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As businesses undergo digital transformation, supply chain fraud poses an increasing threat, necessitating more sophisticated detection and prevention methods. This paper explores the application of machine learning (ML) and artificial intelligence (AI) in detecting and preventing supply chain fraud. The research design involves analyzing a dataset of supply chain operations and employing various ML algorithms to detect consumer-based fraud within the supply chain, which occurs when consumers partake in deceptive practices during the order process of e-commerce transactions. We analyzed 180,000 transactions from an international company recorded between 2015 and 2018. This study emphasises the necessity of human oversight in interpreting the results generated by these technologies. The implications of supply chain fraud on financial stability, legal standing, and reputation are discussed, along with the potential for ML technology to identify irregularities indicative of fraud. Descriptive findings highlight the prevalence of fraudulent transactions in specific payment types. The AI sequential and the CatBoost classifiers were the top-performing algorithms across all performance metrics. The top features to detect unusual orders are delivery status, payment type, and late delivery risks. The discussion emphasises the promising predictive capabilities of the ML and AI models and their implications for detecting supply chain fraud. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of supply chain (SC) risk mitigation and recovery strategies during long-term disruptions caused by the COVID-19 outbreak, which affect both suppliers and plants. Consequently, concurrent disruptions in supply and production are observed, which vary in duration and result in time-varying reductions in supply and production capacity. To cope with long-term disruptions, a modified multi-portfolio approach that integrates simulation and predictions is proposed to develop efficient mitigation and recovery plans. This approach involves selecting primary and recovery supply and production portfolios concurrently. To achieve this objective, time-dependent mixed integer programming (MIP) models that incorporate preparedness and recovery measures are developed to optimise SC operations. A prediction-based decomposition optimisation method is proposed to solve MIP problems and coordinate supply and production portfolios under disruptions and uncertainties. Furthermore, a heuristic approach is established to provide a comprehensive solution process. Finally, computational experiments and comparative analysis are conducted on a real-life case study. The results demonstrate that the proposed modelling and optimisation methods can effectively address disruptions and improve SC resilience. In addition, the developed models and approaches have the potential to serve as decision-making tools in SC management during disruptions. [ABSTRACT FROM AUTHOR]

Food products' quality information is advertised on labels but do customers trust them? This study investigates how the consumers' Willingness-To-Pay (WTP) for food products can be increased by deploying managerial effort and advanced technologies, such as the Blockchain Technology (BT). Our model explains how revealing verified information about product quality throughout the supply chain will generate optimal consumers' WTP and maximise profit. At each echelon of a multi-echelon supply chain, a buyer holds a Bayesian belief about the quality of the input to be procured. This belief is shaped by the accuracy and veracity of the information about this quality. Managerial effort is required both to enhance quality as well as ensure full and verified information. We show why this effort must be made across the chain and how opportunistic behaviour may be circumscribed. Using empirically grounded analytics and real prices of olive oil intermediate produce from various official bodies, we show how the application of BT may be financially justified. This research shows how trust and WTP can be further enhanced through the use of BT and additional smart technologies in a supply chain, which may be projected on other supply chains of organic and sustainable food products. [ABSTRACT FROM AUTHOR]

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

Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as having transformative powers to enable resilient supply chains (SC). Despite such a benefit, and the increase in popularity of AI and analytics in general, research is largely fragmented into streams based on different types of AI technologies across several SC contexts and through varying disciplinary perspectives. In response, we curate and synthesise this fragmented body of knowledge by conducting a systematic literature review of AI research in supply chains that have been published in 3* and 4* Chartered Association of Business Schools (CABS) ranked journals between 2000 and 2023. The search strategy retrieved 5, 293 studies, of which 76 were identified as primary papers relevant to this study. The study contributes to the accumulative building of knowledge by extending theoretical discourse about the specificities of AI for prescriptive analytics to enable SC resilience. This study proposes a strategic AI resilience framework to support SC decision-makers enhance the use and value of prescriptive analytics as an enabler to developing resilient SC. We make the call to action for an orchestrated effort within and between academic disciplines and organisations that are guided by a research agenda to guide future research initiatives. [ABSTRACT FROM AUTHOR]

