Supply Chain Analytics最新文献

An integrated analytics approach to multi-project scheduling and material procurement with coordinated hub location 多项目调度和物料采购的综合分析方法与协调中心位置

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.sca.2025.100191

Sasan Mazaheri , Mahsa Ahmadi , Ali Heidari , Mohammad Hakimi , Mohammad Khalilzadeh

This study presents an integrated framework combining multi-project scheduling, material procurement, and the hub location problem to simultaneously minimize project completion times and overall project and logistics costs. To address the challenge of allocating high-cost renewable resources, we incorporate rental options that balance the trade-off between additional rental expenses and potential project delays. A multi-objective optimization model is developed, integrating the scheduling of multiple projects with coordinated material procurement. To reduce logistics costs and improve delivery efficiency, consolidation hubs are introduced where materials from various suppliers are aggregated before being dispatched to project sites. The model considers the availability of renewable rental resources and storage space capacity while scheduling project activities. Due to the problem's computational complexity, two metaheuristic algorithms NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOSFS (Multi-objective Stochastic Fractal Search) are employed to obtain near-optimal solutions for large-scale scenarios. The proposed approach is validated through a real-world case study involving a bridge construction project and various benchmark instances of different sizes. Results indicate that while NSGA-II performs better on one performance metric, MOSFS consistently outperforms NSGA-II across most criteria, particularly in large-scale problems. The main contributions of this research include integrating project scheduling, material procurement, and hub location within a single unified framework. The model also incorporates renewable rental resources and realistic, type-specific storage capacity constraints that directly affect material flow and the initiation of project activities.

本研究提出了一个综合框架，结合多项目调度、材料采购和枢纽选址问题，同时最小化项目完成时间和整体项目和物流成本。为了解决分配高成本可再生资源的挑战，我们纳入了租赁选项，以平衡额外的租赁费用和潜在的项目延迟之间的权衡。建立了一个多目标优化模型，将多个项目的调度与协调的物资采购相结合。为了降低物流成本和提高交付效率，引入了集结地，将来自不同供应商的材料聚集在一起，然后分发到项目现场。该模型在规划项目活动时考虑了可再生租赁资源的可用性和存储空间容量。考虑到问题的计算复杂度，采用NSGA-II （non - dominant Sorting Genetic Algorithm II）和MOSFS （Multi-objective Stochastic Fractal Search）两种元启发式算法求解大规模场景下的近最优解。通过涉及桥梁建设项目和各种不同规模的基准实例的实际案例研究验证了所提出的方法。结果表明，虽然NSGA-II在一个性能指标上表现更好，但MOSFS在大多数标准上都优于NSGA-II，特别是在大规模问题中。本研究的主要贡献包括将项目调度、材料采购和枢纽位置集成在一个统一的框架内。该模型还包括可再生租赁资源和直接影响物资流动和项目活动启动的实际的、特定类型的储存能力限制。

{"title":"An integrated analytics approach to multi-project scheduling and material procurement with coordinated hub location","authors":"Sasan Mazaheri , Mahsa Ahmadi , Ali Heidari , Mohammad Hakimi , Mohammad Khalilzadeh","doi":"10.1016/j.sca.2025.100191","DOIUrl":"10.1016/j.sca.2025.100191","url":null,"abstract":"<div><div>This study presents an integrated framework combining multi-project scheduling, material procurement, and the hub location problem to simultaneously minimize project completion times and overall project and logistics costs. To address the challenge of allocating high-cost renewable resources, we incorporate rental options that balance the trade-off between additional rental expenses and potential project delays. A multi-objective optimization model is developed, integrating the scheduling of multiple projects with coordinated material procurement. To reduce logistics costs and improve delivery efficiency, consolidation hubs are introduced where materials from various suppliers are aggregated before being dispatched to project sites. The model considers the availability of renewable rental resources and storage space capacity while scheduling project activities. Due to the problem's computational complexity, two metaheuristic algorithms NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOSFS (Multi-objective Stochastic Fractal Search) are employed to obtain near-optimal solutions for large-scale scenarios. The proposed approach is validated through a real-world case study involving a bridge construction project and various benchmark instances of different sizes. Results indicate that while NSGA-II performs better on one performance metric, MOSFS consistently outperforms NSGA-II across most criteria, particularly in large-scale problems. The main contributions of this research include integrating project scheduling, material procurement, and hub location within a single unified framework. The model also incorporates renewable rental resources and realistic, type-specific storage capacity constraints that directly affect material flow and the initiation of project activities.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076801","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-based structural modeling study of circular practices in sustainable supply chains 可持续供应链循环实践的基于分析的结构建模研究

