Supply Chain Analytics最新文献_第4页

A comprehensive bibliometric review of digitalization impacts on supply chain risk management 数字化对供应链风险管理影响的综合文献计量学综述

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-09-18 DOI: 10.1016/j.sca.2025.100165

Hossein Ghanbari , Mostafa Shabani , Emran Mohammadi , Hamidreza Seiti

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.

在工业4.0时代，通过物联网（IoT）、云计算、数字孪生（digital Twin）、人工智能（AI）、区块链技术等先进技术的融合，传统供应链正在向数字供应链转型。这些技术能够实现实时数据共享、增强可见性、提高敏捷性、强大的数据完整性和安全性，以及更有效的决策。随着全球供应链面临越来越多的不确定性和复杂性，数字化对供应链风险管理的影响受到了研究者和实践者的极大关注。然而，尽管人们对该领域的兴趣日益浓厚，但目前该领域的研究现状仍不明朗。现有的工作缺乏对数字技术如何影响不同供应链背景下的风险管理实践的全面概述。为了解决这一差距，本文旨在通过应用文献计量学分析来调查关键研究领域，以识别和可视化数字化对供应链风险管理影响领域的潜在结构。我们进行了文献计量分析，分析了2000年至2024年2月期间数字化对供应链风险管理影响的结构和全球趋势。最初从Web of Science数据库中检索了总共1012个书目记录，其中保留了1001个英文记录进行分析，以确保一致性和可访问性。使用文献计量学分析，我们检查了文献中的关键趋势、主题和相互关系，为该领域的研究如何发展和未来可能采取的潜在方向提供了见解。本研究的发现有助于加深对当前模式和知识差距的理解，为该领域的进一步研究奠定了基础。

{"title":"A comprehensive bibliometric review of digitalization impacts on supply chain risk management","authors":"Hossein Ghanbari , Mostafa Shabani , Emran Mohammadi , 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":"2025-12-01","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}

引用次数: 0

A scoping review and bibliometric analysis of sustainable and resilient supply chain network design 可持续和弹性供应链网络设计的范围回顾和文献计量学分析

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.sca.2025.100162

Rahmi Yuniarti , Suparno , Niniet Indah Arvitrida

Designing sustainable and resilient supply chain networks (SRSCND) has become a strategic priority amid intensifying environmental pressures, market volatility, pandemic disruptions, and geopolitical uncertainties such as trade wars, resource nationalism, and regional conflicts. This study employs a hybrid bibliometric–scoping review (ScoRBA) combined with the PAGER framework to systematically map and synthesize 528 peer-reviewed articles published between 2015 and 2025. The analysis identifies five thematic clusters: (1) digitalization for sustainable decision-making, (2) energy and environmental priorities in low-carbon supply chains, (3) resilience and strategic planning under uncertainty, (4) value-oriented and data-driven reverse supply chains, and (5) heuristic optimization in green and closed-loop systems. Cross-cluster insights highlight that the most innovative solutions emerge at the intersections of these themes—for example, integrating digital decision-support systems with adaptive heuristic optimization for real-time network reconfiguration; coupling circular economy strategies with resilience planning to create low-carbon yet disruption-ready systems; and combining traceability infrastructures with value-recovery optimization in closed-loop networks. Although conceptual maturity is well established, operational maturity remains limited: most studies rely on theoretical modeling, simulation, or isolated case studies, with few sector-specific real-world applications. Social and behavioral dimensions, governance integration, and multi-sector disruption modeling remain underexplored. Future research should prioritize scaling pilot projects into multi-sector industrial implementations, embedding social, cultural, and behavioral factors into quantitative models, and developing adaptive real-time decision systems that integrate environmental, economic, and social objectives. Strengthening industry–academia collaboration, improving open-data access, and leveraging digital twin technologies will be critical to accelerate the transition from theoretical advances to scalable, practice-oriented solutions for building sustainable and resilient supply chains in an era of complex global risks.

