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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. [ABSTRACT FROM AUTHOR]

The design of a supply chain network (SCN) should address two conflicting objectives, i.e. inexpensive yet reliable operation. This paper presents models to choose supply facilities and determine distribution options while considering the expected risk of not serving customers attributed to supply shortages. The goal is to design a reliable network with a lower cost under traditional cost objectives. We formulate a stochastic mixed-integer biobjective programming model considering the protection of facilities and solve this problem with a progressive hedging algorithm (PHA). We examine the tradeoff curve between the cost and risk through a case study involving Shanghai in China. The results indicate that substantial improvements in reliability can be reached with a limited increase in the total cost. [ABSTRACT FROM AUTHOR]

Biomass to biofuel conversion is carried out using complex technologies, which are often capacity restricted. Although these restrictions are used to satisfy the design limitations of the processing units, they often lead to the selection of suboptimal choices for technologies to fulfil the demand. This article proposes a distributed multi-unit mixed integer linear programming model for a biorefinery supply chain capable of utilizing multiple technologies for each biomass. A single-unit superstructure from the literature is improved to accommodate decisions related to multiple technological choices for different biomass and extended to a multi-unit model by allowing the installation of more than one process unit of the selected technology. This study incorporates the investment and operational costs for processing facilities to demonstrate the long-term cost analysis of the supply chain. The efficacy of the proposed model is demonstrated in a distributed biorefinery case study involving industrial units known as secondary processing facilities, which fulfil their biofuel demand using onsite production and external processing facilities. The objective is to obtain an optimal production plan by minimizing the total cost while satisfying the demands of all users and other resource constraints. The optimal solution of the multi-unit model has reduced the total cost by 1.31%, compared to the single-unit model. A rigorous solution analysis of the single and multi-unit models is performed to validate the proposed model. [ABSTRACT FROM AUTHOR]

Supply chain disruptions pose significant economic stability and growth challenges, impacting industries globally. This study aims to systematically review the literature on the use of simulation tools in managing supply chain disruptions, focusing on the historical evolution, prevalent simulation methods, specific challenges addressed, and research gaps. A systematic literature review was conducted using the PRISMA method. An initial pool of 236 articles was identified, from which 213 publications were rigorously reviewed. This study analyzed these articles to map the academic landscape, identify key clusters, and explore the integration of digital advancements in enhancing supply chain resilience. The review identified the chronological development of research in this field, highlighting significant contributions and influential authors. It was found that various simulation methods, including discrete-event simulation, agent-based modeling, and system dynamics, are employed to address different aspects of supply chain disruptions. Two primary research frontiers emerged from the analysis: the strategic reconfiguration of supply chain networks to mitigate ripple effects and the swift implementation of countermeasures to contain disruptions. The findings suggest a need for future research focusing on dynamic analysis and control theory applications to understand and manage supply chain disruptions better. This study also notes the increasing interest and need to use digital technologies (digital twins, artificial intelligence, etc.) in future research. It underscores the necessity for continued research to develop resilient and sustainable supply chain infrastructures aligned with the United Nations' Sustainable Development Goals. The identified research gaps offer a roadmap for future scholarly exploration and practical implementation. [ABSTRACT FROM AUTHOR]

Due to the expansion of privacy protection, and data sharing gets complicated and is known to be a hot issue in the research areas. Many business scenarios in the supply chain network have been highly important in research areas. Especially, blockchain technology ensures the essential solution in the supply chain network for securing information sharing. Yet, it is difficult to maintain security at every blockchain level, whereas it is suggested to use "public–private key cryptography." The fundamental goal of this research work is to present new privacy preservation of data using hybrid meta-heuristic algorithms with blockchain technology. The secured supply chain network is built with blockchain technology. The privacy preservation of data sharing in blockchain technology is handled by the electric fish-Harris hawks optimization (EF-HHO). This new algorithm is utilized for the key generation phase of two processes like "data sanitization and restoration." The optimal key generation follows a multi-objective problem is solved by the variables like "Hiding Failure (HF) rate, information preservation (IP) rate, and false rule (FR) generation, and degree of modification (DM)". The result analysis shows the offered model provides effective performance when compared with existing conventional techniques. [ABSTRACT FROM AUTHOR]