Supply chain resilience is on the agenda of academia and industry like never before. One strong instigator for this phenomenon has been the COVID-19 pandemic, which opened the era of global uncertainties and vulnerabilities. In this paper, we analyse the transformation of supply chain resilience research through the COVID-19 pandemic. Methodologically, we use a hybrid approach based on a combination of elements of a bibliometric and expert analysis to compare the main topics of resilience research before, during, and after the pandemic. Along with an expected observation about an exponential growth of literature on supply chain resilience in and after 2020, we observe a major shift from preparedness and disruption predictions in the pre-pandemic literature towards recovery and proactive adaptation in the pandemic and post-pandemic research. Our analysis systematically reveals some new topics, management practices, and future research areas in supply chain resilience. In particular, digital technology, supply chain viability, the cross-industry ripple effect, and intertwined networks have become new and impactful research areas during the COVID-19 pandemic. Further developments of these topics are expected to be continued in future. Managerial and theoretical implications of the said developments conclude this paper. [ABSTRACT FROM AUTHOR]

The supply chain is an important source of knowledge that enables potential emerging technologies and, in this ecosystem, logistics are regarded as an intermediary for disseminating innovative solutions in a coordinated manner. As an emerging solution in the supply chain area, blockchain has recently been discussed as a disruptive technology and has received growing attention from academics and practitioners. Despite this interest, insufficient knowledge on the potential benefits and risks of blockchain technology causes vagueness for its successful implementation. Therefore, since effective management of logistics and supply chain operations through advanced solutions is of utmost importance, adopting an innovative approach rather than providing anectodal evidences or narrative expressions plays a critical role in blockchain adoption. Accordingly, this research aims to an in-depth analysis of the blockchain in logistics and supply chain by investigating enablers, barriers, and risks of this adoption. To this end, through employing both the Methodi Ordinatio and the narrative network analyses, the future direction of blockchain adoption is presented in light of the presented current state of knowledge. Consequently, the obtained findings offer academic and practical insights into the ambiguous, insufficiently explained, and conflicted areas in the relationship between blockchain technologies and logistics and supply chain contexts. [ABSTRACT FROM AUTHOR]

This paper investigates a multi-period multi-product stochastic inventory problem in which a cash-constrained online retailer can leverage order-based loans provided by some Chinese e-commerce platforms to speed up its cash recovery for deferred revenue. Sample average approximation (SAA) and moment-matching scenario tree are adopted to solve this multi-stage problem with the objective of maximising the retailer's expected profit over the planning horizon. We conduct numerical tests based on crawling real data from an online store. The results show that the solutions of the two stochastic modelling approaches are very close. Moreover, the retailer tends to use order-based loans when its initial available cash is limited or it is facing a long revenue delaying length. Practitioners can make use of the modelling techniques in this paper to estimate whether it is beneficial to apply for an order-based loan. [ABSTRACT FROM AUTHOR]