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1016/j.sca.2026.100196

Mahsa Farrokhi , Mauro Gatti , Aram Bahrini

The importance of sustainability has significantly increased for many companies in recent years. However, limited studies have explored the integrated impact of circular economy practices, dynamic capabilities, and fields of action on supply chain sustainability. This study addresses this gap by applying circular economy principles within retail‑equipment supply chains to evaluate their impact on the economic, environmental, and social dimensions of sustainability. A comprehensive review of the relevant literature on circular economy, dynamic capabilities, fields of action, and sustainable supply chains facilitated the identification of 51 key indicators. To fulfill the research objectives, a retail-equipping manufacturing company was selected as the focal point of analysis, and data was collected through a structured questionnaire. We analyzed the data via partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, estimating a reflective–reflective hierarchical component model on n = 131 responses to a 51-item instrument covering circular economy fields, dynamic capabilities, and sustainability outcomes. Results support seven hypotheses: both fields of action (H1–H3) and dynamic capabilities (H4–H6) positively affect economic, environmental, and social performance, and their joint effect is strong (H7: β = 0.73; R² = 0.54). The study presents an integrated, empirically validated model that links circular economy dynamic capabilities and fields of action, thereby extending dynamic capabilities theory into the sustainability domain. The findings suggest that circular economy principles not only support environmental protection and cost efficiency but also enhance organizational agility, stakeholder satisfaction, and resilience. While the specific outcomes may vary across industries, this study offers a robust foundation for future research and practical insights to guide strategic decision-making for companies aiming to transition to more sustainable, circular supply chains. The model provides actionable diagnostics to prioritize investments in design, reverse logistics, and capability development aligned with triple-bottom-line goals in manufacturing.

近年来，对许多公司来说，可持续性的重要性显著增加。然而，有限的研究探讨了循环经济实践、动态能力和行动领域对供应链可持续性的综合影响。本研究通过在零售设备供应链中应用循环经济原则来评估其对可持续发展的经济、环境和社会层面的影响，从而解决了这一差距。通过对循环经济、动态能力、行动领域和可持续供应链相关文献的全面回顾，确定了51个关键指标。为了完成研究目标，选取一家零售设备制造公司作为分析重点，并通过结构化问卷收集数据。我们通过SmartPLS 3中的偏最小二乘结构方程模型（PLS-SEM）分析了数据，估计了对51项涵盖循环经济领域、动态能力和可持续性结果的工具的n = 131个响应的反射-反射分层成分模型。结果支持7个假设：行动场（H1-H3）和动态能力（H4-H6）对经济、环境和社会绩效均有正向影响，且两者的联合效应较强（H7: β = 0.73; R2 = 0.54）。本研究提出了一个综合的、经验验证的模型，将循环经济动态能力与行动领域联系起来，从而将动态能力理论扩展到可持续性领域。研究结果表明，循环经济原则不仅支持环境保护和成本效率，而且还提高了组织敏捷性、利益相关者满意度和弹性。虽然具体结果可能因行业而异，但本研究为未来的研究提供了坚实的基础，并为旨在向更可持续的循环供应链过渡的公司提供了实用的见解，以指导战略决策。该模型提供了可操作的诊断，以优先考虑设计、逆向物流和能力开发方面的投资，并与制造业的三重底线目标保持一致。

{"title":"An analytics-based structural modeling study of circular practices in sustainable supply chains","authors":"Mahsa Farrokhi , Mauro Gatti , Aram Bahrini","doi":"10.1016/j.sca.2026.100196","DOIUrl":"10.1016/j.sca.2026.100196","url":null,"abstract":"<div><div>The importance of sustainability has significantly increased for many companies in recent years. However, limited studies have explored the integrated impact of circular economy practices, dynamic capabilities, and fields of action on supply chain sustainability. This study addresses this gap by applying circular economy principles within retail‑equipment supply chains to evaluate their impact on the economic, environmental, and social dimensions of sustainability. A comprehensive review of the relevant literature on circular economy, dynamic capabilities, fields of action, and sustainable supply chains facilitated the identification of 51 key indicators. To fulfill the research objectives, a retail-equipping manufacturing company was selected as the focal point of analysis, and data was collected through a structured questionnaire. We analyzed the data via partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, estimating a reflective–reflective hierarchical component model on n = 131 responses to a 51-item instrument covering circular economy fields, dynamic capabilities, and sustainability outcomes. Results support seven hypotheses: both fields of action (H1–H3) and dynamic capabilities (H4–H6) positively affect economic, environmental, and social performance, and their joint effect is strong (H7: β = 0.73; R<sup>2</sup> = 0.54). The study presents an integrated, empirically validated model that links circular economy dynamic capabilities and fields of action, thereby extending dynamic capabilities theory into the sustainability domain. The findings suggest that circular economy principles not only support environmental protection and cost efficiency but also enhance organizational agility, stakeholder satisfaction, and resilience. While the specific outcomes may vary across industries, this study offers a robust foundation for future research and practical insights to guide strategic decision-making for companies aiming to transition to more sustainable, circular supply chains. The model provides actionable diagnostics to prioritize investments in design, reverse logistics, and capability development aligned with triple-bottom-line goals in manufacturing.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076800","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 metaheuristic approach to supply chain inventory optimization with rebates, discounts, and emission controls 具有返利、折扣和排放控制的供应链库存优化的元启发式方法

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-01 DOI: 10.1016/j.sca.2025.100183

Ankur Saurav, Chandra Shekhar, Vijender Yadav

This study develops a sustainable production–inventory model that integrates advance booking, rebate incentives, and green investments under carbon cap-and-trade regulations, aiming to optimize profit while ensuring environmental sustainability. The model accounts for time- and price-sensitive demand influenced by discounts and advertising, and organizes the inventory cycle into four phases: advance booking before production, production with ongoing bookings, normal sales, and a deterioration phase supported by rebate strategies. The study introduces two key innovations: a dual-stage advance booking system with associated maintenance costs and deterioration control via preservation technologies. Carbon emissions arising from production, holding, transportation, and deterioration are compared to a regulatory cap, with penalties for exceeding the limit and credit trading for staying within it. Green technology investments further support emission reduction. The model’s nonlinear optimization problem is solved using the Grey Wolf Optimization algorithm to determine optimal pricing, production quantities, investment levels, and cycle times. Numerical results highlight that integrating sustainability measures, smart pricing strategies, and customer incentives enhances profitability while minimizing environmental impact. This model provides valuable insights for firms and policymakers aiming to align operational efficiency with sustainability objectives under regulatory frameworks.