With the increasing requirements for environmental protection, authorities have enacted different carbon policies to promote sustainable practices. This study considers the competition among different manufacturers and multiple demand markets in the supply chain network. We examine and compare their equilibrium decision conditions under three common carbon policies: carbon cap, carbon cap‐and‐trade, and carbon tax. Results suggest that under lower carbon caps, manufacturers greatly reduce their carbon emissions by reducing production quantities, which causes a decrease in profits. However, an appropriate carbon cap size can not only motivate manufacturers to reduce carbon emissions but also improve their profits. Under a certain price for carbon credit, the effectiveness of a carbon cap‐and‐trade policy is similar to that of a carbon cap policy, but it has more flexibility. Carbon tax is effective, but it brings more tax burden to manufacturers. We provide implications for companies to optimize their production methods to achieve sustainability. [ABSTRACT FROM AUTHOR]

This study takes a supply chain network composed of manufacturers, retailers and demand markets as the research object and analyses the impact of the government's carbon trading price (CTP), carbon tax (CT) and low-carbon product subsidy (LPS) policies on the supply chain network equilibrium. We determine the equilibrium state of members by using variational inequality and then construct a network equilibrium model that considers these three policies. A modified projection algorithm is used to verify and solve the model. The results show that a higher CTP helps to reduce carbon emissions but reduces the transaction quantity of products. Further, when the CTP increases, the system's profits increase only under the LPS policy. However, regardless of the CTP level implemented, if the CT rate is higher than the subsidy level, carbon emissions are lower. Notably, under the same levels of LPS and CT, LPS has a greater effect on the system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Numerous studies on corporate social responsibility (CSR) indicate that firms adopt CSR practices for various reasons related to their supply chain. However, the necessity to conform to a firm's own industry CSR norm is overlooked. Conforming to one's industry CSR norm—a herding behavior known as CSR conformity—ensures firm in‐group legitimacy and preserves internal resources for core business activities. On the other hand, deviating from industry norms sets a firm apart from its peers, making the firm more appealing to supply chain partners. Motivated by this dilemma, this study draws on middle‐status conformity theory and explores how a firm's network prominence determines its CSR conformity. Panel data analyses of 1650 firm‐year observations reveal an inverse U‐shaped relationship between firm network prominence and its CSR conformity, indicating that firms with a mid‐level network prominence engage in higher CSR conformity. However, the inverse U is flattened when a firm's supply chain partners (and their respective industries) share similar CSR standards, suggesting that a firm can only prioritize its own industry CSR norms if its supply chain partners share a compatible CSR standard. These findings highlight the importance of understanding CSR from an organizational conformity perspective, especially in the context of supply chain network. HIGHLIGHTS: Firms use industry CSR norms as a benchmark for CSR engagement–a firm herding behavior known as CSR conformity.Firms conform to industry CSR norms to ensure legitimacy and preserve resources; however, they also need to deviate to differentiate themselves and attract supply chain partners.This research uncovers an inverse U‐shaped relationship between firm network prominence and CSR conformity. Additionally, a focal firm can prioritize its industry CSR norms only when its supply chain partners share a compatible CSR standard. [ABSTRACT FROM AUTHOR]

The management of Agri-food supply chains is a complex task, given the unique product characteristics, perishability, uncertain demand, and specific storage requirements. This research introduces an innovative approach to optimizing product allocation among producers, brokers, wholesalers, and retailers, focusing on minimizing transportation costs and network delivery time through multi-objective programming. To address uncertainties, supply and demand constraints are modelled using a gamma distribution, and the maximum likelihood estimation method determines their parameters with specified probabilities. The study conducts a case analysis to showcase the model's practical effectiveness, and a numerical comparison with alternative approaches is included. The primary goal of this study is to enhance the efficiency of agri-food supply chain management practices, providing valuable insights for practitioners in the field, with a focus on cost reduction and improved delivery time. [ABSTRACT FROM AUTHOR]

