Supply Chain Analytics最新文献

{"title":"An analytical review of artificial intelligence applications in sustainable supply chains","authors":"Amirhossein Ghasemi Abyaneh ,&nbsp;Hossein Ghanbari ,&nbsp;Emran Mohammadi ,&nbsp;Amirali Amirsahami ,&nbsp;Masoud Khakbazan","doi":"10.1016/j.sca.2025.100173","DOIUrl":"10.1016/j.sca.2025.100173","url":null,"abstract":"<div><div>Sustainable supply chains are essential for promoting environmental responsibility, economic efficiency, and social well-being. They help reduce carbon footprints, optimize resource use, and support circular economy initiatives. Economically, they enhance efficiency, lower costs, and mitigate risks related to resource scarcity and environmental regulations. Socially, they ensure ethical sourcing, fair labor practices, and corporate social responsibility. By balancing these dimensions, sustainable supply chains contribute to business resilience while aligning with global sustainability goals, such as the UN Sustainable Development Goals (SDGs). In the age of Artificial Intelligence (AI), rapid technological advancements have significantly transformed supply chain operations, necessitating greater flexibility and the integration of AI-driven techniques. The application of AI in supply chain management has proven highly beneficial, offering enhanced efficiency, predictive capabilities, and improved sustainability. Recent advancements, including Large Language Models (LLMs), are also playing a transformative role in enhancing decision-making and risk management across supply chains. Numerous researchers have highlighted AI's potential in advancing circular economy initiatives by optimizing resource utilization and minimizing waste. However, despite the growing academic interest, research in this domain remains fragmented and lacks a coherent structure. To address this gap, this paper conducts a comprehensive bibliometric analysis to map the current research landscape, identify key themes, and highlight future directions. Bibliographic records were retrieved from the Web of Science database, covering the period from 1997 to 2024. A total of 1070 records were initially gathered for analysis. The findings of this study provide valuable insights into the evolution of research in AI-driven sustainable supply chains, uncover emerging trends, and suggest potential avenues for future exploration. Specifically, the analysis reveals an annual publication growth rate of 23.37 % from 1997 to 2024, with China, India, and the USA as the top contributing countries. Core research themes include AI-enabled logistics optimization, circular economy practices, and supply chain resilience under global disruptions. By offering a structured overview of the field, this study aims to support scholars and practitioners in navigating the intersection of AI and sustainability in supply chain management.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective analytics approach for supply chain optimization in flexible manufacturing systems","authors":"Shahed Mahmud ,&nbsp;Ripon K. Chakrabortty ,&nbsp;Alireza Abbasi ,&nbsp;Michael J. Ryan","doi":"10.1016/j.sca.2025.100170","DOIUrl":"10.1016/j.sca.2025.100170","url":null,"abstract":"<div><div>This study contributes to the advancement of supply chain scheduling (SCS) through the development of a comprehensive mathematical framework that unifies multi-objective supplier selection, demand allocation, production scheduling, and inventory management within flexible manufacturing systems (FMS). Amidst the rapid progress of FMS and Industry 4.0 technologies, integrating these supply-chain decisions has become indispensable for ensuring timely and cost-efficient delivery of customized products. Yet research remains limited when supply, inventory, flexible routing and sequencing decisions must be handled simultaneously, often yielding conflicting objectives. We therefore propose a bi-objective SCS model that jointly optimizes supply, inventory and production portfolios to meet heterogeneous customer orders under due-date constraints. The shop floor is modeled as a flexible job shop with sequence-dependent setup times and inventory constraints, and the framework embeds supplier selection and demand allocation decisions for critical parts. Since the resulting problem is NP-hard, a multi-objective JAYA algorithm (MOJAYA) is devised, featuring a Pareto-cluster update rule and a problem-specific co-evaluated local search. Extensive experiments on 15 benchmark instances show MOJAYA, against four established algorithms, consistently yields wider, more uniform Pareto fronts, lowering mean inverted generational distance by up to 48% and increasing hyper-volume by up to 23% within the same computational budget; Friedman and Wilcoxon tests confirm these gains are statistically significant (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>). In a representative instance, the decision schedule costs 357448 with 44 cumulative tardiness, and supplies managers with detailed production, supply, and inventory portfolios. The proposed approach therefore enhances decision-making flexibility across supply-chain stages, offering a data-driven tool for SCS problems.