本研究建立了一个可持续的生产-库存模型，该模型在碳总量控制与交易规则下整合了提前预订、返利激励和绿色投资，旨在优化利润的同时确保环境的可持续性。该模型考虑了受折扣和广告影响的对时间和价格敏感的需求，并将库存周期组织为四个阶段：生产前的提前预订、持续预订的生产、正常销售和由折扣策略支持的恶化阶段。该研究引入了两个关键创新：两阶段提前预订系统，与维护成本相关，以及通过保存技术控制变质。将生产、持有、运输和变质过程中产生的碳排放与监管上限进行比较，超过上限将受到处罚，不超过上限则进行信用交易。绿色技术投资进一步支持减排。利用灰狼优化算法求解模型的非线性优化问题，确定最优定价、生产数量、投资水平和周期时间。数值结果强调，整合可持续性措施、智能定价策略和客户激励可以提高盈利能力，同时最大限度地减少对环境的影响。该模型为企业和决策者提供了有价值的见解，旨在使运营效率与监管框架下的可持续性目标保持一致。

{"title":"A metaheuristic approach to supply chain inventory optimization with rebates, discounts, and emission controls","authors":"Ankur Saurav, Chandra Shekhar, Vijender Yadav","doi":"10.1016/j.sca.2025.100183","DOIUrl":"10.1016/j.sca.2025.100183","url":null,"abstract":"<div><div>This study develops a sustainable production–inventory model that integrates advance booking, rebate incentives, and green investments under carbon cap-and-trade regulations, aiming to optimize profit while ensuring environmental sustainability. The model accounts for time- and price-sensitive demand influenced by discounts and advertising, and organizes the inventory cycle into four phases: advance booking before production, production with ongoing bookings, normal sales, and a deterioration phase supported by rebate strategies. The study introduces two key innovations: a dual-stage advance booking system with associated maintenance costs and deterioration control via preservation technologies. Carbon emissions arising from production, holding, transportation, and deterioration are compared to a regulatory cap, with penalties for exceeding the limit and credit trading for staying within it. Green technology investments further support emission reduction. The model’s nonlinear optimization problem is solved using the Grey Wolf Optimization algorithm to determine optimal pricing, production quantities, investment levels, and cycle times. Numerical results highlight that integrating sustainability measures, smart pricing strategies, and customer incentives enhances profitability while minimizing environmental impact. This model provides valuable insights for firms and policymakers aiming to align operational efficiency with sustainability objectives under regulatory frameworks.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694208","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 risk-averse multi-objective analytics framework for green supply chain design under uncertainty 不确定性下绿色供应链设计的风险规避多目标分析框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.sca.2026.100199

Shahryar Ghorbani , Javad Nematian , Figen Yıldırım , Selahattin Armağan Vurdu

In response to increasing concerns about environmental issues, businesses, and industries face pressure to mitigate their negative environmental impacts. Consequently, firms must reevaluate their operations to align with environmental standards. To address both economic and environmental objectives, industries need to green their supply chains. However, uncertainties in the real world, such as economic instability, add complexity to this greening process. This study proposes a novel risk-averse two-stage stochastic model for green supply chain (GSC) design under uncertainty, integrating Conditional Value at Risk (CVaR) with multi-objective programming. The model uses discrete Fuzzy Random Variables (FRVs) to capture both randomness and fuzziness in cost and emission parameters. To solve the model, we apply possibility theory and Fuzzy Chance-Constrained Programming (FCCP) to derive deterministic equivalents for optimistic, pessimistic, and hybrid decision-making attitudes. Numerical results from a flour supply chain case in Iran show that higher risk aversion increases both cost and CO₂ CVaR, while possibility levels affect outcomes differently across models. The approach provides managers with a flexible tool for balancing economic and environmental goals under uncertainty.

