forecasting methods in the context of our sponsor company’s complex and dynamic supply chain network. The demand forecast is meant to inform the development of optimization models crucial for ensuring the effective allocation and management of trailer assets, ultimately leading to cost reductions and improved service levels within our sponsor company’s network. The study contributes significantly to supply chain management literature by showcasing the application of sophisticated forecasting techniques in a real-world context and setting the stage for the development of robust optimization models in the domain of drop trailer management.

A goal programming model for the stability analysis of a maritime inventory routing replanning problem

This paper aims to provide insight into the stability of solutions to a multi-product maritime inventory routing problem (MIRP) when replanning due to the occurrence of unexpected events. The MIRP determines vessel routes while ensuring that port inventory levels remain within operational thresholds. In today's global economy, supply chains are often vast and complex. Managing inventory and routes for multiple products across international waters is a daunting task. The MIRP provides a framework for optimizing these complex supply chain networks. Four stability metrics are available to evaluate the stability of the adjusted plans. The problem is modeled using a goal programming approach to consider all stability metrics simultaneously. Then, 30 instances from the literature are solved using CPLEX. It is shown that there are significant differences when all stability metrics, as opposed to a single one, are considered simultaneously, which can lead to better agreement between the vendor and the retailer in decision making. A cost analysis is performed to examine the impact of the cost increase on the stability of the other metrics. The key finding is that when all stability metrics are considered simultaneously, a 5 % increase in cost can result in 7.5 % fewer changes to replanned solutions, while this rate is less than half when the stability metrics are considered individually. This highlights the significance of evaluating all metrics simultaneously instead of analyzing each metric separately.

Blockchain Technology Enabled Traceability Framework: Food Supply Chain Perspective

In the global market, consumers have expectations of good quality food products. To provide flawless food products, traceability has become dominant in a complex food supply chain (SC) network. Stakeholders of the food SC hesitate to share their data with each other, as they have different business strategies. Blockchain Technology (BCT) brings all the parties of food SC on single platform to improve interorganizational trust. The implementation of BCT in food SC offers the traceability of products. This paper proposes a framework for traceability of products in food SC using BCT that offers the consumers to trace the origin of the food products. Blockchain enhances the food traceability, minimize the data tampering, and improves data transparency in food SC. BCT enabled food SC framework has been tested using case study of rice SC and found the enhancement in traceability.

A framework for assessing corporate sustainability risks along global supply chains: an application in the mobile phone industry

In today’s globalized world, characterized by complex supply chain networks, integrating sustainability supply chain risks into the corporate assessment models seems crucial. To tackle the global challenges this paper presents a Sustainability Risk Assessment Framework that allows measurement of environmental and social risks upstream and downstream, along global supply chains, using a combination of quantitative methods (environmental and social footprint computing tools) and qualitative methods (hotspots analysis) from three dimensions (by scenarios, by product components, by lifecycle stage). To test this framework this study focuses on the mobile phone industry, due to the importance of its environmental and social impacts. The results show the low impact of recycling strategies in environmental terms and social terms on the mobile phone industry. Fostering re-use strategies by companies will substantially improve their impacts. This framework overcomes some of the weaknesses of the corporate sustainability risk assessment methodologies, combining quantitative methods and qualitative methods.

Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks.

The impact of COVID-19 is global, and uncertain information will affect product quality and worker efficiency in the complex supply chain network, thus bringing risks. Aiming at individual heterogeneity, a partial mapping double-layer hypernetwork model is constructed to study the supply chain risk diffusion under uncertain information. Here, we explore the risk diffusion dynamics, drawing on epidemiology, and establish an SPIR (Susceptible-Potential-Infected-Recovered) model to simulate the risk diffusion process. The node represents the enterprise, and hyperedge represents the cooperation among enterprises. The microscopic Markov chain approach (MMCA) is used to prove the theory. Network dynamic evolution includes two removal strategies: (i) removing aging nodes; (ii) removing key nodes. Using Matlab to simulate the model, we found that it is more conducive to market stability to eliminate outdated enterprises than to control key enterprises during risk diffusion. The risk diffusion scale is related to interlayer mapping. Increasing the upper layer mapping rate to strengthen the efforts of official media to issue authoritative information will reduce the infected enterprise number. Reducing the lower layer mapping rate will reduce the misled enterprise number, thereby weakening the efficiency of risk infection. The model is helpful for understanding the risk diffusion characteristics and the importance of online information, and it has guiding significance for supply chain management.