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From black box to analytical insight: A data-driven evaluation of technological sustainability in manufacturing supply chains","authors":"Marco Vacchi ,&nbsp;Davide Settembre-Blundo ,&nbsp;Luca Iattici ,&nbsp;Anna Maria Ferrari ,&nbsp;Roberto Rosa ,&nbsp;Nicholas Berselli","doi":"10.1016/j.sca.2025.100171","DOIUrl":"10.1016/j.sca.2025.100171","url":null,"abstract":"<div><div>Technological infrastructures critically drive resilience and sustainability in manufacturing supply chains yet remain severely underrepresented in conventional sustainability assessment frameworks. This paper introduces the Organizational Technological Sustainability Assessment (O-TSA), an innovative data-driven model that transforms operational technology data into strategic insight. Anchored in Life Cycle Thinking, O-TSA evaluates technological sustainability through three quantifiable dimensions: Input/Output Availability, Operational Performance, and Technical Quality, each measured via weighted indicators and standardized scoring functions. The framework delivers two actionable metrics: the Technological Sustainability Index (TSI), providing a precise measurement of current technological maturity, and the Technology Improvement Index (TII), quantifying performance evolution to enable evidence-based decision-making. When applied to a ceramic tile manufacturer, the model revealed specific operational inefficiencies while documenting a significant improvement in technological sustainability over a one-year period, primarily through enhanced documentation systems and digital integration. Empirical validation confirms the model's effectiveness in converting fragmented data streams into prioritized action points. By rendering previously invisible technological dependencies explicit and measurable, the O-TSA framework enables supply chain managers to align technological investments with sustainability objectives, facilitating the development of analytically-managed, resilient industrial ecosystems in resource-intensive environments.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning and policy optimization approach for supply chain order classification","authors":"Ramakrishna Garine ,&nbsp;Ripon K. Chakrabortty","doi":"10.1016/j.sca.2025.100166","DOIUrl":"10.1016/j.sca.2025.100166","url":null,"abstract":"<div><div>Timely delivery is a critical performance metric in supply chain management, yet achieving consistent on-time delivery has become increasingly challenging in the face of global uncertainties and complex logistics networks. Recent disruptions, such as pandemics, extreme weather events, and geopolitical conflicts, have exposed vulnerabilities in supply chains, resulting in frequent delivery delays. While traditional heuristics and simple statistical methods have proven inadequate to capture the myriad factors that contribute to delays in modern supply chains, Machine learning (ML) and Deep Learning (DL) approaches have emerged as powerful tools to improve the accuracy and reliability of delivery delay prediction. Consequently, this study presents a hybrid predictive framework that integrates DL models with Reinforcement Learning (RL) to improve binary classification of order status (on-time vs. late). We first benchmark several DL architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Stacked LSTM, enhanced with regularization and extended training epochs, alongside a fine-tuned eXtreme Gradient Boost (XGBoost) model. These models are evaluated using accuracy, precision, recall, and the F1-score, with Bi-LSTM and Stacked LSTM achieving strong generalization performance. Building on this, we deploy a Proximal Policy Optimization (PPO) agent that incorporates deep learning outputs as part of its observation space. The RL agent uses a reward-based feedback loop to improve adaptability under dynamic conditions. Experimental results show that the hybrid DL-RL model achieves superior classification accuracy and an F1-score greater than 0.99, outperforming standalone methods. Although the PPO agent alone struggled with detecting minorities due to imbalance, integrating DL features mitigated this limitation. The findings support the use of hybrid architectures for real-time order status prediction and provide a scalable pathway for intelligent supply chain decision making. Future work will address class imbalance and enhance policy robustness through cost-sensitive and explainable RL strategies.