Search Results (202)

Background: Supply chain management (SCM) research has expanded across the Middle East and North Africa (MENA), yet the field remains fragmented. Limited synthesis exists on how regional conditions shape research themes, theories, and methods. Methods: This study applies the PRISMA 2020 protocol to review SCM articles indexed in Scopus and Web of Science from January 2000 to March 2025. After screening and eligibility assessment, 512 peer-reviewed studies were retained. Bibliometric mapping and thematic coding were used to identify publication trends, research streams, theoretical lenses, and methodological patterns. Results: SCM research increased sharply after 2015, reflecting national diversification agendas, logistics reform, digitalization, and exposure to global supply chain disruptions. Three dominant streams were identified: resilience, sustainability, and digital transformation. Research output is concentrated in Saudi Arabia and the United Arab Emirates, while cross-country comparative studies remain scarce. Empirical studies rely mainly on cross-sectional surveys and SEM-based analysis, with limited longitudinal, qualitative, mixed-method, and comparative work across the region. Conclusions: The study develops an integrative SCM capability framework linking regional structural conditions, capability development, and supply chain outcomes. The findings support managers and policymakers seeking resilient, sustainable, and digitally enabled supply chains, and define clear future research priorities for the MENA region. Full article

Supply chain management (SCM) involves complex coordination among multiple actors under demand uncertainty. However, most existing studies focus on simplified network structures that fail to capture all relevant dimensions of real-world supply chains or assume deterministic demand. This study proposes a comprehensive stochastic bi-level optimization framework for a multi-factory, multi-retailer, multi-customer, and multi-product supply chain network. The model captures the hierarchical interaction between decision-makers, where the production facility owner acts as the leader and the retailer as the follower, and jointly optimizes profit across both levels. To efficiently solve the resulting bi-level problem, two tailored metaheuristic solution approaches—a two-tier genetic algorithm (TT-GA) and a two-tier particle swarm optimization (TT-PSO)—are developed. Computational experiments across multiple scenarios demonstrate that TT-PSO outperforms TT-GA in Scenarios 1 and 2, achieving overall profit improvements of 6.46% and 0.76%, respectively, while TT-GA yields superior performance in Scenario 3 with a 2.80% profit improvement. The proposed framework provides decision-makers with a robust and practical tool for improving profitability and operational efficiency in complex, uncertain supply chain environments. Full article

Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints. Full article

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. Full article

Disruptive global events such as the COVID-19 pandemic have exposed critical vulnerabilities in the construction industry’s reliance on lean principles and Just-In-Time (JIT) methodologies. These disruptions, categorized as Black Swan Events (BSEs), challenged conventional supply chain management (SCM) and risk management (RM) strategies, resulting in delayed projects and increased costs. This paper explores how BSEs affect construction supply chains and evaluates the industry’s evolving response through RM and resilience-building strategies. A Joanna Briggs Institute (JBI) scoping review of the literature (2000–2024) synthesized evidence across SCM, RM, Lean Construction, JIT, and BSEs, triangulating 86 peer-reviewed studies with authoritative industry reports. The review reveals a lack of integrated research addressing these themes holistically for the construction sector. Key findings show that while JIT and lean approaches optimize efficiency, they fall short during high-impact, low-probability disruptions. Evidence indicates a selective shift toward Just-In-Case (JIC) practices; however, the extent and persistence of this transition vary by project context and merit further study. The study proposes a future research agenda emphasizing interdisciplinary models that integrate lean methods with resilience and anticipatory strategies. These insights aim to support construction firms in developing supply chains that are not only efficient but also adaptable and better prepared for future BSEs. Full article

Supplier assessment plays a pivotal role in various sectors and is a key component of supply chain management (SCM); however, it combines complexities and challenges associated with uncertainty and dynamic requirements. Several studies have applied various methods to assess suppliers; however, their calculations are complex. Therefore, this work introduces a new hybrid method, depending on the full consistency method (FUCOM) with triangular fuzzy numbers (TrFNs) to consistently prioritize supplier assessment criteria, using the distance from the ideal assessment criterion to assess various suppliers, as well as to handle uncertainty. The proposed method is applied to a practical case study, and the results show that it prioritizes the assessment criteria, reflecting stakeholder preferences. Moreover, it enables decision makers to construct a set of decision matrices and take into account various viewpoints, and it uses simple, detailed steps with TOPSIS, thus avoiding confusion. Additionally, the results indicate that the use of the FUCOM-TrFNs provides consistent weights and a robust tool for imprecise problems, with fewer comparisons and less confusion during calculations. Ultimately, the findings provide valuable insights for assessing and selecting suitable suppliers with a more applicable method. Full article