随着人们对环境问题的日益关注，企业和工业面临着减轻其负面环境影响的压力。因此，企业必须重新评估其业务以符合环境标准。为了实现经济和环境两方面的目标，行业需要实现供应链的绿色化。然而，现实世界中的不确定性，如经济不稳定，增加了这一绿化过程的复杂性。将条件风险值（CVaR）与多目标规划相结合，提出了不确定条件下绿色供应链设计的风险规避两阶段随机模型。该模型采用离散模糊随机变量（frv）来捕捉成本和排放参数的随机性和模糊性。为了解决这个模型，我们应用可能性理论和模糊机会约束规划（FCCP）来推导乐观、悲观和混合决策态度的确定性等价。伊朗面粉供应链案例的数值结果表明，较高的风险厌恶会增加成本和CO₂CVaR，而不同模型的可能性水平对结果的影响不同。该方法为管理人员提供了一种灵活的工具，可以在不确定的情况下平衡经济和环境目标。

{"title":"A risk-averse multi-objective analytics framework for green supply chain design under uncertainty","authors":"Shahryar Ghorbani , Javad Nematian , Figen Yıldırım , Selahattin Armağan Vurdu","doi":"10.1016/j.sca.2026.100199","DOIUrl":"10.1016/j.sca.2026.100199","url":null,"abstract":"<div><div>In response to increasing concerns about environmental issues, businesses, and industries face pressure to mitigate their negative environmental impacts. Consequently, firms must reevaluate their operations to align with environmental standards. To address both economic and environmental objectives, industries need to green their supply chains. However, uncertainties in the real world, such as economic instability, add complexity to this greening process. This study proposes a novel risk-averse two-stage stochastic model for green supply chain (GSC) design under uncertainty, integrating Conditional Value at Risk (CVaR) with multi-objective programming. The model uses discrete Fuzzy Random Variables (FRVs) to capture both randomness and fuzziness in cost and emission parameters. To solve the model, we apply possibility theory and Fuzzy Chance-Constrained Programming (FCCP) to derive deterministic equivalents for optimistic, pessimistic, and hybrid decision-making attitudes. Numerical results from a flour supply chain case in Iran show that higher risk aversion increases both cost and CO₂ CVaR, while possibility levels affect outcomes differently across models. The approach provides managers with a flexible tool for balancing economic and environmental goals under uncertainty.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394676","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 hybrid method for multi-item demand forecasting in supply chains 供应链中多项目需求预测的分析驱动混合方法

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.sca.2026.100194

Md. Limonur Rahman Lingkon , Md. Sanowar Hossain , Ripon K. Chakrabortty

This study proposes a new deep learning (DL)–based approach for multi-item demand forecasting in multi-wave distribution networks. In modern merchandising supply systems, traditional forecasting techniques such as moving averages and autoregressive integrated moving average (ARIMA) models are often inadequate for capturing the dynamic nature of sales and operations planning, as they struggle with non-stationary demand, evolving market conditions, and the growing complexity of supply chain networks, resulting in forecasts that are neither sufficiently accurate nor timely. To address these limitations, this study evaluates a hybrid forecasting framework that combines poly-linear regression (PLR) with a transformer-encoder extended long short-term memory (TE-LSTM) architecture to identify latent demand patterns from large and heterogeneous datasets. An empirical analysis compares the proposed PLR–TE–LSTM model with baseline approaches such as standard LSTM across multiple products and distribution locations, demonstrating consistently superior forecasting performance in multi-product, multi-distribution center settings. The study further examines the operational impact of improved forecasting by evaluating alternative inventory review strategies under a fixed-quantity replenishment policy, showing meaningful improvements in forecast accuracy, order fulfillment performance, inventory holding costs, and service levels. The results indicate that the proposed DL framework enhances inventory-related decision-making by reducing excess inventory and stockout risks, thereby improving efficiency and responsiveness in complex distribution networks.

本文提出了一种基于深度学习的多波配电网多项目需求预测方法。在现代销售供应系统中，传统的预测技术，如移动平均线和自回归综合移动平均线（ARIMA）模型，往往不足以捕捉销售和运营计划的动态特性，因为它们与非平稳的需求、不断变化的市场条件和日益复杂的供应链网络作斗争，导致预测既不够准确也不够及时。为了解决这些限制，本研究评估了一种混合预测框架，该框架将多线性回归（PLR）与变压器-编码器扩展长短期记忆（TE-LSTM）架构相结合，以从大型异构数据集中识别潜在的需求模式。一项实证分析将提出的PLR-TE-LSTM模型与基准方法（如跨多个产品和分销地点的标准LSTM）进行了比较，证明了在多产品、多分销中心设置下始终具有卓越的预测性能。该研究通过评估固定数量补充政策下的替代库存审查策略，进一步检查改进预测的业务影响，显示在预测准确性、订单履行绩效、库存持有成本和服务水平方面有意义的改进。结果表明，提出的DL框架通过减少库存过剩和缺货风险来增强库存相关决策，从而提高复杂分销网络的效率和响应能力。

{"title":"An analytics-driven hybrid method for multi-item demand forecasting in supply chains","authors":"Md. Limonur Rahman Lingkon , Md. Sanowar Hossain , Ripon K. Chakrabortty","doi":"10.1016/j.sca.2026.100194","DOIUrl":"10.1016/j.sca.2026.100194","url":null,"abstract":"<div><div>This study proposes a new deep learning (DL)–based approach for multi-item demand forecasting in multi-wave distribution networks. In modern merchandising supply systems, traditional forecasting techniques such as moving averages and autoregressive integrated moving average (ARIMA) models are often inadequate for capturing the dynamic nature of sales and operations planning, as they struggle with non-stationary demand, evolving market conditions, and the growing complexity of supply chain networks, resulting in forecasts that are neither sufficiently accurate nor timely. To address these limitations, this study evaluates a hybrid forecasting framework that combines poly-linear regression (PLR) with a transformer-encoder extended long short-term memory (TE-LSTM) architecture to identify latent demand patterns from large and heterogeneous datasets. An empirical analysis compares the proposed PLR–TE–LSTM model with baseline approaches such as standard LSTM across multiple products and distribution locations, demonstrating consistently superior forecasting performance in multi-product, multi-distribution center settings. The study further examines the operational impact of improved forecasting by evaluating alternative inventory review strategies under a fixed-quantity replenishment policy, showing meaningful improvements in forecast accuracy, order fulfillment performance, inventory holding costs, and service levels. The results indicate that the proposed DL framework enhances inventory-related decision-making by reducing excess inventory and stockout risks, thereby improving efficiency and responsiveness in complex distribution networks.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037467","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 systematic analysis of generative artificial intelligence for supply chain transformation 供应链转型中生成式人工智能的系统分析