Industry 4.0 digital technologies are becoming indispensable for firms striving to enhance their supply chain capabilities and financial performance, but how these relationships play out in practice remains unclear. To address this issue, this study assesses the relationship between supply chain integration, supply chain agility, and financial performance from a dynamic capability perspective. Further analyses are conducted to establish whether Industry 4.0 digital technologies moderate the association between (a) supply chain integration and supply chain agility and (b) supply chain agility and financial performance. Findings based on the data pertaining to a sample of 274 Swedish manufacturing firms indicate that supply chain agility fully mediates the link between supply chain integration and financial performance. However, while Industry 4.0 digital technologies strengthen the effect of supply chain agility on financial performance, they do not moderate the relationship between supply chain integration and supply chain agility. These findings contribute to the ongoing debate regarding how digital technologies play a role in achieving competitive advantage in interplay with dynamic capabilities related to the supply chain. These findings are relevant for decision-makers, as they address the need for organisational adjustments beyond the mere introduction of Industry 4.0 technologies to fully reap their benefits. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) as a crucial approach plays a key role in improving commitment, trust, financial flows, and profitability in a supply chain (SC). Many industrial organisations finance their SC through two resources: internal financing (buyer) and external financing (bank). The main objective of this paper is to develop an advanced data envelopment analysis (DEA) model for measuring the sustainability of financing resources of Industry 4.0 technologies. To do so, for the first time a non-radial DEA model in the presence of both zero inputs and ratio data is proposed. In this paper, the sustainability factors, including economic, environmental, and social factors are incorporated into the proposed approach. The developed DEA model, for the first time, is applied in SCF. The results show the most sustainable financial resource for investing in Industry 4.0 technologies. Also, the inputs and outputs' inefficiencies are determined. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) is a new method of inventory control designed to address the challenges of today's complex and volatile supply chain environment. DDMRP replaces traditional Material Requirements Planning (MRP) with a more agile and responsive approach that is based on a set of colour-coded buffers. DDMRP ensures that the right inventory is in the right place at the right time, enabling companies to respond quickly and effectively to changing customer demand. DDMRP requires the setting of various parameters, such as variability factor (VF) and lead time factor (LTF), which can have a significant impact on systems' performance. Pre-assumed fixed values of DDMRP parameters are used in most of the existing studies. This may lead to either high stockout levels or excess inventory especially in environments involving high level of variability. In this study, we proposed a fuzzy logic-based approach which dynamically adjusts the values of VF and LTF parameters considering demand and lead time variability. The effectiveness of the proposed approach was investigated by comparing its performance with DDMRP based on several numerical experiments. The results showed that the proposed Fuzzy Demand-Driven Material Requirements Planning (FDDMRP) outperforms DDMRP in terms of backorder rate and total cost. [ABSTRACT FROM AUTHOR]

In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side. [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]

This paper proposes a service-oriented manufacturing strategy in a two-echelon supply chain with random demand. When there is no inventory for the retailer to meet demand, customers can choose to order products through the retailer and products will be delivered to them by the manufacturer directly in the promised time, and we call this delivery service in service-oriented manufacturing. We employ a Stackelberg game-theoretic model to analyse the procurement quantity and delivery time decisions by the manufacturer as the leader and the retailer as the follower. Using the performance of the pure-manufacturing strategy as a benchmark, our main results follow. First, the equilibrium strategies of the two players are obtained. Clear decision rules are also given. Second, delivery service in service-oriented manufacturing can be an effective way to reduce the holding cost of the retailer. Third, we describe the customer loss caused by waiting and find how the manufacturer influences procurement quantity of the retailer through the decision of delivery time. Finally, delivery service in service-oriented manufacturing cannot always improve the profit of the manufacturer and the retailer. We give guidance for managers to provide it. [ABSTRACT FROM AUTHOR]

To overcome product homogenisation issues, an increasing number of supply chain members have been binding their competitive advantages to services which extend supply chain competition to a new dimension. This paper proposes a product service supply chain (PSSC) network competition model with multiple competing manufacturers and retailers offering demand-enhancing and costly services. In order to obtain the decision-making results in a non-cooperative game context, equilibrium conditions are established using variational inequality, and the qualitative properties are discussed. We compare the two competition scenarios, price competition only and simultaneous price and service competition, to discover the impact of service-offering on supply chain members. After introducing the service competition, both manufacturers and retailers realise higher profits and marginal profits with the same service costs. In contrast, the manufacturers' marginal profits achieve higher growth rates. We also find that (a) the increase of the demand sensitivities to the service levels of retailers would contribute more to the revenue growth of the whole PSSC network and (b) when service costs are different for manufacturers and retailers, the optimal decision-making on service levels are not only affected by the service costs of the members in the same tier but also correlated with those in other tiers. [ABSTRACT FROM AUTHOR]