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analytical framework for decision criteria validation in complex supply chains","authors":"Frank Michael Theunissen,&nbsp;Shafiq Alam,&nbsp;Aymen Sajjad","doi":"10.1016/j.sca.2025.100169","DOIUrl":"10.1016/j.sca.2025.100169","url":null,"abstract":"<div><div>Multi-Criteria Decision Making (MCDM) in supply chain management often applies rigorous methods for weighting and aggregation yet devotes little attention to the structural validity of the decision criteria that precede them. Even when organisations do not proceed to full MCDM model application, criteria are still elicited during problem structuring and used to justify initiative selection. This paper introduces a topological validation framework that addresses this asymmetry by representing criteria as a high-dimensional Decision Criteria Configuration (DCC). Using tools from Topological Data Analysis (TDA), we translate foundational MCDM axioms into measurable invariants: completeness through connectivity, non-redundancy through structural impact analysis, and logical consistency through cycle detection. Two industrial experiments demonstrate the framework’s utility. In a supply chain strategy-setting workshop, TDA diagnosed the criteria set underpinning initiative selection as a “conceptual monolith,” revealing significant redundancies and systemic feedback loops overlooked by conventional facilitation. In a subsequent inventory classification exercise, the audit resolved expert deadlock by reducing 32 proposed criteria to a minimal, non-redundant core of six operationally essential levers, providing an objective and defensible basis for moving forward. By transforming criteria sets into auditable decision architectures, this approach ensures that MCDM models and the initiatives they justify rest on a validated foundation before weighting or ranking alternatives. For managers, it functions as a pre-hoc “structural audit,” reducing redundancy, exposing hidden interdependencies, and directing resources toward criteria that genuinely drive strategic and operational outcomes.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analytical investigation of inflation’s effects on supply chain strategies","authors":"Kosar Akhavan Chayjan ,&nbsp;Jafar Razmi ,&nbsp;Saman Hassanzadeh Amin","doi":"10.1016/j.sca.2025.100168","DOIUrl":"10.1016/j.sca.2025.100168","url":null,"abstract":"<div><div>Inflation poses significant challenges to supply chain operations by raising procurement and operational costs, dampening customer demand, and complicating decision-making for suppliers and retailers. This study investigates the optimization of supply chain strategies under inflationary pressures, addressing the inadequacy of traditional ordering and pricing approaches. We model a supply chain comprising one supplier and two retailers exposed to inflation-driven price volatility. Using an analytical optimization framework, eight scenarios are evaluated based in retailers’ adoption of hedging strategies through option contracts versus optimal order quantity strategies, while considering lead time dynamics and retailer competition. The results indicate that inflation profoundly influences optimal order quantities, supplier capacity, and the profitability of all supply chain participants. Full collaboration yields profit growth exceeding 1900 % compared to non-cooperative settings, whereas partial collaboration still results in gains of more than 25 %. Conversely, the least efficient scenarios incur profit losses of up to 95 %, highlighting the substantial penalty of insufficient coordination. Notably, the joint adoption of hedging strategies by both retailers yields the highest supply chain profit, particularly in environments characterized by longer lead times or elevated inflation rates. Hedging enables retailers to stabilize prices, sustain customer demand, and shield customers from inflation’s adverse effects. Furthermore, collaboration among retailers enhances overall supply chain resilience. This research offers actionable insights for practitioners aiming to aiming mitigate inflationary risks, emphasizing the essential roles of analytical planning, hedging, and coordination in supply chain management under inflationary conditions.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of text mining analytics for supply chain risk management using online data","authors":"Georgios Gelastopoulos,&nbsp;Christos Keramydas","doi":"10.1016/j.sca.2025.100167","DOIUrl":"10.1016/j.sca.2025.100167","url":null,"abstract":"<div><div>Global supply chains are increasingly complex and vulnerable, requiring new approaches for detecting and managing risks. Text mining, a branch of natural language processing, can extract insights from unstructured online data such as news, reports, and social media. This paper presents a systematic review of 33 peer-reviewed studies on text mining in supply chain risk management (SCRM). The review addresses four research questions: (i) which types of online data are used and how their characteristics affect reliability and timeliness, (ii) which techniques are applied and with what trade-offs, (iii) how text mining contributes to risk identification, prediction, and mitigation, and (iv) what gaps and opportunities remain for future research. A bibliometric analysis is also conducted to highlight publication trends, contributors, and thematic clusters. The findings reveal that Twitter and news sources dominate, while methods range from sentiment analysis and topic modeling to advanced neural models such as BERT. Applications emphasize risk identification and visibility, with emerging work in predictive analytics and decision support. A conceptual framework is proposed linking unstructured data to risk management decisions. This review contributes to the literature by underscoring the value of real-time textual for improving visibility, agility, and resilience in complex supply chains.