在不断加剧的环境压力、市场波动、流行病破坏以及地缘政治不确定性（如贸易战、资源民族主义和地区冲突）的背景下，设计可持续和有弹性的供应链网络（SRSCND）已成为战略重点。本研究采用混合文献计量学-范围审查（ScoRBA）与PAGER框架相结合，系统地绘制和综合了2015年至2025年间发表的528篇同行评议文章。该分析确定了五个主题集群：(1)可持续决策的数字化；(2)低碳供应链的能源和环境优先事项；(3)不确定性下的弹性和战略规划；(4)价值导向和数据驱动的逆向供应链；(5)绿色闭环系统的启发式优化。跨集群洞察强调，最具创新性的解决方案出现在这些主题的交叉点上，例如，将数字决策支持系统与实时网络重构的自适应启发式优化相结合；将循环经济战略与弹性规划相结合，创建低碳但可应对破坏的系统；将可追溯性基础设施与闭环网络中的价值恢复优化相结合。虽然概念成熟度已经建立，但操作成熟度仍然有限：大多数研究依赖于理论建模、模拟或孤立的案例研究，很少有特定部门的实际应用。社会和行为维度、治理集成和多部门中断建模仍未得到充分探索。未来的研究应优先考虑将试点项目扩展到多部门的工业实施中，将社会、文化和行为因素嵌入定量模型中，并开发整合环境、经济和社会目标的自适应实时决策系统。在复杂的全球风险时代，加强产学研合作、改善开放数据获取和利用数字孪生技术，对于加速从理论进步向可扩展、以实践为导向的解决方案的转变，对于构建可持续和有弹性的供应链至关重要。

{"title":"A scoping review and bibliometric analysis of sustainable and resilient supply chain network design","authors":"Rahmi Yuniarti , Suparno , Niniet Indah Arvitrida","doi":"10.1016/j.sca.2025.100162","DOIUrl":"10.1016/j.sca.2025.100162","url":null,"abstract":"<div><div>Designing sustainable and resilient supply chain networks (SRSCND) has become a strategic priority amid intensifying environmental pressures, market volatility, pandemic disruptions, and geopolitical uncertainties such as trade wars, resource nationalism, and regional conflicts. This study employs a hybrid bibliometric–scoping review (ScoRBA) combined with the PAGER framework to systematically map and synthesize 528 peer-reviewed articles published between 2015 and 2025. The analysis identifies five thematic clusters: (1) digitalization for sustainable decision-making, (2) energy and environmental priorities in low-carbon supply chains, (3) resilience and strategic planning under uncertainty, (4) value-oriented and data-driven reverse supply chains, and (5) heuristic optimization in green and closed-loop systems. Cross-cluster insights highlight that the most innovative solutions emerge at the intersections of these themes—for example, integrating digital decision-support systems with adaptive heuristic optimization for real-time network reconfiguration; coupling circular economy strategies with resilience planning to create low-carbon yet disruption-ready systems; and combining traceability infrastructures with value-recovery optimization in closed-loop networks. Although conceptual maturity is well established, operational maturity remains limited: most studies rely on theoretical modeling, simulation, or isolated case studies, with few sector-specific real-world applications. Social and behavioral dimensions, governance integration, and multi-sector disruption modeling remain underexplored. Future research should prioritize scaling pilot projects into multi-sector industrial implementations, embedding social, cultural, and behavioral factors into quantitative models, and developing adaptive real-time decision systems that integrate environmental, economic, and social objectives. Strengthening industry–academia collaboration, improving open-data access, and leveraging digital twin technologies will be critical to accelerate the transition from theoretical advances to scalable, practice-oriented solutions for building sustainable and resilient supply chains in an era of complex global risks.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027093","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}

引用次数: 0

An integrated analytical framework for inventory and pricing of perishable products in multi-echelon supply chains 多级供应链中易腐产品库存与定价的综合分析框架

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-08-24 DOI: 10.1016/j.sca.2025.100157

Jesús Isaac Vázquez-Serrano , Leopoldo Eduardo Cárdenas-Barrón , Julio C. Vicencio-Ortiz , Neale R. Smith , Rafael Ernesto Bourguet-Díaz , Armando Céspedes-Mota , Rodrigo E. Peimbert-García