This paper deals with a problem of tactical planning of production activities. It considers the case of the world's largest phosphate production site, with several mines, several washing plants, and a drying plant, with transport of merchantable phosphate ores (MOs) by rail or pipeline. The tightening of product quality requirements by customers prevents from continuing to plan its production with the current rules. Currently, the plants are managed quite independently, source ores (SOs) are pre-assigned to the MOs for blending, and the MO production routings in the washing plants are predetermined. We propose a model that ensures flexibility in the production of MOs through the choice of dynamic blends and routings. Our approach goes beyond any pre-allocation of resources and aims at synchronising the flows of the washing plants. This specific problem of blending production and planning has not been addressed in the literature. Still, it is widely encountered in many mining industries and offers perspectives for the application of the approach proposed. Numerous tests show that this model ensures the quality constraints related to the composition of several chemical components, with a generalised use of the most economical routings while ensuring horizontal and vertical alignment of decisions. [ABSTRACT FROM AUTHOR]

The booming development of customised e-commerce makes e-commerce supply chains experience a severe test of consumer service level (CSL). An efficient e-commerce supply chain resilience optimisation method addressing economic performance and service performance is proposed to improve customised services under disruption risks. The proposed method includes a hybrid strategy considering both a resistance strategy with blockchain (BCT) adoption and time-dependent recovery strategies simultaneously. A two-stage multi-period multi-product stochastic programming with BCT adoption is then proposed, which (1) implements BCT as the resistance strategy in the pre-disruption stage; (2) collaborates four recovery strategies in the post-disruption stage; (3) supports the e-tailer in making decisions and optimises his profit and order fulfillment time while taking product priorities into account. Using the actual data of Chinese e-commerce during the public health emergency in 2020, it is demonstrated that (1) the applicability of the model with BCT adoption in both supply chain resilience and CSL improvement; (2) the performance of the hybrid strategy in long-term disruption management; (3) the efficiency and robustness of the proposed solution approach for multi-objective high-dimensional stochastic programming problems. E-tailers can optimise decision-making under long-term disruptions by the proposed methodology in practices. [ABSTRACT FROM AUTHOR]

This study examines the relationship between advertising and financing decisions in an online supply chain consisting of an e-platform and a capital-constrained retailer. The e-platform is the leader in a Stackelberg game, and the interaction is modelled in two advertising scenarios. The results indicate that advertising can reduce risk and is always beneficial, with an e-platform willing to support the retailer only when the former decides on the advertising level. When providing both advertising and financing services, the e-platform gains the ability to directly regulate the retailer's ordering decisions, thereby increasing its strategic flexibility. [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 study investigated the effect of a supplier's customer concentration level on its operating performance during the COVID-19 pandemic. Using a large sample of publicly listed U.S. manufacturing firms, we measured abnormal changes in operating performance of the sample firms across two distinct stages of the pandemic: the disruption phase ( $ N = 2,557 $ N = 2 , 557) and the recovery phase ( $ N = 2,454 $ N = 2 , 454). Our regression results show that the effect of customer concentration varied depending on the phase of the pandemic. Suppliers with a concentrated customer base performed worse during the first year of the pandemic when the global supply chain experienced significant disruptions. During this disruption, firms with lower degrees of customer concentration managed their supply chain risks more effectively, resulting in higher operating performance than their benchmark firms. However, when firms entered the recovery phase, we found that a firm's concentration level had the opposite effect on its operating performance. During the recovery phase, firms with concentrated customer bases could coordinate and collaborate more effectively with major customers, leading to improved operating performance. Based on our findings, we discuss the theoretical and managerial implications of managing the supply chain structure during its disruption and recovery. [ABSTRACT FROM AUTHOR]

This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers. [ABSTRACT FROM AUTHOR]

