Digital transformation is a key driver of the Digital China initiative, while supply chain finance represents a significant innovation in financial services. However, the relationship between supply chain finance and enterprise digital transformation remains underexplored. Based on a sample of 20,165 firm-year observations from Chinese A-share non-financial listed firms over the period 2012-2024, this study investigates the impact of supply chain finance on enterprise digital transformation and its underlying mechanisms using a two-way fixed effects panel regression model with instrumental variable estimation. The results show that supply chain finance significantly enhances enterprise digital transformation, and the finding remains robust to alternative variable measurements and endogeneity tests. Mechanism analysis reveals that supply chain finance facilitates digital transformation through three channels: profit enhancement, cost improvement, and risk smoothing. Heterogeneity analysis indicates that the positive effect of supply chain finance varies with regional characteristics and digital technology endowments. By constructing a novel firm-level SCF indicator and identifying three distinct transmission channels, this study provides micro-level evidence on how supply chain finance serves the real economy and accelerates enterprise digital transformation.
This study used the coffee supply chain (SC) to explore the perceptions of organisational capabilities (OCs) held by suppliers (producers) and buyers (roasters). We conducted a thematic analysis to provide insights into synergies and discrepancies in OC perceptions, sustainability, and buyer - seller collaboration. Semi-structured interviews were conducted with 10 coffee suppliers in Brazil and 8 coffee buyers in Portugal. Interview transcripts were analysed using a mixed-methods content analysis, and a visual tool was used to generate conceptual cluster maps, supported by Leximancer software. For thematic analysis, the interviews were coded using MAXQDA. The results provide insights beyond the dyadic supplier - buyer perspective on OCs. We show that buyers and suppliers are not yet fully aligned to implement a sustainable SC due to OC-related limitations, a perceived lack of collaboration, and limited transparency in relationships and negotiations. Our findings indicate that, in long-term buyer - supplier relationships, both parties benefit from social capital. This study contributes to sustainable SC management by providing insights into OCs.
Industry 5.0 represents a value-oriented industrial paradigm that moves beyond the technology-driven logic of Industry 4.0 by placing human-centric, sustainability, and ethics at the core of production and supply systems. This study examines how these principles are reflected in logistics and supply chain management through a structured conceptual review. A PRISMA-inspired screening protocol was applied to the Scopus and Web of Science databases covering the period 2015-2025, resulting in the selection of 18 studies for conceptual synthesis. The review identifies three interrelated transformation dimensions shaping Industry 5.0-oriented supply chains: human - machine collaboration, social and environmental sustainability, and ethical, value-based technology governance. Human - machine collaboration connects technology adoption with operational outcomes through mechanisms related to ergonomics, cognitive workload, and employee well-being. Sustainability objectives influence system-level supply chain design and resource allocation decisions, while ethical governance frameworks guide responsible technology use and support managerial accountability and stakeholder trust. By theorising the interactions among these dimensions, the study develops an integrated conceptual framework that explains how value-based priorities reshape logistics and supply chain structures and management practices, and provides a foundation for future empirical research.
We investigate a centralised distribution network consisting of multiple retail stores and an online store. Each retail location faces unpredictable demand specific to its region, while the online store experiences varying demand across all locations. The warehouse primarily fulfils online orders, but retail stores can also use their inventory to support two omnichannel strategies: Ship-from-Store and Buy-Online-Pickup-in-Store. Before the selling season, retail stores must decide whether to implement one or both strategies, as they require prior preparation and resource allocation. Retail stores replenish their inventory from the distribution centre before the season starts. During the selling season, decision-makers optimise inventory allocation across the network to meet both in-store and online demand, aiming to maximise net profit. This problem is formulated as a two-stage stochastic optimisation model. We apply meta-heuristic techniques, including Simulated Annealing, Tabu Search, and Genetic Algorithm, to solve the model. Computational experiments demonstrate the effectiveness of these methods, achieving high-quality solutions with minimal deviation from the optimal outcome.
Fashion supply chains are under growing scrutiny for their environmental and social impacts, demanding a transition from linear to sustainable, circular models supported by digital innovation. This PRISMA-guided systematic review synthesises 48 peer-reviewed studies published over the past decade, examining how digitalisation enables sustainability in fashion supply chains, mapped to SCOR processes and triple-bottom-line outcomes. Findings reveal two dominant mechanisms: traceability platforms combining IoT/RFID with blockchain to ensure cross-tier transparency, and decision and virtualisation systems (AI/ML, big-data analytics, 3D CAD, digital sampling, digital twins and robotics) that translate shared data into operational amelioration. Improving visibility, forecasting, inventory accuracy, waste reduction and shorter lead times, although social impacts remain underexplored. Effective scale-up relies on interoperable standards, robust governance and supplier integration, while high costs, capability gaps and regulatory fragmentation persist as barriers. The review advances an integrative framework and proposes a research agenda on longitudinal evaluation, complementarities and open data governance.
