Supply Chain Analytics最新文献

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。

{"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":"2025-12-01","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 machine learning and evolutionary optimization framework for carbon-aware supply chain routing 碳感知供应链路径的机器学习和进化优化框架

Supply Chain Analytics

Pub Date : 2025-11-28 DOI: 10.1016/j.sca.2025.100182

Lorena Sánchez-Pravos , Javier Parra-Domínguez , Sara Rodríguez González , Pablo Chamoso

The increasing urgency of carbon footprint reduction in supply chain operations demands innovative optimization approaches that balance economic efficiency with environmental sustainability. This paper presents a novel carbon-aware route optimization framework that integrates machine learning-based emission prediction with genetic algorithm optimization for sustainable supply chain management. Our hybrid approach combines Random Forest and XGBoost models in an optimized ensemble to predict carbon emissions with high accuracy (MAPE: 9.48%, R

^{2}

: 0.928), while a genetic algorithm optimizes routes considering both cost and carbon constraints. The framework is validated through two complementary scenarios: (1) controlled experiments on synthetic datasets (n=3,500 routes across three network sizes: 500, 1000, and 2000 routes) derived from real-world emission factors demonstrate 19.5% average emission reduction with 4.7% cost increase, and (2) a quasi-real case study on Salamanca regional distribution network (n=12 routes, 776.6 tons CO2e annually) achieves a 41.4% emission reduction with 8.6% cost increase through strategic modal shifts to rail transport. Both scenarios significantly outperform traditional cost-only optimization methods. The proposed approach provides supply chain managers with actionable insights for achieving sustainability goals while maintaining operational efficiency.

供应链运营中碳足迹减少的紧迫性日益增加，需要创新的优化方法来平衡经济效率和环境可持续性。本文提出了一种新的碳感知路径优化框架，该框架将基于机器学习的排放预测与遗传算法优化相结合，用于可持续供应链管理。我们的混合方法将随机森林和XGBoost模型结合在一个优化的集合中，以高精度预测碳排放（MAPE: 9.48%, R2: 0.928），而遗传算法同时考虑成本和碳约束来优化路线。该框架通过两种互补场景进行验证：(1)在合成数据集上进行控制实验（n=3,500条路由，跨越三种网络规模）；(2)以Salamanca区域配送网络为例（n=12条路线，每年776.6吨二氧化碳当量），通过战略方式转向铁路运输，实现了41.4%的减排和8.6%的成本增加。这两种方案都明显优于传统的纯成本优化方法。所提出的方法为供应链管理者提供了可操作的见解，以实现可持续发展目标，同时保持运营效率。

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引用次数: 0

An empirical study on technology adoption and supply chain optimization using structural modeling 基于结构模型的技术采用与供应链优化实证研究

Supply Chain Analytics

Pub Date : 2025-11-25 DOI: 10.1016/j.sca.2025.100181

Ali Mohaghar , Rohollah Ghasemi , Mojtaba Taghipour

This study examines the direct impact of Industry 4.0 on supply chain performance, focusing on the mediating role of coordination and integration. Data were collected via a questionnaire targeting companies active in the Iranian polyethylene supply chain and analyzed using Structural Equation Modeling in the Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS). Coordination and integration partially mediate this relationship and facilitate improved operational efficiency. The polyethylene industry faces significant challenges, including poor upstream-downstream coordination, supply-demand imbalances, and limited production quotas. Industry 4.0 technologies, including the Internet of Things, big data analytics, and automation, offer innovative solutions to these barriers, thereby increasing the resilience and sustainability of the supply chain. The findings show that Industry 4.0 has a significant impact on supply chain performance by enabling real-time data sharing and process optimization. This research demonstrates how adopting advanced Industry 4.0 technologies, such as the Internet of Things, big data analytics, and automation, can specifically enhance supply chain coordination, data transparency, and predictive decision-making. In the Iranian polyethylene industry, these technologies enable real-time monitoring of material flows, enhance collaboration between upstream and downstream partners, and reduce disruptions caused by sanctions and market volatility. The study provides practical implications for Iranian policymakers and managers, including developing digital infrastructure, establishing integrated information platforms, and promoting data-driven strategies to achieve sustainable and resilient supply chain performance.

本研究考察了工业4.0对供应链绩效的直接影响，重点关注协调和整合的中介作用。通过针对活跃在伊朗聚乙烯供应链中的公司的问卷调查收集数据，并使用社会科学统计软件包（SPSS）中的结构方程模型和力矩结构分析（AMOS）进行分析。协调和整合在一定程度上调解了这种关系，并促进了业务效率的提高。聚乙烯行业面临着重大挑战，包括上下游协调不佳、供需失衡和生产配额有限。工业4.0技术，包括物联网、大数据分析和自动化，为这些障碍提供了创新的解决方案，从而提高了供应链的弹性和可持续性。研究结果表明，工业4.0通过实现实时数据共享和流程优化，对供应链绩效产生了重大影响。这项研究展示了如何采用先进的工业4.0技术，如物联网、大数据分析和自动化，可以具体地增强供应链协调、数据透明度和预测性决策。在伊朗的聚乙烯行业，这些技术可以实时监控物料流动，加强上下游合作伙伴之间的合作，减少制裁和市场波动造成的中断。该研究为伊朗的政策制定者和管理者提供了实际意义，包括发展数字基础设施，建立综合信息平台，促进数据驱动战略，以实现可持续和弹性的供应链绩效。

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引用次数: 0

A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains 零售供应链中不确定性预测与适应性库存规划的混合学习框架