This study incorporates fairness concern in a low-carbon supply chain coordination mechanism where a single manufacturer sells its product to consumers through a single retailer. We develop four different scenarios of the Stackelberg master-slave game utility model—both members are neutral (NN), the manufacturer (FN) or retailer (NF) has fairness concern, and both are not neutral (FF), where the Nash bargaining fairness reference is leveraged to capture the impact of fairness preference on low-carbon supply chain optimisation decision-making profits, level of carbon emission reduction, warranty period, and revenue-sharing. Finally, numerical studies are conducted to quantify the impact of the Nash bargaining fairness concern. Research shows that: (1) fairness concern made it worse for the retailer but beneficial for the manufacturer and the system. (2) fairness concern causes a reduction in the level of carbon emission reduction and warranty period. However, the reduction of carbon emission reduction trading price and a certain range of revenue sharing effectively reduces the impact of fairness concern on members. (3) The revenue-sharing contract effectively reduces the negative impacts of fairness concern on supply chain members. The paper is a guide for enterprises development and cooperation but also provides empirical evidence for the government. [ABSTRACT FROM AUTHOR]

Consumers' attention to sustainable production and consumption is promoting innovation in sustainable supply chain operations. We build an extended sustainable supply chain with remanufacturing (and/or) product sharing, and consider three operation modes: Sustainable supply chain operation with remanufacturing but No product Sharing mode (SNS), Sustainable supply chain operation with product sharing but No Remanufacturing mode (SNR), and Sustainable supply chain operation with both product Sharing and Remanufacturing mode (SSR). How does sustainable consumption (i.e. product sharing) affect sustainable production decisions and how to choose the optimal operation mode? Our findings: The SSR mode positively reinforces the sustainability-oriented market segmentation and can be globally optimal with an integrated value-added effect of 'remanufacturing + product sharing'. From the perspective of total benefit optimisation, when the commission rate of a sharing platform and the proportion of service consumers are higher (lower), the transformation from the SNS mode to the SSR mode (from the SNR mode to the SSR mode) can be the best operational strategy. Maximising total benefits may have a compression effect on product sharing, which can be effectively adjusted by improving consumers' perceived value of remanufactured products. This study provides guidance for selecting combination strategies for sustainable supply chains in the context of carbon emission reduction. [ABSTRACT FROM AUTHOR]

Governmental subsidy policy and channel information structure have substantial effects on supply chain emission reduction, operations and marketing decisions. In this paper, we use game theory to study the interplay of government subsidizing arrangement and information asymmetry on manufacturers' emission reduction and retailers' low-carbon promotion. Suppose realistically that the investment coefficients for manufacturers' emission reduction levels and retailer' low-carbon promotion levels are their respective private information. Among diverse governmental subsidy schemes, we find dual subsidizing are the best choice for the defined level of total subsidy under information asymmetry, while it is not always the best choice if the total subsidy level is uncertain. Under information asymmetry, the results show that manufacturer have no incentive to lie under any subsidy policy. However, the retailer has incentive to misrepresent individual information and its misreporting behaviour is related to the coefficient and allocation ratio of governmental subsidies. On this basis, we illustrate that a screening model can be deployed to reduce the negative effects of information asymmetry on supply chain operations. We therefore propose the method of improving the performance of low-carbon supply chains under various situations of information asymmetry and governmental subsidizing schemes to achieve system-wide Pareto improvement. [ABSTRACT FROM AUTHOR]

We study how U.S. manufacturing firms' investment responds to tariff reductions in supplier industries. Our estimates, based on tariff reductions following multinational trade agreements, suggest that a hypothetical 10% reduction of all upstream tariffs would increase downstream investment by 4% to 6%. This estimate is not explained by decreasing uncertainty and stems from tariff reductions for homogeneous and low-R&D inputs, consistent with the investment response resulting from cost reductions rather than superior foreign technology embodied in imported inputs. Evidence from an instrumental variable estimation using the sudden increase in Chinese import penetration suggests that import competition also increases downstream investment. [ABSTRACT FROM AUTHOR]