The objective of this study is to identify supply chain risks and propose effective risk mitigation strategies in a manufacturing organisation. Unlike traditional methods, which rely on probability-based likelihood models, our approach blends qualitative and quantitative methods and examines supply chain risk mapping, thereby evaluating 'Time-to-recover' and 'Time-to-survive' as key performance indicators to measure risk. Our approach uncovers hidden risks within the supply chain and suggests that items perceived as low value have a greater impact on supply chain disruption. We further propose risk mitigation strategies tailored to each risk category to minimise the impact of unforeseen disruptions. Practitioners can utilise the study to enhance supply chain resilience for critical components in their organisations.
Supply chain integration is a key driver of new quality productivity(NQP), which is central to China's innovation-driven development strategy. Using panel data from Chinese A-share listed firms (2013-2022), we examine how supply chain integration affects NQP. The results show that supply chain integration significantly enhances NQP, specifically by promoting new production relations and new production tools, but not new labourers. Financing constraints serve as a mediating mechanism, whereas environmental uncertainty negatively moderates this relationship. The positive effect is stronger in high-tech, service, and large-sized firms. From a supply chain network perspective, this study provides empirical evidence that supply chain integration is a critical enabler of NQP, enriching supply chain management literature and offering a theoretical basis for accelerating enterprise development of NQP.
Freight-matching platforms have emerged as pivotal innovations, streamlining operations also for smaller cargo transporters. Despite their transformative potential, the widespread adoption of these platforms faces substantial barriers that impede their effective integration into existing supply chains. This study uses a systematic literature review to identify and categorise these barriers and the operational outcomes associated with the adoption of freight-matching platforms. Employing the Diffusion of Innovations (DOI) theory, we explore how specific characteristics - relative advantage, compatibility, complexity, testability, and observability - influence the adoption process across the theory's five stages: awareness, persuasion, decision, implementation, and confirmation. Our findings detail distinct challenges and benefits at each stage, offering targeted strategies to overcome barriers and maximise operational benefits. This advances the understanding of technology adoption in logistics and equips logistics companies and platform developers with strategic insights to effectively tailor their adoption strategies, thereby enhancing operational efficiency and competitive advantage in global supply chains.
Machine learning has been widely applied to backorder prediction in inventory management, yet cost-sensitive analysis is often overlooked. This study examines the trade-off between false positives and false negatives using adaptive F beta optimisation with dynamic thresholding, enabling tailored precision-recall control based on business-defined cost ratios. We evaluate conventional machine learning and deep learning algorithms with various class-imbalance handling strategies on a popular Kaggle backorder dataset. Experimental results indicate that Random Forest combined with random under-sampling achieves the strongest predictive performance. Notably, we also observe that a simplistic baseline-predicting the majority class 'no backorder' for all instances-can minimise total cost more effectively than advanced algorithms under certain cost scenarios. Specifically, when false positive costs equal or exceed 50% of the total misclassification cost, this baseline becomes more cost-efficient. Conversely, when false negative costs dominate, adaptive F beta thresholding yields substantial cost savings. These findings underscore the necessity of aligning model selection with domain-specific cost structures rather than optimising purely for statistical accuracy.
The objective of this study is to conduct a comparative analysis of emerging supply chain risks in the Industry 4.0 environment, as perceived by large company industry experts and SME professionals. This comparison is valuable for extending and validating prior research findings on risk perceptions and is expected to offer both conceptual and practical insights into risk mitigation strategies in the Industry 4.0 context. Data for this study were collected from n = 868 supply chain professionals working in Indian SMEs. The findings indicate that Indian SME supply chain practitioners with low technology adoption prioritise demand, disruption, and financial risks, whereas those with high technology adoption emphasise cybersecurity, disruption, and demand risks. The latter aligns closely with expert assessments, which identify disruption, cybersecurity, and system-related risks as key challenges in the Industry 4.0 environment (Rank order correlation = 0.69). These findings contribute to a deeper understanding of the decision-making processes of industry professionals as they develop risk management strategies.
The purpose of this study is to modelling supply chain risks (SCRs) in Iran's e-retailing industry in the post-COVID pandemic. This research employed a mixed methodology. We used a systematic literature review to identify risks and items. The identified SCRs and items were categorised by 10 experts, and the hierarchical relationships among them were determined using interpretive structural modelling (ISM). The complex interrelationships between various risks were analysed with partial least squares (PLS). Ten risks had been categorised into seven hierarchical levels. The environmental risks in level 7 as the most fundamental driving risks. This is followed by legal and regulatory risks (level 6), strategic and financial risks (level 5), resource risks (level 4), outsourcing and security risks at level three, operational risks, supplier risks (level 2), and level 1 represents the buyer risks as the most dependent outcomes. This paper addresses a significant gap by moving beyond simple risk identification to model the causal and hierarchical structure of SCRs.
