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

As a manufacturing power in the era of Industry 4.0, the Chinese traditional manufacturing industry urgently needs innovation and upgrading. In this process, the pressure on the traditional manufacturing industry is mainly financial shortages. The government can act as a Booster to add power to the upgrading by providing financial support. We build a three-tier game model consisting of Raw Material Suppliers, a Traditional Manufacturer, and an Original Equipment Manufacturer to explore the upgrading conditions and construct three settings: Agency, Consignment, and Design. We find that the government subsidy can serve as an effective way to ease enterprises' financial burden and improve the innovation level, which is more evident when the traditional manufacturer innovates. In addition, when the market competition and the government subsidy are both at a moderate level, the combined effect of the two factors is positive, and the condition is the best to upgrade. Encouragingly, the social welfare under Design will be the highest when the government subsidy is slightly great. Finally, excessive subsidy hinders upgrading and aggravates market competition, even if the traditional manufacturer enjoys exorbitant profits. HIGHLIGHTS The upgrading on the manufacturing industry in the era of Industry 4.0 The right of product design in supply chain models The cross-influence of the government subsidy and market competition on industrial upgrading is introduced The conditions to help upgrade the manufacturing industry are given in the era of Industry 4.0 [ABSTRACT FROM AUTHOR]

In the servicization business model, a service-oriented manufacturer sells the service derived from its products. Because of the special compensation method, a service-oriented manufacturer is more likely to be underfunded. A Stackelberg game is formulated to investigate the interaction among a bank, a manufacturer, and an operator, in which two financing strategies are considered: bank financing and guaranteed (internal) financing. The difference between these two strategies is who bears the bankruptcy risk of the manufacturer. With bank financing, the bank bears the risk and determines the interest rate, while under guaranteed financing, the operator sets a guaranteed financing interest rate. We find that under bank financing, the low operating efficiency of the manufacturer increases the bank's share of the supply chain benefits. In particular, the service-oriented manufacturer can rarely be financed if the bankruptcy risk is high. However, under guaranteed financing, the operator may, interestingly, even subsidise a manufacturer with high operating efficiency. In this situation, a high bankruptcy risk could incentivize the manufacturer to improve its operating efficiency for the sake of the subsidy. Practically, guaranteed financing could coordinate the supply chain through interest rate adjustments or subsidies when facing different risk levels, which could improve both firms' benefits. [ABSTRACT FROM AUTHOR]

The surge in e-commerce has significantly altered consumer shopping habits, shifting towards online purchases. This change renders the traditional single-channel supply chain insufficient, often resulting in lost profits. Consequently, many businesses now incorporate an online channel alongside their physical stores. This shift has led to the adoption of dual-channel supply chains by manufacturers, aiming to attract a broader customer base and expand market share. Leasing options are becoming increasingly popular, offering a novel method for consumers to acquire products. However, studies on dual-channel supply chains that include leasing options are scarce and typically focus on single-period pricing. This paper explores a realistic, centralised dual-channel supply chain model that encompasses manufacturing, remanufacturing, refurbishing, distribution, and storage, along with selling, leasing, and repair services. A mathematical model is developed for a multi-period environment. This model simultaneously optimises manufacturing, remanufacturing, distribution strategies, storage costs, service offerings, and pricing for both sold and leased products, considering repair services for defective items. The paper presents numerous numerical analyses to demonstrate how integrating leasing services impacts the supply chain's profitability, the effect of customer preferences on pricing strategies, and how the cost of collecting used products influences remanufacturing decisions over a set time frame. [ABSTRACT FROM AUTHOR]

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. [ABSTRACT FROM AUTHOR]

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains. [ABSTRACT FROM AUTHOR]