The customer order decoupling point (CODP) is the point in the supply chain where the product is linked to a customer order and is, by definition, the last stock point along the supply chain. The CODP decouples the upstream operations that are made to stock from the downstream operations that are triggered by customer orders. We employed a systematic literature review of the empirical research on the CODP in operations and supply chain management (OSCM). We identified 40 articles that explicitly contributed with empirical results concerning factors and characteristics for the operations upstream versus downstream of the CODP, or between make-to-stock (MTS) and make-to-order (MTO) operations. The 32 factors that were identified were grouped into four main categories: market and product factors, operations factors, supply chain factors, and performance measures. Based on the content analysis, we propose a framework that provides a holistic view on the CODP. The distinctive differences between MTS and MTO characteristics strongly suggest that there are two configurations: one for the upstream MTS-type operations and another for the downstream MTO-type operations. We discuss implications for research and practice and provide suggestions for further research. [ABSTRACT FROM AUTHOR]

Mishra and Raghunathan (2004) and Kim (2008) conducted a study on the same model but reached conflicting results on under what circumstances vendor-managed inventory (VMI) benefits the retailer as opposed to retailer-managed inventory (RMI) in a setting where two products are partially substitutable. However, we note that their conflicting results were due to different assumptions about unmet demand after product substitution, which is influenced by consumers response to retail stockouts. This paper then extends their studies by incorporating consumer response to retail stockouts and re-examining the impacts of VMI on the retailer's performance as well as on each manufacturer's. Therefore, this paper reconciles the conflicts in the existing scholarly work and provides a more complete understanding of when each supply chain member is better off with VMI than RMI in a two-product setting. Additionally, the incorporation of consumer response to retail stockouts sheds light on how product substitution and consumers' store loyalty affect the value of VMI to each supply chain member. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [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]

Despite the exponential growth of artificial intelligence (AI) research in operations, supply chain, and productions management literature, empirical insights on how organisational behavioural mechanisms at the human–technology interface will facilitate AI adoption in small- and medium-sized enterprises (SMEs), and subsequent impact of the adoption on sustainable practices and supply chain resilience (SCR) is under-researched. To bridge these gaps, we integrate resource orchestration and knowledge-based view theoretical perspectives to develop a novel structural model examining antecedents to SCR and AI adoption, considering AI adoption as a pivot for facilitating SCR. The structural equation modelling technique was employed on the data collected from 280 Vietnamese manufacturing SMEs' operations managers. Our results demonstrate that leadership will drive AI adoption by creating a data-driven, digital and conducive culture, and strengthening employee skills and competencies. Furthermore, AI adoption positively influences CE practices, SC agility and risk management, which will help to achieve SCR. For managers, the importance of internal organisational employee-centric mechanisms to create value from AI adoption without impeding business value is highlighted. We reveal the enablers that will help in transforming SMEs to become resilient by deriving appropriate responses to unprecedented disruptions through data-driven decision-making leveraging AI adoption. [ABSTRACT FROM AUTHOR]

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model. [ABSTRACT FROM AUTHOR]

Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial $ 2^{nd} $ 2 nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction. [ABSTRACT FROM AUTHOR]

Global trade is plagued by slow and inefficient manual processes associated with physical documents. Firms are constantly looking for new ways to improve transparency and increase the resilience of their supply chains. This can be solved by the digitalisation of supply chains and the automation of document- and information-sharing processes. Blockchain is touted as a solution to these issues due to its unique combination of features, such as immutability, decentralisation and transparency. A lack of business cases that quantify the costs and benefits causes uncertainty regarding the truth of these claims. This paper explores how the costs and benefits of a blockchain-based solution for digitalising and automating documentation flows in cross-border supply chains compare to a conventional centralised relational database solution. The research described in this paper uses primary data collected through semi-structured interviews with industry experts, as well as secondary data from literature. Two models based on existing services were developed and the costs and benefits compared and then analysed using the Architecture Trade-off Analysis Method (ATAM) and the Analytic Network Process (ANP). Findings from the analysis show that a consortium blockchain solution like TradeLens is the favourable solution for digitalising and automating information flows in cross-border supply chains. [ABSTRACT FROM AUTHOR]