Supplier selection and transportation are critical to supply chain management, as they directly affect purchasing performance and material flow efficiency. However, balancing procurement and transportation costs while ensuring supply reliability remains insufficiently addressed in the literature. This paper explores the impact of transportation on supplier selection and order allocation by proposing a multi-criteria decision support model based on mathematical programming. The model minimises total costs, including purchasing, ordering, transportation, and inventory, while considering delivery quality, supplier capacity, and buyer demand, with inventory managed across multiple stages encompassing suppliers, transit, and buyer. It then determinesefficient suppliers and optimal order quantities assigned to each. To validate the model, it was applied to a real-world case in the French railway sector. The results demonstrate that integrating transportation planning into the supplier selection process reduces costs while enhancing supply chain reliability and overall performance. The study also highlights relevant directions for future research.
Navigating the complexities of achieving resilience and operational excellence in healthcare supply chains requires urgent and strategic digital transformation (DT). This study, grounded in Resource-Based View (RBV) and Organisational Information Processing Theory (OIPT), investigates how transformational leadership and information sharing influence DT adoption in hospitals. Based on survey data from 200 healthcare managers, the findings reveal that information sharing significantly drives DT adoption, whereas transformational leadership shows no direct effect, challenging common assumptions. DT adoption positively impacts transparency, collaboration, and resilience, which are essential for operational excellence. However, while collaboration and resilience enhance performance, transparency alone shows a negative impact, suggesting that visibility without support structures may expose inefficiencies. These results highlight the non-linear and context-dependent nature of DT outcomes in healthcare, offering both theoretical and practical contributions. The study provides actionable insights for managers and policymakers to craft effective, context-aware digital strategies that strengthen supply chain capabilities in dynamic healthcare environments.
Process development is a key driver of corporate competitiveness, as its objectives, methods, and timing are crucial for minimising losses. Although several approaches aim to enhance logistics process efficiency, no widely adopted, structured framework exists for the integration of digital technologies such as artificial intelligence or augmented reality into logistics process development. This study addresses this research gap by proposing a novel, integrated framework that supports the digital transformation of logistics processes. The framework was developed through an inductive approach combining practical insights and theoretical findings. The operational logic of the developed framework was validated through a simulation-based case study derived from a real industrial process and modified for research purposes. In the case study demonstrating the applicability of the framework, production increased from 30 to 78 products, while resource utilisation improved from 25% to 70%. These results indicate substantial performance improvements in terms of efficiency and capacity utilisation, reinforcing the practical relevance of the framework.
This study presents a quantitative model that evaluates the impact of supplier diversification on supply chain resilience. It focuses on the supply of essential perishable products, such as vaccines, from unreliable suppliers prone to delays. An optimisation model is developed to determine the optimal supplier mix and is solved using a Particle Swarm Optimization algorithm. The model accounts for constant demand, suppliers with varying reliability levels, diverse fixed and variable costs, and delivery times subject to random delays. Due to the perishability of the products, maintaining safety stock is challenging; therefore, a quadratic penalty for stock-outs is incorporated. The study reveals that multi-sourcing can substantially reduce the total cost compared to relying on a single supplier, while also enhancing resilience. Cost savings increase with the number of suppliers, particularly when the penalty for unmet demand is high; however, the marginal benefits eventually diminish. A computational study indicates that using two suppliers instead of one can reduce expected total costs by 34.65%.
The study investigated the role of logistics integration in enhancing customer satisfaction within Zimbabwe's heavy equipment sector. Using a mix of explanatory and descriptive research designs, data was collected from 120 respondents across 40 companies through structured questionnaires. The findings revealed that logistics services among heavy equipment suppliers are poorly integrated, significantly impacting customer satisfaction in areas such as inventory availability, order accuracy, timeliness and on-time delivery. However, while logistics integration improved information accuracy, it had limited influence on effective communication. Key issues identified include delays in product availability, frequent errors in order processing, extended lead times, inconsistent delivery schedules, and the dissemination of inaccurate product information. The study recommends that suppliers improve demand forecasting and inventory management, ensure compliance with customer specifications, respond promptly to orders and consistently deliver accurate information. Additionally, training sales teams to handle the sector's complex customer needs is essential to enhance overall satisfaction. These measures are critical for building trust and maintaining competitiveness.