Inventory management and pricing strategies are fundamental to supply chain operations, particularly for wholesalers who serve as intermediaries between manufacturers and retailers at specific times. A wholesaler's profitability depends critically on two key operational decisions: effective inventory control to minimize costs and strategic price-setting for retail customers. This paper introduces an innovative hybrid model that combines optimization and discrete-event simulation to address these challenges, with a specific focus on perishable goods management and determining break-even pricing points. The proposed hybrid model is comprehensive in scope, accommodating multiple perishable products across various time periods and suppliers while accounting for the inherent uncertainties in wholesale operations. Its dual-component structure leverages optimization techniques for inventory cost minimization while employing simulation to address operational variability. The model provides detailed mathematical frameworks for calculating unit-level critical selling prices, both inclusive and exclusive of operational costs. To validate the model's effectiveness, the research presents a case study of a pharmaceutical wholesaler, drawing on data from the United Nations Office for Project Services. The hybrid model's performance was evaluated against two established empirical methodologies in the supply chain: the Lowest Acquisition Cost Approach and the Earliest Product Acquisition Approach. The results demonstrate significant improvements, with the hybrid model achieving a 20 % reduction in average total costs and an 18 % decrease in average critical selling price compared to traditional approaches.

库存管理和定价策略是供应链运作的基础，特别是对于在特定时间充当制造商和零售商之间中介的批发商。批发商的盈利能力主要取决于两个关键的经营决策：有效的库存控制以最大限度地降低成本和为零售客户制定战略价格。本文介绍了一种创新的混合模型，该模型结合了优化和离散事件模拟来解决这些挑战，特别关注易腐货物管理和确定盈亏平衡定价点。所提出的混合模型在范围上是全面的，在考虑批发业务中固有的不确定性的同时，可以适应不同时间段和供应商的多种易腐产品。它的双组件结构利用优化技术来最小化库存成本，同时采用模拟来解决操作的可变性。该模型提供了详细的数学框架，用于计算单位水平的关键销售价格，包括和不包括运营成本。为了验证该模型的有效性，本研究利用联合国项目事务厅的数据，对一家药品批发商进行了个案研究。混合模型的性能对比了两种已建立的供应链经验方法：最低获取成本法和最早产品获取法。结果显示了显著的改进，与传统方法相比，混合模型的平均总成本降低了20% %，平均关键销售价格降低了18% %。

{"title":"An integrated analytical framework for inventory and pricing of perishable products in multi-echelon supply chains","authors":"Jesús Isaac Vázquez-Serrano , Leopoldo Eduardo Cárdenas-Barrón , Julio C. Vicencio-Ortiz , Neale R. Smith , Rafael Ernesto Bourguet-Díaz , Armando Céspedes-Mota , Rodrigo E. Peimbert-García","doi":"10.1016/j.sca.2025.100157","DOIUrl":"10.1016/j.sca.2025.100157","url":null,"abstract":"<div><div>Inventory management and pricing strategies are fundamental to supply chain operations, particularly for wholesalers who serve as intermediaries between manufacturers and retailers at specific times. A wholesaler's profitability depends critically on two key operational decisions: effective inventory control to minimize costs and strategic price-setting for retail customers. This paper introduces an innovative hybrid model that combines optimization and discrete-event simulation to address these challenges, with a specific focus on perishable goods management and determining break-even pricing points. The proposed hybrid model is comprehensive in scope, accommodating multiple perishable products across various time periods and suppliers while accounting for the inherent uncertainties in wholesale operations. Its dual-component structure leverages optimization techniques for inventory cost minimization while employing simulation to address operational variability. The model provides detailed mathematical frameworks for calculating unit-level critical selling prices, both inclusive and exclusive of operational costs. To validate the model's effectiveness, the research presents a case study of a pharmaceutical wholesaler, drawing on data from the United Nations Office for Project Services. The hybrid model's performance was evaluated against two established empirical methodologies in the supply chain: the Lowest Acquisition Cost Approach and the Earliest Product Acquisition Approach. The results demonstrate significant improvements, with the hybrid model achieving a 20 % reduction in average total costs and an 18 % decrease in average critical selling price compared to traditional approaches.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007564","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}

引用次数: 0

A multi-phase analytics framework for supply chain supplier selection and order allocation with delay risks and Industry 4.0 readiness 考虑延迟风险和工业4.0准备的供应链供应商选择和订单分配的多阶段分析框架