Supply chain learning (SCL), which is reflected in organizational learning, referring to the learning between organizations in the supply chain, carries the promise to enable sustainable competitive advantages. Many large multinational companies, such as IKEA, Nestle, and Microsoft, have therefore integrated supply chain knowledge management and continuous learning into their corporate strategies. While there is evidence in extant research about a positive correlation between both the subjective attitude and learning ability of supply chain members and their performance improvement, areas where insight is still missing pertain to the relationship between supply chain members' subjective psychological factors, and their relationship network structures. This is a serious omission, since these dimensions likely play a key role in the dynamics underlying SCL. In order to alleviate this void, we consider a multi-tier SCL network and develop a model in which a supply chain member's attraction is weighted based on its previous learning experience. The game mechanism underlying SCL captured in this experience-weighted attraction (EWA) model is then tested using a simulation study of IKEA China's multi-tier supply chain network for its sustainable cotton initiative. The results suggest that learning costs can be reduced and learning spillover befits can be increased by the provision of rewards to network member companies and better communication. In addition, the perception of and preference for SCL by suppliers can be influenced by initiating sustainable advocacy and providing knowledge and technology training, as well as fostering a range of subjective factors we investigate in our study, such as the strategic attractiveness the decline ratio due to forgetting, the attractiveness improvement ratio due to preferences, and the response sensitivity to strategies. The findings offer insight into the influence mechanisms of the supply chain network structure and subjective attitude about SCL, which are especially applicable to large, multinational enterprises. [ABSTRACT FROM AUTHOR]

In the context of the economic globalization, there is an increased disruption risk in the supply chain network due to the outsourcing, complexity and uncertainty. At the same time, the disruption may propagate across the entire supply chain network because of the interdependence. With the resource constraints, appropriate recovery strategies which can minimize the impact of disruption propagation and effectively improve the supply chain network resilience have attracted a great deal of attention. In this paper, we first construct the disruption propagation model considering the recovery strategy based on the characteristics of the competitiveness, time delay and underload cascading failure in the supply chain network. This model uses the memetic algorithm to determine the set of recovery nodes among all disruption nodes, which can minimize the impact of disruption propagation. And then, the simulation analysis is conducted on the synthetic network and the real-world supply chain network. We compare the proposed recovery strategy with other strategies (according to the genetic algorithm, according to the descending order of the load of failure node, according to the ascending order of the load of failure node, according to the descending order of the node degree, according to the ascending order of the node degree) and provide decision-making reference against supply chain disruptions. [ABSTRACT FROM AUTHOR]

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to investigate the R&D investment (RDI) of manufacturing firms from the perspective of supply chain network, especially the effect of firms' supply chain network structures (network power and network cohesion) on its RDI, and further to explore the contingency conditions of this effect within the context of Chinese manufacturing supply chains. Design/methodology/approach: The authors collect a large sample of Chinese manufacturing firms over the period 2014–2019 and construct a large-scale supply chain network, and finally obtain 2,390 firms from 20,483 observations. Ordinary least squares regression was adopted to analyse how supply chain network structures affect RDI in manufacturing firms. Findings: It is surprising that firm's supply chain network structures have a negative effect on RDI. In addition, knowledge and technology intensity (KTI) positively moderate the relationship between network cohesion and RDI. Originality/value: This study contributes to the innovation stream from the perspectives of supply chain network, and provides the empirical findings that the negative role of a firm's supply chain network structure on its RDI for the first time. The rationale for these negative effects is straightforward according to the social capital theory that manufacturing firms with a high level of social capital that are possibly to accept established patterns of thinking and behaviour, causing them to decrease the enthusiasm of RDI. [ABSTRACT FROM AUTHOR]

Supply chain financial fraud, characterized by extensive false fund circulation and fictitious business events, causes substantial financial losses and undermines the efficiency of supply chain operations. To address this challenge, we introduce an innovative research framework that utilizes knowledge graphs and spatial-temporal neural networks for effective fraud detection. Our approach involves constructing a supplier-customer knowledge graph from data of Chinese listed companies, capturing the complex supply-demand relationships within the supply chain. We designed a spatial-temporal Graph Neural Network (GNN) that models both node attributes and the time-evolving graph topology. By incorporating temporal and spatial dual attention mechanisms, our model adeptly identifies local topology and temporal changes in the knowledge graph. Empirical evaluations demonstrate that our Dual Attention Spatial-Temporal Graph Neural Network (DAST-GNN) outperforms existing methods, achieving an AUC of 93.64%, which is 10.41% higher than the leading machine learning methods. Furthermore, analyzing supplier-customer relationships across different historical periods enhances fraud detection, highlighting the robustness of our approach. This research offers a potent tool for regulators, investors, and researchers, advancing the security and efficiency of supply chain operations.