Modern supply chains are complex, interconnected systems that contain emergent, invisible dependencies. Lack of visibility often hinders effective risk planning and results in delayed discovery of supply chain problems, with examples ranging from product contamination, unsustainable production practices, or exposure to suppliers clustered in geographical areas prone to natural or man-made disasters. Initiatives that rely on manual collection of data often fail due to supply chain complexity and unwillingness of suppliers to share data. In this paper, we propose a neurosymbolic machine learning technique to proactively uncover hidden risks in supply chains and discover new information. Our method uses a combination of graph neural networks and knowledge graph reasoning. Unlike existing research our model is able to infer multiple types of hidden relationship risks, presenting a step change in automated supply chain surveillance. The approach has been tested on two empirical datasets from the automotive and energy industries, illustrating that it can provide inference in multiple types of links such as companies, products, production capabilities, certifications; thereby facilitating complex queries that go beyond who-supplies-whom. As such, additional risk insights can emerge from graph structure, providing practitioners with new knowledge. [ABSTRACT FROM AUTHOR]

International trade, through global supply chains, is central to global sustainable production and consumption. Redistribution of environmental resources impacts through international trade has become apparent in ecological research – and may have long-term design and planning implications for production and supply chain management. However, current trade and production accounting methods are still incapable of providing accurate and reliable results for policymakers and organisational decision makers to manage global and supply chain trade issues. In this study, a blockchain-based conceptual framework for embodied environmental resources accounting in trade is proposed to address global supply chain and trade issues. Framework design, benefits, and challenges are also discussed. Future research and linkage to theory are also provided. This study provides a strong systemic foundation for managing environmental resources at the nexus of international trade, sustainable production, and global supply chains. [ABSTRACT FROM AUTHOR]

Visibility across supply chains has been a key concern for organisations for many years, but the tools and information systems to achieve real-time visibility have not been available until recently. In response to uncertainty and complexity, advanced information and communication technologies have been explored for supply chain visibility (SCV). However, managerial perspectives are largely absent from the current literature. In response, this paper systematically documents managerial factors influencing SCV and information that should be collected and shared among supply chain partners for better visibility. A multi-stage Delphi analysis was conducted with 26 supply chain experts from various globally recognised enterprises with manufacturing units located worldwide. The results provide details on prioritised managerial perspectives and experiences within (1) factors affecting SCV (drivers, enablers, challenges and contingencies), (2) SCV content (supplier, internal and customer information) and (3) implications of SCV (capabilities and performance effects). One observation was that forecasting is not deemed as important due to severe disruptions in supply chains. Real-time visibility for better predictability emerged as the top priority. This study is among the few that empirically explores factors influencing supply chain visibility and generates new insights into why barriers can be difficult to overcome in complex supply chain settings. [ABSTRACT FROM AUTHOR]

Cybersecurity presents non-negligible challenges for firm collaboration and supply chain viability, as information exchange among nodes introduces potential interdependent risks. How to make appropriate decisions on security investments and information exchange modes is a significant issue for supply chain members. Considering two information exchange modes: system interconnection and system independence, this study develops two game models to investigate the cybersecurity investments in a vertical supply chain composed of a retailer and n suppliers. Initial analysis shows that although firms learn investment decisions mutually in the face of a changing cybersecurity environment, suppliers always take a free ride on the efforts of retailer in two cases and the increased interdependent risks will damp nodes enthusiasm for security investments. Next, to compare the two cases, we introduce information exchange efficiency as the mediate parameter to link degree of system interconnection and proportion of information shared. We found that under the system independence mode, firms with high information-exchanging demand in the large-scale supply chain are more motivated to invest in cybersecurity. Furthermore, we extend our models to a centralised decision-making scenario. We find that security investment efficiency is greatly improved, and the free-riding behaviour of supplier is significantly reduced when systems are interconnected. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a complementary technology in supply chain research. However, the majority of AI approaches explored in this context afford little to no explainability, which is a significant barrier to a broader adoption of AI in supply chains. In recent years, the need for explainability has been a strong impetus for research in hybrid AI methodologies that combine neural architectures with logic-based reasoning, which are collectively referred to as Neurosymbolic AI. The aim of this paper is to provide a comprehensive overview of supply chain management literature that employs approaches within the neurosymbolic AI spectrum. To that end, a systematic review is conducted, followed by bibliometric, descriptive and thematic analyses on the identified studies. Our findings indicate that researchers have primarily focused on the limited subset of neurofuzzy approaches, while some supply chain applications, such as performance evaluation and sustainability, and sectors such as pharmaceutical and construction have received less attention. To help address these gaps, we propose five pillars of neurosymbolic AI research for supply chains and provide four use cases of applying unexplored neurosymbolic AI approaches to address typical problems in supply chain management, including a discussion of prerequisites for adopting such technologies. We envision that the findings and contributions of this survey will help encourage further research in neurosymbolic AI for supply chains and increase adoption of such technologies within supply chain practice. [ABSTRACT FROM AUTHOR]

Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives. [ABSTRACT FROM AUTHOR]

This paper investigates a single-vendor, multiple-buyer coordinated supply chain under stochastic demand. Each buyer implements a continuous review policy, and the shortage quantity is fully back-ordered. The lead time for the first shipment includes setup, production, and transportation times, while the lead time for the remaining shipments includes only transportation time. The production time is a function of production lot and production rate, which are both decision variables, and the setup and transportation times are controllable as well. For each buyer, the safety stock in the first replenishment cycle is not necessarily identical to that in the remaining cycles. Two successive shipments from the vendor to a buyer may have different sizes, and their ratio is a decision variable. The problem is finding the production and inventory replenishment policy, production rate, and lead times that minimise the long-run expected total cost per time unit. We demonstrate properties satisfied by the cost function and develop an optimisation algorithm, whose performance is compared to a benchmark algorithm based on a commercial solver in a numerical experiment. The experiments also investigate the benefits stemming from the proposed model when compared to models reproducing situations that leverage some or any of the controlled factors. [ABSTRACT FROM AUTHOR]

In this policy brief, Antonia Hmaidi, Senior Analyst in the Science, Technology and Innovation Program at MERICS, analyzes Huawei, a company that is emerging as the leader of China’s national team in semiconductors, dominating chip manufacturing and seeking to integrate the country’s entire supply chain. Its ambitions stem from both its placement on the U.S. Entity List and strong government support at the national and local level. Internationally, Huawei is coy about these ambitions, hiding its supply chain involvement and often operating under a different company’s name. Meanwhile, Huawei’s experience is also encouraging other Chinese technology companies to support China’s quest for chip self-sufficiency, developing new hardware and software for that purpose. The clandestine nature of Huawei’s involvement—it is not known who serves what role in semiconductor production—makes it more difficult for Western companies and governments to assess China’s progress in technology, vet potential partners and customers, and conduct risk assessments.

Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains. [ABSTRACT FROM AUTHOR]

Existing research on advertising structures in a supply chain has mainly been conducted with symmetric quality information and the interaction between quality information disclosure and advertising has not been clarified. To identify the optimal advertising structure and disclosure strategy for a manufacturer, we explore manufacturer advertising and cooperative advertising in the context of product quality information asymmetry. We examine the implications of the manufacturer's product quality information disclosure on his advertising strategies and the impact of advertising on quality information disclosure decisions. When cooperative advertising is more effective than manufacturer advertising and the product quality is low, the manufacturer should adopt manufacturer advertising, which leads to higher perceived quality and improves the retailer's economic condition. We find that advertising can inspire the manufacturer to disclose more product quality information regardless of the advertising structure, which occurs when the effectiveness of advertising is large. Furthermore, the manufacturer, the retailer, and consumers can benefit from cooperative advertising when cooperative advertising is more effective than manufacturer advertising and the product quality is high. We also consider an extension where the manufacturer and retailer advertise simultaneously and find that advertising leads to more quality information being disclosed when the disclosure cost is low. [ABSTRACT FROM AUTHOR]