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.sca.2025.100172

Hesam Shidpour , Nima Karimi , George Baryannis , Mohsen Shidpour

Numerous studies have addressed the Supplier Selection and Order Allocation (SSOA) problem, focusing on optimal quantity allocation. However, in practice, suppliers often fail to deliver allocated quantities on time due to operational delays or disruptions. Thus, incorporating supplier delays into order allocation decisions is essential. This paper introduces a multi-phase optimization framework that integrates the impact of delays into the SSOA process. In the initial phase, several Machine Learning (ML) algorithms are employed to predict delay probabilities at the order level. This study is the first to utilize ML-based delay probability predictions - rather than binary classification (on-time vs. delayed) - to determine optimal supplier allocations. The algorithms are evaluated using performance metrics such as accuracy, F1 score, precision, recall, and AUC, with TOPSIS used to select the most effective algorithm. Predicted probabilities are then aggregated to the supplier level for integration into the optimization model. Given the growing importance of Industry 4.0, the framework incorporates an Industry 4.0 Readiness Index (IRI), constructed using linguistic terms and interval numbers to handle subjective evaluations. The SWARA method is used to assign weights to evaluation criteria. These elements are embedded in a bi-objective optimization model, solved via the augmented ε-constraint method, aiming to minimize supply chain costs while maximizing suppliers' IRI scores. A numerical example based on a real-world case study validates the approach. Results show significant changes in supplier allocations when delay probabilities are considered, with a 4.84 % increase in total supply chain cost, primarily due to increased procurement in certain periods.

许多研究都针对供应商选择与订单分配（SSOA）问题，重点关注最优数量分配。然而，在实践中，由于运营延误或中断，供应商经常不能按时交付分配的数量。因此，将供应商延迟纳入订单分配决策是必不可少的。本文介绍了一个多阶段优化框架，它将延迟的影响集成到SSOA过程中。在初始阶段，使用几种机器学习（ML）算法来预测订单级别的延迟概率。这项研究首次利用基于机器学习的延迟概率预测——而不是二元分类（准时与延迟）——来确定最佳供应商分配。使用准确性、F1分数、精度、召回率和AUC等性能指标对算法进行评估，TOPSIS用于选择最有效的算法。然后将预测的概率聚合到供应商级别，以便集成到优化模型中。考虑到工业4.0的重要性日益增加，该框架纳入了工业4.0准备指数（IRI），该指数使用语言术语和区间数字构建，以处理主观评估。使用SWARA方法为评价标准分配权重。这些要素嵌入到一个双目标优化模型中，通过增广ε-约束方法求解，旨在最小化供应链成本，最大化供应商IRI得分。基于实际案例研究的数值示例验证了该方法的有效性。结果表明，当考虑延迟概率时，供应商分配发生了显著变化，供应链总成本增加了4.84 %，主要是由于某些时期采购的增加。

{"title":"A multi-phase analytics framework for supply chain supplier selection and order allocation with delay risks and Industry 4.0 readiness","authors":"Hesam Shidpour , Nima Karimi , George Baryannis , Mohsen Shidpour","doi":"10.1016/j.sca.2025.100172","DOIUrl":"10.1016/j.sca.2025.100172","url":null,"abstract":"<div><div>Numerous studies have addressed the Supplier Selection and Order Allocation (SSOA) problem, focusing on optimal quantity allocation. However, in practice, suppliers often fail to deliver allocated quantities on time due to operational delays or disruptions. Thus, incorporating supplier delays into order allocation decisions is essential. This paper introduces a multi-phase optimization framework that integrates the impact of delays into the SSOA process. In the initial phase, several Machine Learning (ML) algorithms are employed to predict delay probabilities at the order level. This study is the first to utilize ML-based delay probability predictions - rather than binary classification (on-time vs. delayed) - to determine optimal supplier allocations. The algorithms are evaluated using performance metrics such as accuracy, F1 score, precision, recall, and AUC, with TOPSIS used to select the most effective algorithm. Predicted probabilities are then aggregated to the supplier level for integration into the optimization model. Given the growing importance of Industry 4.0, the framework incorporates an Industry 4.0 Readiness Index (IRI), constructed using linguistic terms and interval numbers to handle subjective evaluations. The SWARA method is used to assign weights to evaluation criteria. These elements are embedded in a bi-objective optimization model, solved via the augmented ε-constraint method, aiming to minimize supply chain costs while maximizing suppliers' IRI scores. A numerical example based on a real-world case study validates the approach. Results show significant changes in supplier allocations when delay probabilities are considered, with a 4.84 % increase in total supply chain cost, primarily due to increased procurement in certain periods.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361619","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}