With the continuous expansion of the global market and the change of consumer preferences, the market demand presents a trend of complexity and diversity, which requires the supply chain network to have a high degree of flexibility and adaptability to quickly adjust the allocation of resources, optimize the production process, shorten the delivery cycle, and continue to innovate to meet the ever-upgrading needs of consumers. By simulating natural selection and genetic mechanism, genetic algorithm can search effectively in the solution space and improve the solution efficiency. At the same time, combined with the variable neighborhood search strategy, we can change the search neighborhood in the iterative process of genetic algorithm to avoid falling into the local optimal solution, and further improve the quality of the solution. Therefore, this paper proposes a multi-objective supply chain network planning based on proximity optimization and hybrid genetic algorithm (GA) variable neighborhood search strategy. In order to verify the effectiveness of the proposed strategy, the study conducts multiple sets of arithmetic tests. The results revealed that the gap between the maximum and minimum values of the optimal solutions of the studied algorithms can be controlled within 1.5% in the medium-scale examples. In the large-scale example, the optimal solution of the research algorithm was controlled within 0.41%. In addition, the study also tested the optimization effect of the proposed model on different dimensions. The results revealed that in the economic cost dimension and social impact dimension, the model achieves the highest optimization effect at example 11, while the performance is relatively weak at example 2. In the environmental pollution dimension, the research model improved more than 0.5% on average over the triple bottom line optimization model. In summary, the strategy offers a new and useful tool for supply chain network planning and performs well in terms of both solution efficiency and solution quality.

In proposing an integrated production–distribution model for multinational corporations (MNC), the current research undertakes a novel, simultaneous consideration of facility location, capacity acquisition/disposal, labor, manufacturing, purchasing, exchange rates, tariffs, and container and/or individual shipping factors. Unlike prior studies that examined some such factors. Based on real-world data from a window manufacturing MNC, a mixed integer linear programming model is developed and solved using CPLEX Solver. It reveals how the different features affect the base solution. Specifically, increased demand pushes more manufacturing to US and UK facilities rather than (low-cost) Chinese facilities; it helps meets some of China's demand and leads to reduced total tariff costs. Decreased demand instead favors the Chinese facility, and the increase in tariff costs can be offset by savings in manufacturing and transportation costs. Changes to the factors have insignificant effects on the production and distribution mix, with little to no impact on total profits, because these costs are lower than capacity costs. The comprehensive proposed model provides firms with a framework for obtaining meaningful insights into how they can maintain their operations in sustainable, profitable ways. This research also should assist policymakers in making informed decisions regarding regulations on production and distribution strategies. [ABSTRACT FROM AUTHOR]

Almost half of the European railways are served by diesel-powered trains used for both cargo and passenger transport. Similar considerations apply to the bus sector in urban and suburban areas. The decarbonization of the transport sector can be achieved by using hydrogen as a fuel for trains and buses. However, the main barrier to adopting hydrogen fuel cell trains and buses is the construction of adequate infrastructure. Thus, to foster a sustainable energy transition, existing renewable plants should be considered when designing new hydrogen supply chains. In the proposed Sicilian case study, existing wind farms are chosen as electricity production plants. Most of them have been shut down due to grid unbalancing issues; still, the curtailed renewable electricity could be used to produce zero-carbon hydrogen via water electrolysis, significantly reducing costs. To account for this opportunity, this study models the hydrogen production, transportation, and refueling stages, employing a mixed-integer linear programming approach to find the optimal location and capacities of hydrogen infrastructure, while minimizing the total daily cost of the supply chain. The model is applied to Sicily and different scenarios for varying hydrogen demands for trains and buses are analyzed and discussed. Results show that capital expenses cover more than 90% of the total cost of the supply chain, with a hydrogen cost dispensed at the filling station ranging from 6.32 to 9.02 €/kgH 2. The environmental analysis shows that as hydrogen demand rises, so do the carbon emissions from the distribution stage. However, the impact of using wind-generated electricity in terms of avoided emissions is notably greater. Finally, it is shown that transporting hydrogen in gaseous form using tube trailers is economically more attractive than transportation in liquid form with tanker trucks in the early stages of the network. • Hydrogen production via grid-wind-electrolysis coupled systems is discussed. • Sicily has an untapped potential for hydrogen production from wind. • A MILP optimization model minimizes the daily total cost of a hydrogen supply chain network. • Hydrogen demands from trains and buses are used as input parameters for the optimization model. • Optimized hydrogen cost dispensed at the filling station ranges from 6.32 to 9.02 €/kgH 2. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the relationship between supply chain network structures and firm financial performance and the moderating role of international relations. In this study, which is grounded in social capital theory and applies the perspective of systemic risk, the authors theorize the effects of supply chain network structures on firm performance. Design/methodology/approach: The authors extracted data from two Chinese databases and constructed a supply chain network of the firms concerned based on nearly 4,300 supply chain relations between 2009 and 2018. The authors adopted the fixed effects model to investigate the relationship between supply chain network structures and firm financial performance. Findings: The econometrics results indicate that network structures, including the degree, centrality, clustering coefficients and structural holes, are significantly related to firm financial performance. A significant and negative relationship exists between international relations and firm financial performance. The authors also find that international relations strongly weaken the relationship between supply chain network structures and firm financial performance. Originality/value: This study, which collects secondary data from developing countries (e.g. China) and explores the impacts of supply chain network structures on firm stock performance, contributes to the existing literature and provides practical implications. [ABSTRACT FROM AUTHOR]

