Supply Chain Analytics最新文献

英文中文

Supply Chain Analytics

Pub Date : 2026-01-01

引用次数: 0

Supply Chain Analytics

Pub Date : 2026-01-01

引用次数: 0

Supply Chain Analytics

Pub Date : 2026-01-01

引用次数: 0

Supply Chain Analytics

Pub Date : 2026-01-01

引用次数: 0

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 : 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。

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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 : 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。

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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 : 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。

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引用次数: 0

An analytical review of vendor-managed inventory models in sustainable supply chains 可持续供应链中供应商管理库存模型的分析综述

Supply Chain Analytics

Pub Date : 2025-12-17 DOI: 10.1016/j.sca.2025.100189

Katherinne Salas-Navarro , Melissa Rojano-Flores , Valentina Salcedo-Villanueva , Leopoldo Eduardo Cárdenas-Barrón

A vendor-managed inventory system is a collaborative strategy in network logistics. It helps establish a sourcing and inventory control policy that optimizes logistics costs and enhances the efficient use of resources. This research presents a meta-analysis and systematic literature review of 334 articles published in well-known peer-reviewed journals between 2014 and 2024. The main objective is to evaluate the importance and recent trends of the vendor-managed inventory strategy in sustainable supply chains. This study utilizes the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to enhance the organization, transparency, and reproducibility of the systematic literature review. The meta-analysis presents a perspective on the principal authors, journals, institutions, countries, and sponsors that develop research and publish on the topic. The systematic literature review categorizes supply chain structures, demand types, shortages, vendor-managed inventory aggregations, and payment policies. Also, imperfect production systems, remanufacturing, product deterioration, inventory-routing challenges, carbon emission regulations, and proposed solutions are included. This study provides an overview of recent developments, applications, industries, supply chains, emerging trends, and future research directions. The main finding is that the vendor-managed inventory approach, applied in sustainable supply chains, improves stock availability, reduces waste of perishables, and yields environmental and sustainability benefits. Also, facilities synchronized decision-making and minimized inefficiencies associated with decentralized control. Future research should aim for greater realism, flexibility, and integration of behavioral and digital dimensions.

供应商管理库存系统是网络物流中的一种协同策略。它有助于建立采购和库存控制政策，优化物流成本，提高资源的有效利用。本研究对2014年至2024年间发表在知名同行评议期刊上的334篇文章进行了meta分析和系统文献综述。主要目的是评估供应商管理库存策略在可持续供应链中的重要性和最新趋势。本研究采用PRISMA（系统评价和荟萃分析的首选报告项目）方法来提高系统文献综述的组织性、透明度和可重复性。荟萃分析展示了主要作者、期刊、机构、国家和赞助者对该主题进行研究和发表的观点。系统的文献综述对供应链结构、需求类型、短缺、供应商管理的库存汇总和支付政策进行了分类。此外，不完善的生产系统、再制造、产品劣化、库存路线挑战、碳排放法规和建议的解决方案也包括在内。本研究概述了最新的发展、应用、产业、供应链、新兴趋势和未来的研究方向。主要发现是，供应商管理的库存方法应用于可持续供应链，提高了库存可用性，减少了易腐品的浪费，并产生了环境和可持续发展效益。此外，设施同步决策和最小化与分散控制相关的低效率。未来的研究应该以更大的现实性、灵活性和行为与数字维度的整合为目标。

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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 : 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。

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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 : 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。

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