引用次数: 0

An equilibrium-based framework for managing collusion in multi-channel supply chains 基于均衡的多渠道供应链合谋管理框架

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.sca.2025.100176

Mohammad Akbarzadeh Sarabi , Fariborz Jolai , Ata Allah Taleizadeh

Collusion among retailers remains a persistent concern in multi-channel supply chains, where competition has intensified with the rise of online platforms and direct manufacturer sales. Despite extensive research on channel coordination and pricing strategies, limited attention has been given to how e-tailers and direct web-store channels influence collusive behavior and welfare outcomes. To address this gap, this study develops a game-theoretic model of a manufacturer-led supply chain comprising a traditional retailer, an e-tailer, and a manufacturer’s direct web-store. We analyze four structural scenarios and three decision-making modes (competition, collusion, and centralized coordination) to derive the equilibrium strategies of all participants. The results show that collusion increases retailers’ joint profits only when no web-store channel exists, but introducing a direct channel weakens the profitability and stability of collusion. Moreover, the welfare effects depend critically on the e-tailer’s contractual design: under the agency format, collusion may enhance coordination and total welfare, while under the reselling format, it raises prices and harms consumers. The study contributes to the emerging literature on anti-collusion mechanisms in digital supply chains, offering analytical insights and managerial guidance for manufacturers, retailers, and regulators seeking to manage competition and fairness in multi-channel environments.

在多渠道供应链中，零售商之间的串通仍然是一个挥之不去的问题。随着在线平台和制造商直销的兴起，供应链上的竞争加剧了。尽管对渠道协调和定价策略进行了广泛的研究，但对电子零售商和直接网络商店渠道如何影响串通行为和福利结果的关注有限。为了解决这一差距，本研究建立了一个制造商主导的供应链的博弈论模型，该模型由传统零售商、电子零售商和制造商的直接网络商店组成。本文分析了四种结构情景和三种决策模式（竞争、合谋和集中协调），得出了所有参与者的均衡策略。结果表明，只有在不存在网店渠道的情况下，零售商合谋才会增加零售商的共同利润，而引入直接渠道会削弱零售商合谋的盈利能力和稳定性。此外，合谋的福利效应主要取决于电子零售商的契约设计：在代理模式下，合谋可以增强协调和总福利，而在转售模式下，合谋提高了价格，损害了消费者。该研究为数字供应链中反合谋机制的新兴文献做出了贡献，为寻求在多渠道环境中管理竞争和公平的制造商、零售商和监管机构提供了分析见解和管理指导。

{"title":"An equilibrium-based framework for managing collusion in multi-channel supply chains","authors":"Mohammad Akbarzadeh Sarabi , Fariborz Jolai , Ata Allah Taleizadeh","doi":"10.1016/j.sca.2025.100176","DOIUrl":"10.1016/j.sca.2025.100176","url":null,"abstract":"<div><div>Collusion among retailers remains a persistent concern in multi-channel supply chains, where competition has intensified with the rise of online platforms and direct manufacturer sales. Despite extensive research on channel coordination and pricing strategies, limited attention has been given to how e-tailers and direct web-store channels influence collusive behavior and welfare outcomes. To address this gap, this study develops a game-theoretic model of a manufacturer-led supply chain comprising a traditional retailer, an e-tailer, and a manufacturer’s direct web-store. We analyze four structural scenarios and three decision-making modes (competition, collusion, and centralized coordination) to derive the equilibrium strategies of all participants. The results show that collusion increases retailers’ joint profits only when no web-store channel exists, but introducing a direct channel weakens the profitability and stability of collusion. Moreover, the welfare effects depend critically on the e-tailer’s contractual design: under the agency format, collusion may enhance coordination and total welfare, while under the reselling format, it raises prices and harms consumers. The study contributes to the emerging literature on anti-collusion mechanisms in digital supply chains, offering analytical insights and managerial guidance for manufacturers, retailers, and regulators seeking to manage competition and fairness in multi-channel environments.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528679","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}

引用次数: 0

An analytical framework for decision criteria validation in complex supply chains 复杂供应链中决策标准验证的分析框架

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-09-29 DOI: 10.1016/j.sca.2025.100169

Frank Michael Theunissen, Shafiq Alam, Aymen Sajjad

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.