Goal: This study aims to minimize the total cost of a supply chain network and determine the optimal product flow under demand uncertainty. Design / Methodology / Approach: A mathematical model is presented to minimize the total supply chain cost by identifying the optimal facility locations and product flows. The applicability of the proposed model is evaluated through a real-life case study of a multinational sporting goods retailer with sensitivity analysis. Moreover, Monte Carlo simulation is used to capture the demand uncertainty and test the robustness of the model. Results: The minimized cost is achieved with optimal facility locations and product flows. The optimal result shows a 3% reduction in the total cost, making it the most robust solution under demand uncertainty. Limitations of the investigation: The proposed model is only applicable to a single-commodity supply chain network. In addition, the cost components of the network are limited to facility costs and transportation costs, disregarding the other cost components. Practical implications: This research demonstrates a methodology that can be used as a decision support system by managers to make strategic and tactical decisions in a supply chain network when demand is uncertain. Originality / Value: The MILP and simulation techniques used together to construct a three-tiered supply chain under uncertainty receive little attention in the literature. In addition to developing a novel threeechelon MILP model, this research makes use of a real-world case study to illustrate the methodology's performance in the context of demand uncertainty through simulation. [ABSTRACT FROM AUTHOR]

To understand how supply uncertainty affects the structure of supply chain networks we consider a setting where retailers and suppliers must establish a costly relationship with each other prior too engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide which suppliers to link to based on their wholesale prices. Subsequently, retailers compete with each other in Cournot fashion to sell the acquired supply to consumers. We find that in equilibrium retailers concentrate their links among too few suppliers, that is, there is insufficient diversification of the supply base. We find that either reduction in supply variance or increase in mean supply, increases a supplier's profit. However, these two ways of improving service have qualitatively different effects on welfare: improvement of the expected yield by a supplier makes everyone better off, whereas change in the variance of the yield impacts system members differently. [ABSTRACT FROM AUTHOR]

Distribution network for e-commerce is an important supply chain (SC) networks required for effective management of warehousing. Due to the recent disruption in the SC, minimal lead times for product delivery days can no longer be guaranteed, thereby causing an increase in distribution and delivery of goods. This paper investigated some human factors (HFs) to understand the root cause of the ongoing disruption in the SC system using statistical and exploratory data analysis. The findings revealed that HFs has a huge significance in a distribution system. To our best knowledge, these findings are seldom reported in literature of e-commerce systems. [ABSTRACT FROM AUTHOR]

The technology of Blockchain has the characteristics of decentralization, non-tampering and traceability, and it is applied to the traceability of green food, it helps to solve the quality and safety problems of Information asymmetry and untrustworthy green food. However, with the development of the network and complexity of the green food supply chain, more and more traceability information is needed. In this paper, the blockchain side chain is introduced to build a credible platform for the traceability of green food with multi-chain on the main chain. The main chain is enlarged by the side chain to improve its storage capacity, and the Bloom filter modified Bmerkle tree is used to improve the efficiency of traceability queries. Finally, the running process of traceability platform is analyzed by taking specific green food as an example. The construction of trusted main-side multi-chain traceability platform can eliminate the Information asymmetry in complex supply chain network, ensure the authenticity of traceability information, and enhance consumer confidence in green food. At the same time, it can expand the storage capacity of single chain, improve the efficiency of traceability query and protect the privacy of private data. And it is hoped to provide reference for the application of block chain technology in traceability system.