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research. [ABSTRACT FROM AUTHOR]

In this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

This paper examines how physical climate exposure affects firm performance and global supply chains. We document that heat at supplier locations reduces the operating income of suppliers and their customers. Further, customers respond to perceived changes in suppliers' exposure: when suppliers' realized exposure exceeds ex ante expectations, customers are 7% more likely to terminate supplier relationships. Consistent with experience-based learning, this effect increases with signal strength and repetition and decreases with country-level climate adaptation. Subsequent replacement suppliers show a lower expected and realized but similar projected heat exposure. We find similar results for suppliers' exposure to floods. [ABSTRACT FROM AUTHOR]

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members. [ABSTRACT FROM AUTHOR]

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic exposed the vulnerabilities of global supply chains (SCs) and highlighted the need for more resilient and viable SCs. Panic-buying, in particular, has been a major challenge for SCs as it can create sudden surges in demand that are difficult to anticipate and manage. However, the literature lacks viable SC models and strategies to address panic-buying related challenges. As such, this research aims to identify and model viable recovery strategies to increase SC's agility, resilience, and survivability and reduce panic-buying's impact during a large-scale disruption in critical SCs. This study contributes by developing an integrated agent-based modeling (ABM) and optimisation method to simulate the behaviour of SCs under different scenarios and evaluating the effectiveness of four proposed strategies and three recovery plans. The findings reveal that increasing production at decentralised manufacturing facilities can be achieved by increasing order frequency to multiple suppliers and by partnering with third-party transporters, which can mitigate the effects of panic-buying. This results in higher output and availability of essential goods in the market, significantly managing panic-buying related challenges. Lastly, the study recommends practical solutions for businesses to enhance their SCs' responsiveness to sudden demand surges from panic-buying. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem. [ABSTRACT FROM AUTHOR]

In this paper, stochastic optimisation of CVaR is applied to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. In order to establish the risk-averse boundaries on supply chain viability space, two stochastic optimisation models are developed with the two conflicting objectives: minimisation of Conditional Cost-at-Risk and maximisation of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory between the two boundaries is selected using a stochastic mixed integer quadratic programming model. The proposed approach is applied to maintain the supply chain viability in the smartphone manufacturing and the results of computational experiments are provided. The findings indicate that when the decision-making is more risk-aversive, the size of the viability space between the two boundaries is greater. As a result, more room is available for selecting viable production trajectories under severe disruptions. Moreover, the larger is viability space, the higher is both worst-case and average resilience of the supply chain. Risk-neutral, single-objective decision-making may reduce the supply chain viability. A single-objective supply chain optimisation which moves production to the corresponding boundary of the viability space, should not be applied under severe disruption risks to avoid greater losses. [ABSTRACT FROM AUTHOR]

Although supply chain ecosystem health (SCE Health) is receiving attention in relation to environmental uncertainty, its conception and measurement are largely undocumented, and how to pursue SCE Health under environmental turbulence is unclear. Supply chain learning (SCL) is an important way to build dynamic capabilities, and whether it can empower the achievement of SCE Health is worthy of investigative study. Therefore, grounded in the dynamic capabilities theory, a survey data-based structural equation modelling (SEM) approach is employed. Based on four experts' opinions and an in-depth literature review, 47 measurement items (11 for SCL, 28 for SCE Health, and 8 for environmental turbulence) were identified in the questionnaire design. Further, 208 valid questionnaires from the field survey of supply chain management (SCM)-related firms in China were collected and used for SEM analysis. The results show that the internal learning of SCL stimulates its external learning. SCL empowers the pursuit of SCE Health, which is strengthened under higher environmental turbulence. The theoretical framework and results also derive practical insights and support from 11 interviewees of five companies. [ABSTRACT FROM AUTHOR]