供应链管理中的多准则决策（MCDM）通常采用严格的加权和汇总方法，但很少关注其前面决策准则的结构有效性。即使组织没有进行完整的MCDM模型应用，在问题构建过程中仍然会得出标准，并用于证明主动性选择的合理性。本文介绍了一个拓扑验证框架，通过将标准表示为高维决策标准配置（DCC）来解决这种不对称。使用拓扑数据分析（TDA）的工具，我们将基本的MCDM公理转化为可测量的不变量：通过连接实现完整性，通过结构影响分析实现非冗余，通过循环检测实现逻辑一致性。两个工业实验证明了该框架的实用性。在供应链战略制定研讨会上，TDA将支持主动性选择的标准集诊断为“概念性的大件”，揭示了传统促进所忽视的重大冗余和系统性反馈循环。在随后的盘存分类工作中，审计通过将32项拟议标准减少到最小的、非冗余的六个业务基本杠杆核心，解决了专家僵局，为下一步工作提供了客观和可靠的基础。通过将标准集转换为可审计的决策体系结构，该方法确保MCDM模型和它们所证明的计划在对备选方案进行加权或排序之前建立在经过验证的基础上。对于管理人员来说，它的功能是预先的“结构审计”，减少冗余，暴露隐藏的相互依赖性，并将资源导向真正驱动战略和操作结果的标准。

{"title":"An analytical framework for decision criteria validation in complex supply chains","authors":"Frank Michael Theunissen, Shafiq Alam, 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":"2025-12-01","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}

引用次数: 0

A textual analytics approach to sustainable supply chain dynamics in European maritime logistics 欧洲海运物流可持续供应链动态的文本分析方法

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.sca.2025.100163

George R. Dimakis, George Tsironis, Konstantinos P. Tsagarakis, Yannis Marinakis

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.

本文对871家欧洲海事公司进行了全面分析，重点关注其总部的空间分布、劳动力人口统计和数字足迹（以LinkedIn关注者指标衡量）。为了补充定量数据，使用潜在狄利克雷分配（Latent Dirichlet Allocation， LDA）对公司LinkedIn描述进行文本挖掘分析，揭示了创新、以客户为中心的理念和全球整合方面的新兴主题。研究结果表明，英国和荷兰的公司在区域上高度集中，这反映了历史上的海洋遗产和强大的港口基础设施。此外，中小企业（SMEs）的普遍存在凸显了该行业碎片化但具有弹性的结构，而不同企业规模的数字存在仍然不均衡，只有少数企业在社交媒体上获得了实质性的影响力和知名度。总而言之，这些见解表明，海上物流具有推动业务协调、区域发展和全球贸易连通性系统性改善的潜力。加强其整合可以支持更高效的供应链，缓解地区差异，并增强该行业的全球竞争力。

{"title":"A textual analytics approach to sustainable supply chain dynamics in European maritime logistics","authors":"George R. Dimakis, George Tsironis, Konstantinos P. Tsagarakis, 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":"2025-12-01","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}

引用次数: 0

A multi-objective analytics approach for supply chain optimization in flexible manufacturing systems 柔性制造系统供应链优化的多目标分析方法

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.sca.2025.100170

Shahed Mahmud , Ripon K. Chakrabortty , Alireza Abbasi , Michael J. Ryan

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 (

p < 0.05

). 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.

本研究通过开发一个综合的数学框架，将柔性制造系统（FMS）中的多目标供应商选择、需求分配、生产调度和库存管理统一起来，为供应链调度（SCS）的进步做出了贡献。在FMS和工业4.0技术的快速发展中，整合这些供应链决策对于确保及时和经济高效地交付定制产品至关重要。然而，当供应、库存、灵活的路线和排序决策必须同时处理时，研究仍然有限，往往产生相互冲突的目标。因此，我们提出了一个双目标SCS模型，该模型联合优化供应、库存和生产组合，以满足到期日期约束下的异构客户订单。车间被建模为具有顺序依赖的设置时间和库存约束的灵活作业车间，并且该框架嵌入了关键部件的供应商选择和需求分配决策。由于所得到的问题是np困难的，因此设计了一种多目标JAYA算法（MOJAYA），该算法具有帕累托聚类更新规则和特定于问题的协同评估局部搜索。在15个基准实例上进行的大量实验表明，与四种已建立的算法相比，MOJAYA始终产生更宽、更均匀的Pareto前沿，在相同的计算预算下，将平均倒代距离降低了48%，将超容量提高了23%。Friedman和Wilcoxon检验证实了这些增益在统计学上是显著的（p<0.05）。在一个有代表性的实例中，决策计划的成本为357448，累计延迟时间为44，并为管理人员提供详细的生产、供应和库存组合。因此，提出的方法提高了供应链各个阶段的决策灵活性，为SCS问题提供了数据驱动的工具。