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analytical approach to blockchain-driven identity management in sustainable forest supply chains","authors":"Robertas Damaševičius ,&nbsp;Rytis Maskeliūnas","doi":"10.1016/j.sca.2025.100161","DOIUrl":"10.1016/j.sca.2025.100161","url":null,"abstract":"<div><div>This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive bibliometric review of digitalization impacts on supply chain risk management","authors":"Hossein Ghanbari ,&nbsp;Mostafa Shabani ,&nbsp;Emran Mohammadi ,&nbsp;Hamidreza Seiti","doi":"10.1016/j.sca.2025.100165","DOIUrl":"10.1016/j.sca.2025.100165","url":null,"abstract":"<div><div>In the age of Industry 4.0, traditional supply chains are transforming into digital supply chains through the integration of advanced technologies such as the Internet of Things (IoT), Cloud Computing, Digital Twin, Artificial Intelligence (AI), and Blockchain Technologies. These technologies enable real-time data sharing, enhanced visibility, increased agility, robust data integrity and security, and more effective decision-making. As global supply chains face increasing uncertainties and complexities, the impact of digitalization on supply chain risk management has gained significant attention from both researchers and practitioners. However, despite this rising interest, the current research status in this field remains somewhat unclear. The existing body of work lacks a comprehensive overview of how digital technologies can influence risk management practices across different supply chain contexts. To address this gap, this paper aims to investigate the key research areas by applying bibliometric analysis to identify and visualize the underlying structure of the field of digitalization’s impact on supply chain risk management. We conducted a bibliometric review to analyze the structure and global trends related to the impact of digitalization on supply chain risk management, covering the period from 2000 to February 2024. A total of 1012 bibliographic records were initially retrieved from the Web of Science databases, with 1001 English-language records retained for analysis to ensure consistency and accessibility. Using bibliometric analyses, we examined key trends, topics, and interrelationships within the literature, providing insights into how research in this area has evolved and potential future directions it may take. The findings of this study contribute to a deeper understanding of current patterns and knowledge gaps, offering a foundation for further research efforts in this field.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A textual analytics approach to sustainable supply chain dynamics in European maritime logistics","authors":"George R. Dimakis,&nbsp;George Tsironis,&nbsp;Konstantinos P. Tsagarakis,&nbsp;Yannis Marinakis","doi":"10.1016/j.sca.2025.100163","DOIUrl":"10.1016/j.sca.2025.100163","url":null,"abstract":"<div><div>This paper offers a comprehensive analysis of 871 European maritime firms, focusing on the spatial distribution of their headquarters, workforce demographics and digital footprint, as measured by LinkedIn follower metrics. To complement the quantitative data, Latent Dirichlet Allocation (LDA) was employed for text mining analysis on company LinkedIn descriptions, revealing emergent themes in innovation, customer-centric philosophy and global integration. The results point to a strong regional concentration of firms in Great Britain and the Netherlands, reflecting historical marine legacies and robust port infrastructures. Furthermore, the prevalence of small to medium-sized enterprises (SMEs) highlights the industry’s fragmented yet resilient structure, while digital presence remains uneven across firm sizes, with only a minority achieving substantial influence and visibility on social media. To summarize, these insights suggest that maritime logistics holds potential to drive systemic improvements in operational coordination, regional development, and global trade connectivity. Enhancing its integration could support more efficient supply chains, mitigate regional disparities, and bolster the industry’s global competitiveness.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}