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1016/j.sca.2025.100188

Zied Bahroun , Afef Saihi , Rami As’ad , Moayad Tanash

Global supply chains face persistent disruptions from geopolitical shocks, sustainability pressures, and volatile demand, creating an increasing need for resilient and transparent operations. Generative Artificial Intelligence (GAI), including Large Language Models (LLMs), Generative Adversarial Networks (GANs), and multimodal generative systems, is emerging as a new decision layer that can generate scenarios, synthetic data, and actionable textual insights rather than only point predictions. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review analyzes 98 peer-reviewed studies on GAI applications in Supply Chain Management (SCM) and, to the best of the authors’ knowledge, provides the first combined thematic and Supply Chain Operations Reference (SCOR) model-based mapping of these applications. Publication activity shows a sharp upward trend, with fewer than five papers published before 2021 and 45 published in 2024 alone. Nearly four-fifths of the reported applications focus on the Plan and Enable processes, while the Make and Return processes account for only 4 % and 1 % of the coded functions, respectively. Although LLM- and Generative Pre-trained Transformer (GPT)-based models underpin over 40 % of the implementations, approximately 45 % of the studies do not fully specify their underlying architectures, indicating methodological immaturity. Reported benefits are concentrated in demand forecasting and risk analysis, supplier screening, logistics visibility, and sustainability analytics; however, most evidence remains at the prototype level and rarely reports system-wide Key Performance Indicators (KPIs). The review concludes with a targeted research agenda that emphasizes longitudinal evaluation, hybrid GAI-driven optimization with digital twin architectures, and governance-by-design frameworks to support the responsible and scalable adoption of GAI in supply chains.

全球供应链面临地缘政治冲击、可持续性压力和需求波动带来的持续中断，因此对弹性和透明运营的需求日益增加。生成式人工智能（GAI），包括大型语言模型（llm）、生成式对抗网络（gan）和多模态生成系统，正在作为一个新的决策层出现，它可以生成场景、合成数据和可操作的文本洞察，而不仅仅是点预测。这个首选报告项目系统审查和荟萃分析（PRISMA）指导的系统审查分析了98个同行评议的供应链管理（SCM）中GAI应用的研究，并且，据作者所知，提供了这些应用的第一个结合主题和供应链操作参考（SCOR）模型的映射。发表活动呈急剧上升趋势，2021年之前发表的论文不足5篇，仅2024年就有45篇。将近五分之四的报告应用程序集中在计划和启用过程上，而制造和返回过程分别只占编码功能的4% %和1% %。尽管基于LLM和生成预训练转换器（GPT）的模型支撑了超过40% %的实现，但是大约45% %的研究并没有完全指定它们的底层架构，这表明了方法论的不成熟。报告的好处集中在需求预测和风险分析，供应商筛选，物流可见性和可持续性分析；然而，大多数证据仍然停留在原型水平，很少报告全系统的关键绩效指标（kpi）。该报告总结了一项有针对性的研究议程，强调纵向评估、混合ai驱动优化与数字孪生架构，以及设计治理框架，以支持供应链中负责任和可扩展地采用GAI。

{"title":"A systematic analysis of generative artificial intelligence for supply chain transformation","authors":"Zied Bahroun , Afef Saihi , Rami As’ad , Moayad Tanash","doi":"10.1016/j.sca.2025.100188","DOIUrl":"10.1016/j.sca.2025.100188","url":null,"abstract":"<div><div>Global supply chains face persistent disruptions from geopolitical shocks, sustainability pressures, and volatile demand, creating an increasing need for resilient and transparent operations. Generative Artificial Intelligence (GAI), including Large Language Models (LLMs), Generative Adversarial Networks (GANs), and multimodal generative systems, is emerging as a new decision layer that can generate scenarios, synthetic data, and actionable textual insights rather than only point predictions. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review analyzes 98 peer-reviewed studies on GAI applications in Supply Chain Management (SCM) and, to the best of the authors’ knowledge, provides the first combined thematic and Supply Chain Operations Reference (SCOR) model-based mapping of these applications. Publication activity shows a sharp upward trend, with fewer than five papers published before 2021 and 45 published in 2024 alone. Nearly four-fifths of the reported applications focus on the Plan and Enable processes, while the Make and Return processes account for only 4 % and 1 % of the coded functions, respectively. Although LLM- and Generative Pre-trained Transformer (GPT)-based models underpin over 40 % of the implementations, approximately 45 % of the studies do not fully specify their underlying architectures, indicating methodological immaturity. Reported benefits are concentrated in demand forecasting and risk analysis, supplier screening, logistics visibility, and sustainability analytics; however, most evidence remains at the prototype level and rarely reports system-wide Key Performance Indicators (KPIs). The review concludes with a targeted research agenda that emphasizes longitudinal evaluation, hybrid GAI-driven optimization with digital twin architectures, and governance-by-design frameworks to support the responsible and scalable adoption of GAI in supply chains.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799697","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 enhancing hospital pharmacy supply chain performance using fuzzy rough set theory 基于模糊粗糙集理论的医院药房供应链绩效提升分析框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.sca.2025.100187