Impacts of Product Variety and Supply Chain Networks on the Influx of Information Exchange in Industry Applications

Managing product variety is a challenging problem given the increasing complexity of supply chain networks. To overcome this complexity, managing integration in the supply chain is essential for companies to coordinate effectively. By managing the influx of information exchange between the various entities involved in the supply chain network, integration can be achieved successfully. In this paper, we are targeting research questions regarding the impact of the influx of information exchange on product variety and supply chain networks and the key factors influencing its exchange from different industries’ perspectives. To investigate our research questions and to conduct a case study across different industries and companies, this study aims to explore the impact of supply chain network complexity, which causes an influx of information exchange due to increasing product variety through qualitative research. In our results, by categorizing the raw interview data, we visualize correspondent opinions to facilitate deep analysis, including factors such as product variety, supply chain networks, and information exchange. The key factors that can influence the influx of information exchange from different industries’ and companies’ perspectives are presented in our results to provide valuable insights into the significant factors affecting the success of the smart business.

3D Baskı Teknolojisinin Çevre ve Ekonomi Bağlamında Kitlesel Özelleştirme ve Tedarik Zinciri Üzerindeki Yıkıcı Doğası

Today's production paradigm is shaped by mass production, where production processes and tools are standardized, expertise is not taken much into account, and the same products are produced either in batches or massively in small varieties. Production is carried out in factories in certain centers, and the products produced are often distributed all over the world. There is a need for a multi-agent supply chain network and supply chain management with the stages of delivering the products to the final consumer, starting from the pre-production stage. It has become clear that the need for a complex supply chain network by producing products from a single center is no longer economical and environmentally friendly. In recent years, consumers have started to think about the harmful effects of the products they buy. Changing customer needs and demands have also made companies consider the mass customization production model. Recently, three-dimensional printing technologies, also defined as additive manufacturing technology, have emerged as a new production technology. With this disruptive technology, production models and supply chain networks are subject to change and transformation. In this study, the environmental and economic benefits of 3D printing technology are analyzed in the context of mass customization production model and supply chains.

Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring.

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy.

Research on Risk Propagation in Complex Supply Chain Networks Based on SEIR Model

The complexity of supply chain network structure makes the risk propagation among the enterprises in the supply chain more and more complicated. This paper constructs a SEIR risk propagation model for complex supply chain networks based on the analytical idea of contagion model, which reveals the risk propagation mechanism of supply chain networks from a macro perspective. The article verifies the existence of risk propagation threshold and the stability of different equilibrium points by analyzing the basic regeneration number, equilibrium point and stability of the model; explores the effectiveness of different parameters on the basic regeneration number by combining sensitivity analysis; and verifies them by using MATLAB simulation software. The results show that the basic regeneration number is a key factor affecting the risk propagation, and the risk diffusion in this system can be effectively curbed by controlling the basic regeneration number.

Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiments (RFE) and Intelligent Attack Experiments (IAE). Specifically, this study investigates the effect of RFE and IAE across multiple tiers and scales. The global supply chain data was mined and analysed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analysed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimisation tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilised in the study can be applied to the study of supply chain reachability and viability in other industries.

Energy‐efficient and sustainable supply chain in the manufacturing industry

AbstractThis study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy‐efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed‐integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy‐efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small‐size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy‐efficient solutions for the considered supply chain network.