Smart supply chains, enabled by Industry 4.0 technologies, are increasingly recognized as key drivers of competitiveness, leveraging data across the value chain to enhance visibility, responsiveness, and resilience, while supporting better planning, optimized resource utilization, and agile customer service. Effective data management has thus become a strategic capability, fostering operational performance, innovation, and long-term value creation. However, existing research and practice remain fragmented, often focusing on isolated functions such as production, logistics, or quality, the most data-intensive and critical domains in smart manufacturing, without comprehensively addressing data acquisition, storage, integration, analysis, and visualization across all supply chain phases. This article addresses these gaps through a systematic literature review of 55 peer-reviewed studies published between 2020 and 2025, conducted following PRISMA guidelines using Scopus and Web of Science. Contributions are categorized into reviews, frameworks/models, and empirical studies, and the analysis examines how data is collected, integrated, and leveraged across the entire supply chain. By adopting a holistic perspective, this study provides a comprehensive understanding of data management in smart manufacturing supply chains, highlights current practices and persistent challenges, and identifies key avenues for future research. Full article

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article

This study examines how inventory management influences accrual-based earnings management in emerging markets. Specifically, it analyzes the effect of three inventory performance indicators—Inventory Turnover Ratio (ITR), Inventory Service Level (ISL), and Inventory Coverage Rate (ICR)—on discretionary accruals (AVDA), measured as the absolute value of discretionary accruals estimated using the Kothari model. The Moroccan context offers a relevant setting due to the scarcity of research linking operational supply-chain metrics to financial reporting practices in emerging economies. The empirical analysis relies on 321 firm-year observations from 41 non-financial companies listed on the Casablanca Stock Exchange between 2016 and 2023. A panel fixed-effects regression model is employed to assess the association between inventory indicators and AVDA. Results show a significant negative relationship between ISL and discretionary accruals, while ITR and ICR exhibit no significant effects. These findings indicate that higher inventory service reliability is associated with reduced earnings management, highlighting the governance role of inventory-related SCM practices in Morocco. Full article

This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable insights, enhancing transparency, improving forecasting, optimizing production and inventory, reducing waste, and enabling circular economy practices. Complementarily, OR provides methodological rigor through optimization models, simulation, and multicriteria decision-making, enabling organizations to balance economic, environmental, and social objectives in supply chain design and operations. The findings reveal that BI and OR jointly contribute to 11 of the 17 United Nations Sustainable Development Goals (SDGs), demonstrating their strategic relevance for global sustainable development. This paper’s contribution is twofold: it consolidates fragmented academic research through an integrative framework clarifying how BI and OR reinforce sustainability within SCM, and it provides practitioners with evidence of how these tools can generate both operational efficiency and a competitive advantage while meeting environmental and social responsibilities. Future research should focus on bridging existing gaps in the literature and advancing the practical applications of these technologies. Full article

This article aims to identify changes in the components of pellet supply chain management (SCM). The following research question is explored: To what extent are pellet supply chains changing? A research gap was identified in the use of pellets for energy and the analysis of safe management of logistics processes in the pellet supply chain (PSC). The study uses theoretical and empirical research methods: literature analysis and statistical methods covering the years 2017–2023 and scientific observation to obtain information about the facts, phenomena, and components of safe management of logistics processes in the PSC. The results of the study suggest that supply chains play a role as one of the main drivers of energy transition, and the PSC may be one of them. A modified PSC can contribute to more environmentally friendly procurement, leaner logistics, and tighter scheduling, reducing waste and emissions in existing energy systems without immediately changing the electricity/fuel mix. Full article