Detcharat Sumrit, Sudarat Katthamaruesee

The pharmaceutical supply chain is vital to hospital operations but faces persistent challenges, including drug shortages, regulatory constraints, and inventory inefficiencies. This study explores the application of the Triple-A Supply Chain (TASC) framework agility, adaptability, and alignment to enhance hospital pharmacy performance. A novel hybrid multi-criteria decision-making (MCDM) model is proposed, integrating the Ordinal Priority Approach (OPA) and Aczel–Alsina Weighted Assessment (ALWAS), supported by fuzzy rough set (FRS) theory. This approach improves the reliability of expert judgment under uncertainty, addressing limitations of traditional deterministic models. OPA results identify “data visibility” (agility), “policy and regulatory alignment” (alignment), and “contingency planning” (adaptability) as the most influential TASC criteria. ALWAS analysis highlights “patient-centric inventory coverage,” “stockout frequency of high-priority medications,” and the “critical drug stock availability index” as the most significantly impacted performance indicators. These findings underscore the importance of transparent information flows, regulatory coherence, and resilience planning in achieving responsive and reliable pharmacy operations. Theoretically, the study bridges the resource-based view (RBV) and dynamic capabilities (DC), positioning TASC dimensions as strategic intangible assets that foster adaptability and competitive advantage in uncertain environments. Managerially, the results offer actionable insights for hospital leaders to enhance agility, embed contingency protocols, and align operations with institutional and regulatory priorities. The integration of advanced decision-making tools with strategic supply chain principles provides a comprehensive framework for performance improvement. Beyond hospital pharmacies, the proposed framework offers conceptual and practical value, with potential applications in broader healthcare contexts such as vaccine logistics, emergency preparedness, and digital health systems.

药品供应链对医院运营至关重要，但面临着持续的挑战，包括药品短缺、监管限制和库存效率低下。本研究探讨了应用aaa供应链框架的敏捷性、适应性和一致性来提升医院药房绩效。在模糊粗糙集理论的支持下，将排序优先法（OPA）与Aczel-Alsina加权评价法（ALWAS）相结合，提出了一种新的混合多准则决策模型。该方法提高了不确定性下专家判断的可靠性，解决了传统确定性模型的局限性。OPA结果将“数据可见性”（敏捷性）、“政策和法规一致性”（一致性）和“应急计划”（适应性）确定为最具影响力的TASC标准。ALWAS分析强调“以患者为中心的库存覆盖率”、“高优先级药物的缺货频率”和“关键药物库存可用性指数”是影响绩效最显著的指标。这些发现强调了透明的信息流、监管一致性和弹性规划对于实现响应性和可靠性药房运营的重要性。理论上，本研究将资源基础观（RBV）和动态能力观（DC）结合起来，将TASC维度定位为在不确定环境中培养适应性和竞争优势的战略性无形资产。在管理方面，结果为医院领导者提供了可操作的见解，以提高敏捷性，嵌入应急协议，并使运营与机构和监管优先事项保持一致。先进的决策工具与战略供应链原则的整合为绩效改进提供了一个全面的框架。除了医院药房之外，拟议的框架还具有概念和实践价值，在疫苗物流、应急准备和数字卫生系统等更广泛的卫生保健环境中具有潜在的应用价值。

{"title":"An analytical framework for enhancing hospital pharmacy supply chain performance using fuzzy rough set theory","authors":"Detcharat Sumrit, Sudarat Katthamaruesee","doi":"10.1016/j.sca.2025.100187","DOIUrl":"10.1016/j.sca.2025.100187","url":null,"abstract":"<div><div>The pharmaceutical supply chain is vital to hospital operations but faces persistent challenges, including drug shortages, regulatory constraints, and inventory inefficiencies. This study explores the application of the Triple-A Supply Chain (TASC) framework agility, adaptability, and alignment to enhance hospital pharmacy performance. A novel hybrid multi-criteria decision-making (MCDM) model is proposed, integrating the Ordinal Priority Approach (OPA) and Aczel–Alsina Weighted Assessment (ALWAS), supported by fuzzy rough set (FRS) theory. This approach improves the reliability of expert judgment under uncertainty, addressing limitations of traditional deterministic models. OPA results identify “data visibility” (agility), “policy and regulatory alignment” (alignment), and “contingency planning” (adaptability) as the most influential TASC criteria. ALWAS analysis highlights “patient-centric inventory coverage,” “stockout frequency of high-priority medications,” and the “critical drug stock availability index” as the most significantly impacted performance indicators. These findings underscore the importance of transparent information flows, regulatory coherence, and resilience planning in achieving responsive and reliable pharmacy operations. Theoretically, the study bridges the resource-based view (RBV) and dynamic capabilities (DC), positioning TASC dimensions as strategic intangible assets that foster adaptability and competitive advantage in uncertain environments. Managerially, the results offer actionable insights for hospital leaders to enhance agility, embed contingency protocols, and align operations with institutional and regulatory priorities. The integration of advanced decision-making tools with strategic supply chain principles provides a comprehensive framework for performance improvement. Beyond hospital pharmacies, the proposed framework offers conceptual and practical value, with potential applications in broader healthcare contexts such as vaccine logistics, emergency preparedness, and digital health systems.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938583","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 Bayesian learning approach for predictive resilience in engineer-to-order supply chains 工程师到订单供应链中预测弹性的贝叶斯学习方法