{"title":"A multi-objective analytics approach for supply chain optimization in flexible manufacturing systems","authors":"Shahed Mahmud , Ripon K. Chakrabortty , Alireza Abbasi , 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><</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":"2025-12-01","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}

引用次数: 0

From black box to analytical insight: A data-driven evaluation of technological sustainability in manufacturing supply chains 从黑箱到分析洞察：制造业供应链中技术可持续性的数据驱动评估

Supply Chain Analytics

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.sca.2025.100171

Marco Vacchi , Davide Settembre-Blundo , Luca Iattici , Anna Maria Ferrari , Roberto Rosa , Nicholas Berselli

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.

技术基础设施对制造业供应链的弹性和可持续性至关重要，但在传统的可持续性评估框架中，技术基础设施的代表性仍然严重不足。本文介绍了组织技术可持续性评估（O-TSA），这是一种将运营技术数据转化为战略洞察力的创新数据驱动模型。O-TSA以生命周期思维为基础，通过三个可量化的维度来评估技术可持续性：投入/产出可用性、运营绩效和技术质量，每个维度都通过加权指标和标准化评分函数来衡量。该框架提供了两个可操作的指标：技术可持续性指数（TSI），提供当前技术成熟度的精确测量，以及技术改进指数（TII），量化绩效演变以实现基于证据的决策。当应用于瓷砖制造商时，该模型揭示了具体的操作效率低下，同时记录了一年来技术可持续性的显着改善，主要是通过增强文档系统和数字集成。经验验证证实了该模型在将碎片数据流转换为优先行动点方面的有效性。O-TSA框架将以前不可见的技术依赖关系呈现为明确和可测量的，使供应链管理者能够将技术投资与可持续性目标结合起来，促进资源密集型环境中分析管理、弹性工业生态系统的发展。

{"title":"From black box to analytical insight: A data-driven evaluation of technological sustainability in manufacturing supply chains","authors":"Marco Vacchi , Davide Settembre-Blundo , Luca Iattici , Anna Maria Ferrari , Roberto Rosa , 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":"2025-12-01","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}

引用次数: 0

An analytics-driven framework for securing industrial IoT-Enabled Supply Chain Management Systems 一个分析驱动的框架，用于保护工业物联网支持的供应链管理系统

Supply Chain Analytics

Pub Date : 2025-09-01 Epub Date: 2025-06-05 DOI: 10.1016/j.sca.2025.100128

Naveen Saran , Nishtha Kesswani

In today’s dynamic technological environment, the integration of IoT into Supply Chain Management Systems (SCMS) has significantly enhanced functionality, visibility, and decision-making. However, integrating Industrial-IoT (IIoT) with Supply Chain Networks (SCN) is an equally significant security concern because of interconnected systems amplified exposure and complexity. This study proposes an original Intrusion Detection System (IDS) framework based on the Staked Ensemble Model appropriate for IIoT-Enabled SCMS. A stacked ensemble model-based IDS framework operates as a novel solution to protect IIoT-Enabled SCMS. A multilayered system unites Extreme Gradient Boosting (XGBoost) with Light Gradient Boosting Machine (LightGBM) along with Deep Neural Networks (DNN) as a stacked ensemble design to enable decentralized and secure collaborative learning across the supply chain network and protect user data and maintain system stability as well as network reliability. On the other hand, Synthetic Minority Oversampling Technique (SMOTE) and Principal Component Analysis (PCA) are established techniques, and our contribution is in optimizing the application of those for IIoT traffic. We tackle the class imbalance in intrusion data with SMOTE to better detect rare attacks and to use PCA to reduce the high dimensions of feature space for less computational effort and more efficient pattern recognition. To meet the requirements of the IIoT use cases, these preprocessing techniques are effectively embedded in the framework. Moreover, the proposed modular IDS architecture, the curation and fine tuning of the various learners, and the approach to full validation are all novel. We rigorously evaluate the model under K-Fold Cross Validation using the IoT-23 dataset and prove superior detection performance when compared to state-of-the-art approaches. Specifically, this research contributes a scalable and efficient IDS for an IIoT scenarios such as real-world IIoT enabled SCMS, which improves security analytics and facilitates network defense in key operational functionalities such as low data rates, low computational resources availability and restricted communication over the year.