This study examines the evolution of environmental discourse in supply chain management (SCM) research from 2004 to 2024, systematically comparing scholarly trajectories with media narratives to identify critical implementation gaps at the theory–practice interface. Following PRISMA guidelines, we employ structural topic modeling on 6586 academic articles and 384,190 media articles (2019–2023) within the SPAR-4-SLR protocol, we document substantial growth in sustainability scholarship—from fewer than 200 publications in 2004 to over 700 in 2024—with research emphasis shifting from compliance-oriented frameworks toward strategic integration models. Systematic comparison reveals significant misalignments: six domains—community-based sustainability initiatives, climate adaptation strategies, plastic reduction mandates, food security resilience, event-driven crisis responses, and sustainable product design—receive substantially greater media emphasis than scholarly investigation, constituting what we characterize as the implementation knowledge gap. This gap reflects disconnection between theoretically sophisticated academic frameworks emphasizing long-term strategic integration and practitioner concerns prioritizing acute operational challenges, rapid regulatory compliance, and grassroots sustainability mechanisms. Our findings demonstrate that, while academic research remains theoretically robust, it insufficiently captures short-term adaptation imperatives, community-level integration mechanisms, and sector-specific resilience strategies that climate volatility demands. By establishing a transferable analytical framework integrating media discourse with academic literature, this study advances sustainable supply chain management theory through reconceptualizing implementation challenges as central research concerns while generating actionable imperatives for aligning scholarship, policy interventions, and industrial strategies toward climate-resilient supply chain systems. Full article

The rise of Industry 4.0 has made digital transformation a critical element of modern supply chain management, offering organizations a pathway to competitive advantage. While the prior literature has examined aspects of digitalization, few studies present a comprehensive view of the transformation process through to its practical application. This study proposes and aims to validate a digital transformation model developed by the author, based on a systematic literature review of 284 articles from Scopus and Web of Science. The model outlines a progression from data management to the integration of enabling technologies, culminating in enhanced supply chain decision-making. It introduces three key metrics—readiness, adoption, and Digital Maturity—to guide companies through distinct stages of transformation. Validation will be conducted using a mixed-methods approach, combining expert interviews and a quantitative survey with SCM professionals. This study offers both a theoretical framework and practical roadmap to support organizations in evolving their digital transformation strategies. Full article

Organizations increasingly face challenges in aligning project management and supply chain management, as project success relies on reliable supply chains while supply chain resilience hinges on effective project coordination. Despite the growing recognition of this interdependence, research remains fragmented, with most studies treating PM and SCM in isolation, limiting systematic theorization and practical guidance for integration. Addressing this gap, this review examines how optimization methods can facilitate PM–SCM integration. Through a comprehensive bibliometric analysis, incorporating co-citation, keyword co-occurrence, and cluster analysis, the study maps the intellectual structure, thematic evolution, and diverse applications of optimization within both domains. The findings uncover key trends, showing that optimization provides a methodological foundation for managing complexity and uncertainty across diverse integration scenarios, including project scheduling, resource allocation, and supply chain coordination. It further reveals that emerging technologies extend these optimization approaches by enabling real-time prediction, improved transparency, and adaptive decision-making. Theoretically, the study reframes PM and SCM as interdependent components of an adaptive system, offering a concrete and analytically tractable framework for operationalizing integration. Practically, it outlines strategies for strengthening cross-domain coordination and risk management through optimization-enabled solutions. By consolidating fragmented research, this review not only synthesizes the evolution of optimization in PM–SCM contexts but also identifies critical future opportunities, emphasizing the development of scenario-specific models, technology-driven integration mechanisms, and resilience-oriented strategies to enhance performance in project-intensive settings. Full article

This study aims to develop and evaluate an AI- and big-data-based innovation system for proactively managing ESG (Environmental, Social, and Governance) risks within the tourism supply chain. Drawing on heterogeneous data sources including supply chain records, news articles, social media, and public databases, the research employs advanced methodologies such as network analysis, anomaly detection, natural language processing (including greenwashing detection), and predictive modeling. Through this comprehensive approach, the study demonstrates the feasibility and effectiveness of a dynamic AI-driven ESG risk management system that delivers reliable risk identification and quantitative performance evaluation. The theoretical contribution lies in bridging AI-driven ESG evaluation frameworks with sustainable tourism and hospitality literature, moving beyond static, indicator-based assessments toward a more systematic, replicable, and predictive methodology capable of capturing the dynamic, multiscalar, and networked nature of tourism supply chains. Ultimately, this research provides tourism and hospitality firms with a powerful tool to enhance transparency, mitigate ethical and reputational risks, and strengthen stakeholder trust, while offering actionable insights for managers and policymakers developing data-driven ESG integration strategies. Full article