Supply Chain Analytics

Pub Date : 2025-11-22 DOI: 10.1016/j.sca.2025.100180

Zizi Mohammed, Chafi Anas, Mohammed El Hammoume

Demand forecasting and quantification of uncertainty is an essential asset of the retail supply chain optimization and risk-based inventory decisions. This study will introduce a new hybrid conditional variance model (combining gradient boosting machines (XGBoost, LightGBM), recurrent neural networks (LSTM-GRU hybrid), and econometric volatility modeling (GARCH) using a stacked ensemble meta-learning method to make retail demand forecasts over multiple horizons. The framework handles important deficiencies of current methods by providing simultaneously high-precision point predictions and probability prediction intervals by conditional estimation of variance. The M5 Walmart benchmark dataset of 8000 high-volume product time series including all features engineered in terms of 58 time, statistic, price and event dimensions are empirically validated. Stacked ensemble architecture has high predictive work at R

^{2}

= 0.9681, root mean squared = 1.48 units and mean absolute error = 0.77 units, which is significantly better than base models. Integrated GARCH(1,1) component effectively explains forecast residual volatility whose mean conditional variance is 2.82 square units, which allows it to construct dynamically adaptive 95% confidence intervals. Forecast shift analysis shows average magnitude of day-to-day revision of 3.21 units with great correlation between the magnitude of the predicted variance and the actual forecast volatility. The proposed framework offers supply chain practitioners actionable probabilistic predictions to aid risk-conscious inventory location and adaptive safety inventory determination, which is a major improvement over traditional point estimation techniques.

需求预测和不确定性量化是零售供应链优化和基于风险的库存决策的重要资产。本研究将引入一种新的混合条件方差模型（结合梯度增强机（XGBoost、LightGBM）、循环神经网络（LSTM-GRU混合）和计量波动模型（GARCH）），使用堆叠集成元学习方法进行多视域零售需求预测。该框架通过方差条件估计同时提供高精度点预测和概率预测区间，解决了现有方法的重要不足。M5沃尔玛基准数据集包含8000个大批量产品时间序列，包括在58个时间、统计、价格和事件维度上设计的所有特征。堆叠集成体系结构具有较高的预测效果，R2= 0.9681，均方根= 1.48单位，平均绝对误差= 0.77单位，显著优于基础模型。综合GARCH（1,1）分量能有效解释平均条件方差为2.82平方单位的预测剩余波动率，构造动态自适应的95%置信区间。预测偏移分析显示，日均修正幅度为3.21个单位，预测方差的大小与实际预测波动率之间存在较大的相关性。提出的框架为供应链从业者提供了可操作的概率预测，以帮助风险意识库存定位和自适应安全库存确定，这是对传统点估计技术的重大改进。

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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 : 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。

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引用次数: 0

An analytical approach to risk assessment in agri-food supply chains using fuzzy inference systems 基于模糊推理系统的农业食品供应链风险评估分析方法

Supply Chain Analytics

Pub Date : 2025-11-19 DOI: 10.1016/j.sca.2025.100179

Madushan Madhava Jayalath , R.M. Chandima Ratnayake , H. Niles Perera , Amila Thibbotuwawa

This study presents a structured, quantitative risk assessment framework for agri-food supply chains (AFSCs), aligned with the guidelines of ISO 31000:2018. The approach integrates Fuzzy Inference Systems (FIS) to quantify and mitigate risks, offering an effective tool to reduce subjectivity, manage uncertainty, and enhance decision-making accuracy. A FIS based risk assessment model was developed using the Probability of Failure (PoF), Consequence of Failure (CoF) and Potential Failure Risk (PFR). Employing the developed FIS models, three disruption scenarios in AFSCs in developing economies were evaluated. The scenarios include: (1) lack of quality farm inputs, (2) lack of logistics infrastructure, and (3) supply-demand mismatches. As per the results, lack of farm inputs results in very high risk in price volatility, high risk in farmer revenue loss and food availability, and moderate risk in post-harvest waste. Logistics inefficiencies are leading to moderate risk in farmer revenue loss while posing low risk in food availability, price volatility, and post-harvest waste. Systemic risks due to supply-demand mismatches result in high risks in price volatility, farmer revenue loss, food availability and post-harvest waste. The proposed risk assessment framework provides the blueprint to develop a risk assessment software for AFSCs in developing economies, which can provide insights on how to combine risk assessment in policy development for supply chain modernisation. Findings of the study suggest that there is a need for a policy-driven systematic approach through market intelligence to manage this volatile supply chain.

本研究提出了一个结构化的、定量的农业食品供应链风险评估框架，与ISO 31000:2018的指导方针保持一致。该方法集成了模糊推理系统（FIS）来量化和降低风险，为减少主观性、管理不确定性和提高决策准确性提供了有效的工具。利用失效概率（PoF）、失效后果（CoF）和潜在失效风险（PFR）建立了基于FIS的风险评估模型。采用已开发的FIS模型，对发展中经济体中afsc的三种中断情景进行了评估。这些情景包括：(1)缺乏优质的农业投入；(2)缺乏物流基础设施；(3)供需不匹配。结果表明，农业投入的缺乏导致价格波动的风险非常高，农民收入损失和粮食供应的风险很高，收获后浪费的风险中等。物流效率低下导致农民收入损失的风险较小，而在粮食供应、价格波动和收获后浪费方面的风险较低。供需错配导致的系统性风险导致价格波动、农民收入损失、粮食供应和收获后浪费的高风险。拟议的风险评估框架为发展中经济体的供应链服务供应商开发风险评估软件提供了蓝图，它可以为如何将风险评估与供应链现代化的政策制定结合起来提供见解。研究结果表明，有必要通过市场情报采取政策驱动的系统方法来管理这一不稳定的供应链。

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引用次数: 0