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.sca.2025.100190

Aicha Alaoua , Mohammed Karim

Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things (IoT)-driven adaptation, and Bayesian Neural Network (BNN) updating to conceptually investigate predictive resilience. Using industry-informed synthetic data that reflect realistic variability in lead times and operational disruptions, the framework is demonstrated through Monte Carlo simulation conducted across sixteen parameter scenarios under both moderate and high variability conditions, providing a proof-of-concept tool that illustrates potential operational benefits in EtO supply chains and establishes a foundation for future empirical validation. Results show that the baseline log-normal model performs adequately in stable conditions, its accuracy deteriorates under parameter shifts, and the IoT-adjusted framework reduces sensitivity to variability, while the BNN-enhanced model further improves robustness by jointly modeling aleatoric and epistemic uncertainty. The approach advances supply chain analytics by integrating statistical modeling, real-time IoT feedback, and Bayesian learning, offering theoretical insights and simulation-based, conceptual decision-support implications for supplier management and risk analysis.

准确的供应商交货期预测对于保持工程师到订单（EtO）供应链的弹性至关重要，其特点是高度定制和不确定性。本研究开发了一个基于模拟的预测框架，结合对数正态灵敏度分析、物联网（IoT）驱动的适应和贝叶斯神经网络（BNN）更新，从概念上研究预测弹性。利用反映交货时间和运营中断的实际可变性的行业信息合成数据，通过蒙特卡罗模拟在中等和高可变性条件下的16个参数场景中进行了该框架的演示，提供了一个概念验证工具，说明了EtO供应链的潜在运营效益，并为未来的经验验证奠定了基础。结果表明，基线对数正态模型在稳定条件下表现良好，但在参数变化下准确性下降，物联网调整框架降低了对可变性的敏感性，而bnn增强模型通过联合建模任意不确定性和认知不确定性，进一步提高了鲁棒性。该方法通过集成统计建模、实时物联网反馈和贝叶斯学习来推进供应链分析，为供应商管理和风险分析提供理论见解和基于仿真的概念性决策支持。

{"title":"A Bayesian learning approach for predictive resilience in engineer-to-order supply chains","authors":"Aicha Alaoua , Mohammed Karim","doi":"10.1016/j.sca.2025.100190","DOIUrl":"10.1016/j.sca.2025.100190","url":null,"abstract":"<div><div>Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things (IoT)-driven adaptation, and Bayesian Neural Network (BNN) updating to conceptually investigate predictive resilience. Using industry-informed synthetic data that reflect realistic variability in lead times and operational disruptions, the framework is demonstrated through Monte Carlo simulation conducted across sixteen parameter scenarios under both moderate and high variability conditions, providing a proof-of-concept tool that illustrates potential operational benefits in EtO supply chains and establishes a foundation for future empirical validation. Results show that the baseline log-normal model performs adequately in stable conditions, its accuracy deteriorates under parameter shifts, and the IoT-adjusted framework reduces sensitivity to variability, while the BNN-enhanced model further improves robustness by jointly modeling aleatoric and epistemic uncertainty. The approach advances supply chain analytics by integrating statistical modeling, real-time IoT feedback, and Bayesian learning, offering theoretical insights and simulation-based, conceptual decision-support implications for supplier management and risk analysis.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938584","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 interpretive analysis of influential drivers for control tower adoption in supply chains 对供应链中控制塔采用的影响因素的解释性分析

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2025-11-20 DOI: 10.1016/j.sca.2025.100178

Magesh kumar M. Nadar , Angappa Gunasekaran , Vaibhav S. Narwane

A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance.

供应链控制塔（SCCT）为供应链管理提供实时信息、分析和决策支持，帮助组织在中断和效率低下发生之前对其进行管理。现代供应链的复杂性以各种影响因素为特征，这些因素显著影响着供应链控制塔（SCCT）的性能。解释这些因素之间的相互作用是供应链管理者努力提高决策质量和绩效的关键。因子分析用于识别、确定优先级，并对有助于实现SCCT有效性的有影响的成功因素进行排序。本研究通过应用总解释结构模型（Total Interpretive Structural Modeling， TISM）来评估SCCT采用的影响因素，并通过MICMAC （matrix d 'Impacts croissamas Multiplication appliqusame Classement）分析来确定它们的相对重要性。结果表明，供应链可见度和透明度是影响供应链绩效的主要因素，而可持续增长战略是影响供应链绩效的次要因素。本研究为供应链管理者提供了宝贵的实践理解，帮助他们加快工作速度，有效地应用SCCT，最终提高供应链绩效。