Goal: This study aims to minimize the total cost of a supply chain network and determine the optimal product flow under demand uncertainty. Design / Methodology / Approach: A mathematical model is presented to minimize the total supply chain cost by identifying the optimal facility locations and product flows. The applicability of the proposed model is evaluated through a real-life case study of a multinational sporting goods retailer with sensitivity analysis. Moreover, Monte Carlo simulation is used to capture the demand uncertainty and test the robustness of the model. Results: The minimized cost is achieved with optimal facility locations and product flows. The optimal result shows a 3% reduction in the total cost, making it the most robust solution under demand uncertainty. Limitations of the investigation: The proposed model is only applicable to a single-commodity supply chain network. In addition, the cost components of the network are limited to facility costs and transportation costs, disregarding the other cost components. Practical implications: This research demonstrates a methodology that can be used as a decision support system by managers to make strategic and tactical decisions in a supply chain network when demand is uncertain. Originality / Value: The MILP and simulation techniques used together to construct a three-tiered supply chain under uncertainty receive little attention in the literature. In addition to developing a novel three-echelon MILP model, this research makes use of a real-world case study to illustrate the methodology's performance in the context of demand uncertainty through simulation.

Under the background of economic globalization, green supply chain management has become a key strategic management tool for enterprises to improve their competitiveness as well as sustainable development. This paper proposes the objective function and constraints in supply chain network optimization based on the policies related to the dual-carbon target and the requirements in the cross-border industrial cooperation model and uses the robust model to solve and realize the establishment of the supply chain network optimization model. The simulation test found that the solving efficiency of this model and the control of carbon emissions are better than the traditional genetic algorithm. Meanwhile, after the optimization of this model, the supplier delivery rate of cross-border enterprise Y can reach 91.28%, which is far beyond the target expectation, and the carbon emission of the enterprise is far below the carbon emission limit, so the green cross-border supply chain network can be realized. The model in this paper can improve the weak link in the green management decision of cross-border supply chain network, strengthen the application of a robust model in the design and optimization of cross-border supply chain network, and provide auxiliary decision-making support for the construction of cross-border supply chain network scientifically and effectively.

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Copyright of Journal of Industrial Engineering Research in Production Systems is the property of Bu-Ali Sina University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, integrated supply chain management has become a serious concern for companies due to the severe competitive market since it is a factor playing a major role in both increasing companies' profitability and creating customer satisfaction. Meanwhile, interest in utilizing information technology in supply chain management and moving towards a digital supply chain to reach a better rank in the industry has rapidly increased. Additionally, the increase in population and the growth of cities leads to the creation of heavy traffic loads bringing difficult challenges to the company's distribution network such as a sharp rise in transportation costs and untimely delivery. On the other hand, having fixed warehouses in the urban area not only is unprofitable for companies but also impossible due to both the increase in costs and the lack of enough space to develop in densely populated and crowded areas. In this paper, a flexible distribution network is designed using the small mobile warehouse's strategy to address the problems raised. Trucks and pickups are considered small mobile warehouses and distribution units in the distribution area, respectively. Wireless sensors are used as an information technology unit in each vehicle to monitor the network condition and make a flexible communication in which all sensors are connected to the processing center located in the organization's building. Consequently, the processing center identifies the optimal points in the distribution area for reloading between small mobile warehouses (trucks) and distribution units (pickups), while minimizing the total cost of network transportation. The applicability of the proposed model is investigated using a case study and sensitivity analyses. [ABSTRACT FROM AUTHOR]

Due to the rapid development of information technology, supply chain network is evolving, which involves a higher level of interdependence between organisations. Conventional production capability evaluation relies on centralised approaches with limited sharing of performance and evaluation data. Besides, traditional evaluation methods are mainly based on subjective manual operation using limited data. In this paper, we propose a production capability evaluation system by incorporating Internet of Things (IoT), machine learning and blockchain technology for supply chain network. It contributes to achieving real-time data collection and automated enterprise production capability evaluation mechanism. Besides, blockchain technology is adopted to enable open and decentralised data storage and sharing, provide fair and automatic trading of data. The proposed system is evaluated through a simulation experiment. It demonstrated how to utilise the proposed system to choose suitable upstream enterprises. The successful development of the system could help to enhance production efficiency, reduce risk and provide a reasonable and more sustainable production management in supply chain network. [ABSTRACT FROM AUTHOR]

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.