在当今动态的技术环境中，将物联网集成到供应链管理系统（SCMS）中大大增强了功能，可见性和决策。然而，将工业物联网（IIoT）与供应链网络（SCN）集成是一个同样重要的安全问题，因为互联系统会增加风险和复杂性。本研究提出了一种原始的入侵检测系统（IDS）框架，该框架基于适用于IIoT-Enabled SCMS的利害关系集成模型。基于堆叠集成模型的IDS框架作为一种新颖的解决方案来保护支持iiot的SCMS。多层系统将极端梯度增强（XGBoost）与光梯度增强机（LightGBM）以及深度神经网络（DNN）结合在一起，作为堆叠集成设计，实现跨供应链网络的分散和安全协作学习，保护用户数据，维护系统稳定性和网络可靠性。另一方面，合成少数派过采样技术（SMOTE）和主成分分析（PCA）是成熟的技术，我们的贡献是优化这些技术在工业物联网流量中的应用。我们利用SMOTE来解决入侵数据中的类不平衡问题，以更好地检测罕见的攻击，并利用PCA来降低特征空间的高维数，从而减少计算量，提高模式识别效率。为了满足工业物联网用例的需求，这些预处理技术被有效地嵌入到框架中。此外，所提出的模块化IDS架构、各种学习器的管理和微调以及完全验证的方法都是新颖的。我们使用IoT-23数据集严格评估K-Fold交叉验证下的模型，并证明与最先进的方法相比，该模型具有优越的检测性能。具体来说，本研究为工业物联网场景（如现实世界的工业物联网支持SCMS）提供了可扩展且高效的IDS，从而改进了安全分析并促进了关键操作功能（如低数据速率，低计算资源可用性和全年通信受限）的网络防御。

{"title":"An analytics-driven framework for securing industrial IoT-Enabled Supply Chain Management Systems","authors":"Naveen Saran , Nishtha Kesswani","doi":"10.1016/j.sca.2025.100128","DOIUrl":"10.1016/j.sca.2025.100128","url":null,"abstract":"<div><div>In today’s dynamic technological environment, the integration of IoT into Supply Chain Management Systems (SCMS) has significantly enhanced functionality, visibility, and decision-making. However, integrating Industrial-IoT (IIoT) with Supply Chain Networks (SCN) is an equally significant security concern because of interconnected systems amplified exposure and complexity. This study proposes an original Intrusion Detection System (IDS) framework based on the Staked Ensemble Model appropriate for IIoT-Enabled SCMS. A stacked ensemble model-based IDS framework operates as a novel solution to protect IIoT-Enabled SCMS. A multilayered system unites Extreme Gradient Boosting (XGBoost) with Light Gradient Boosting Machine (LightGBM) along with Deep Neural Networks (DNN) as a stacked ensemble design to enable decentralized and secure collaborative learning across the supply chain network and protect user data and maintain system stability as well as network reliability. On the other hand, Synthetic Minority Oversampling Technique (SMOTE) and Principal Component Analysis (PCA) are established techniques, and our contribution is in optimizing the application of those for IIoT traffic. We tackle the class imbalance in intrusion data with SMOTE to better detect rare attacks and to use PCA to reduce the high dimensions of feature space for less computational effort and more efficient pattern recognition. To meet the requirements of the IIoT use cases, these preprocessing techniques are effectively embedded in the framework. Moreover, the proposed modular IDS architecture, the curation and fine tuning of the various learners, and the approach to full validation are all novel. We rigorously evaluate the model under K-Fold Cross Validation using the IoT-23 dataset and prove superior detection performance when compared to state-of-the-art approaches. Specifically, this research contributes a scalable and efficient IDS for an IIoT scenarios such as real-world IIoT enabled SCMS, which improves security analytics and facilitates network defense in key operational functionalities such as low data rates, low computational resources availability and restricted communication over the year.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254828","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}

引用次数: 0