{"title":"An interpretive analysis of influential drivers for control tower adoption in supply chains","authors":"Magesh kumar M. Nadar , Angappa Gunasekaran , Vaibhav S. Narwane","doi":"10.1016/j.sca.2025.100178","DOIUrl":"10.1016/j.sca.2025.100178","url":null,"abstract":"<div><div>A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584400","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 data-driven and cognitive analytics framework for sustainable supply chain transformation in industry 6.0 工业6.0中可持续供应链转型的数据驱动和认知分析框架

Supply Chain Analytics

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.sca.2026.100197

Andrés Fernández-Miguel , Susana Ortíz-Marcos , Mariano Jiménez-Calzado , Alfonso P. Fernández del Hoyo , Fernando García-Muiña , Davide Settembre-Blundo

The transition from data-driven to cognitively adaptive supply chains represent a critical step toward Industry 6.0, where learning, coordination, and sustainability must be addressed jointly. Existing supply chain analytics approaches remain limited in capturing adaptive and systemic behaviors under uncertainty, particularly in resource- and energy-intensive industrial contexts. This study proposes a Cognitive and Data-Driven Framework for Supply Chains based on federated learning and synthetic data simulations grounded in aggregated industrial benchmarks. The framework introduces the Adaptivity Coefficient (A_c), a composite metric integrating learning velocity, anticipatory responsiveness, and technological exposure to quantify cognitive readiness at the network level. Results from simulation experiments show that cognitively adaptive supply chains achieve significant performance improvements compared to conventional predictive approaches. Specifically, cognitive coordination reduces cumulative disruption costs by 18–25 %, lowers emissions intensity by up to 15 %, and shortens recovery time by approximately 27 %. The analysis further demonstrates that adaptive learning expands the Pareto-efficient frontier, enabling simultaneous gains in cost efficiency, resilience, and environmental performance under varying levels of uncertainty. These findings suggest that cognitive adaptivity functions as a strategic capability rather than a purely technical feature. The study concludes by highlighting the managerial and policy implications of embedding cognitive learning into supply chain governance and by outlining pathways for future empirical validation in hard-to-abate manufacturing sectors.

从数据驱动到认知适应性供应链的转变是迈向工业6.0的关键一步，在工业6.0中，学习、协调和可持续性必须共同解决。现有的供应链分析方法在捕捉不确定性下的适应性和系统性行为方面仍然有限，特别是在资源和能源密集型工业环境中。本研究提出了一个基于联合学习和综合工业基准的综合数据模拟的供应链认知和数据驱动框架。该框架引入了适应性系数（Ac），这是一个综合学习速度、预期反应和技术暴露的复合度量，用于量化网络层面的认知准备。仿真实验结果表明，与传统的预测方法相比，认知自适应供应链实现了显著的性能改进。具体而言，认知协调可将累积破坏成本降低18-25 %，将排放强度降低15 %，并将恢复时间缩短约27 %。分析进一步表明，适应性学习扩展了帕累托效率边界，使成本效率、弹性和环境绩效在不同不确定性水平下同时获得收益。这些发现表明，认知适应性是一种战略能力，而不是纯粹的技术特征。该研究最后强调了将认知学习嵌入供应链治理的管理和政策含义，并概述了未来在难以减弱的制造业中进行实证验证的途径。

{"title":"A data-driven and cognitive analytics framework for sustainable supply chain transformation in industry 6.0","authors":"Andrés Fernández-Miguel , Susana Ortíz-Marcos , Mariano Jiménez-Calzado , Alfonso P. Fernández del Hoyo , Fernando García-Muiña , Davide Settembre-Blundo","doi":"10.1016/j.sca.2026.100197","DOIUrl":"10.1016/j.sca.2026.100197","url":null,"abstract":"<div><div>The transition from data-driven to cognitively adaptive supply chains represent a critical step toward Industry 6.0, where learning, coordination, and sustainability must be addressed jointly. Existing supply chain analytics approaches remain limited in capturing adaptive and systemic behaviors under uncertainty, particularly in resource- and energy-intensive industrial contexts. This study proposes a Cognitive and Data-Driven Framework for Supply Chains based on federated learning and synthetic data simulations grounded in aggregated industrial benchmarks. The framework introduces the Adaptivity Coefficient (A<sub>c</sub>), a composite metric integrating learning velocity, anticipatory responsiveness, and technological exposure to quantify cognitive readiness at the network level. Results from simulation experiments show that cognitively adaptive supply chains achieve significant performance improvements compared to conventional predictive approaches. Specifically, cognitive coordination reduces cumulative disruption costs by 18–25 %, lowers emissions intensity by up to 15 %, and shortens recovery time by approximately 27 %. The analysis further demonstrates that adaptive learning expands the Pareto-efficient frontier, enabling simultaneous gains in cost efficiency, resilience, and environmental performance under varying levels of uncertainty. These findings suggest that cognitive adaptivity functions as a strategic capability rather than a purely technical feature. The study concludes by highlighting the managerial and policy implications of embedding cognitive learning into supply chain governance and by outlining pathways for future empirical validation in hard-to-abate manufacturing sectors.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394470","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