Supply Chain Analytics最新文献_第6页

An inventory optimization model for reliable and sustainable supply chains under trade credit and carbon constraints 贸易信用和碳约束下可靠可持续供应链的库存优化模型

Supply Chain Analytics

Pub Date : 2025-06-23 DOI: 10.1016/j.sca.2025.100132

Ankur Saurav, Vijender Yadav, Chandra Shekhar

The adoption of environmentally sound supply chain management strategies is gaining prominence in response to increasing sustainability efforts in emerging economies. As global environmental degradation escalates, industries are compelled to replace conventional products with green and reliable alternatives. This research introduces a dual-objective strategy aimed at minimizing operational costs while ensuring environmental preservation, focusing on identifying optimal approaches for production firms to achieve cost reduction alongside sustainability within the supply chain. It enables strategic insights in supply chain management to optimize inventory decisions, enhance financial adaptability, and ensure environmental compliance while maintaining profitability and reliability. Additionally, the study examines the impact of a two-level trade credit policy for a supplier-manufacturer-customer supply chain across nine scenarios based on credit periods. Four key contributions include (i) the evaluation of product greenness, reliability, price, and advertising on demand and deterioration rates; (ii) the analysis of reliability’s effect on production systems; (iii) the assessment of green technologies and cap-and-tax policies in emissions reduction; and (iv) the exploration of a partial two-level trade credit policy within a reliable production-based supply chain framework. The objective is to determine optimal investments in green technology, production run time, and cycle time to minimize total system costs, supported by numerical examples and graphical illustrations, offering insights for sustainable supply chain development in emerging economies.

采用无害环境的供应链管理战略是日益突出的响应在新兴经济体的可持续发展努力。随着全球环境恶化的加剧，工业被迫用绿色可靠的替代品取代传统产品。本研究介绍了一种双重目标策略，旨在最大限度地降低运营成本，同时确保环境保护，重点是为生产企业确定最佳方法，以实现供应链内的成本降低和可持续性。它使供应链管理中的战略见解能够优化库存决策，增强财务适应性，并确保环境合规性，同时保持盈利能力和可靠性。此外，本研究还考察了基于信贷周期的九个场景下，两级贸易信贷政策对供应商-制造商-客户供应链的影响。四个关键贡献包括(i)对产品绿色度、可靠性、价格、按需广告和劣化率的评估；（ii）可靠性对生产系统的影响分析；（iii）评估绿色技术和减排方面的限额和税收政策；（iv）在可靠的以生产为基础的供应链框架内探索部分两级贸易信贷政策。目标是确定绿色技术、生产运行时间和周期时间方面的最佳投资，以最大限度地降低系统总成本，并通过数值示例和图形插图提供支持，为新兴经济体的可持续供应链发展提供见解。

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引用次数: 0

A two-layer approach to analyzing imbalance in signed supply chain networks using probability-based triad census 基于概率的三元统计的两层供应链网络不平衡分析方法

Supply Chain Analytics

Pub Date : 2025-06-23 DOI: 10.1016/j.sca.2025.100141

Mansooreh Mirzaie , Maryam Nooraei Abadeh , Sondos Bahadori , Ji Zhang

Signed supply chain networks are essential for representing relationships between entities in commercial activities. Imbalances in these relationships can lead to significant consequences, including financial losses and reputational damage. This paper presents a two-layer analytical framework designed to identify and address imbalances in signed supply chain networks. The approach begins by mapping the initial Person-Other-X (P-O-X) layer onto the network’s signed nodes, establishing a foundational framework for the analysis. Throughout the network’s evolution, the method rigorously tracks its dynamics, focusing on assessing and improving the overall balance of the supply chain network. The analysis employs probability-based triad motifs to evaluate the impact of strategic sign changes on the network’s structure. This provides valuable insights into how alterations to supplier-manufacturer, distributor-customer, or other critical relationships affect the network’s stability. Sensitivity analysis is used to identify essential nodes or regions where sign changes substantially impact the network’s structural patterns. A comparative study of the probability-based Triad Census is conducted to understand the effects of sign changes on the network structure. Key contributions include the identification of influential nodes using centrality metrics and the impact of their sign changes on structural balance. The study’s findings emphasize the significant role of Betweenness Centrality in fostering balanced relationships within the supply chain network. This underscores the importance of key nodes, such as critical suppliers or distribution centers, in ensuring the overall stability and performance of the commercial network. Finally, this proactive strategy ensures a smoother flow of goods through the network, ultimately boosting performance and competitiveness in the marketplace.

签署的供应链网络对于表示商业活动中实体之间的关系至关重要。这些关系的不平衡可能导致严重后果，包括经济损失和声誉损害。本文提出了一个两层分析框架，旨在识别和解决签名供应链网络中的不平衡问题。该方法首先将初始的Person-Other-X （P-O-X）层映射到网络的签名节点上，为分析建立一个基本框架。在整个网络的演变过程中，该方法严格跟踪其动态，专注于评估和改善供应链网络的整体平衡。该分析采用基于概率的三联动机来评估战略符号变化对网络结构的影响。这为供应商-制造商、分销商-客户或其他关键关系的变化如何影响网络的稳定性提供了有价值的见解。灵敏度分析用于识别符号变化对网络结构模式产生重大影响的关键节点或区域。为了了解符号变化对网络结构的影响，本文对基于概率的三联体普查进行了比较研究。主要贡献包括使用中心性指标识别有影响的节点及其符号变化对结构平衡的影响。研究结果强调了中间性中心性在促进供应链网络内平衡关系中的重要作用。这强调了关键节点，如关键供应商或配送中心，在确保商业网络的整体稳定性和性能方面的重要性。最后，这种积极主动的策略确保了货物在网络中的流动更加顺畅，最终提高了业绩和市场竞争力。

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引用次数: 0

A machine learning approach for enhancing supply chain visibility with graph-based learning 利用基于图的学习增强供应链可见性的机器学习方法

Supply Chain Analytics

Pub Date : 2025-06-21 DOI: 10.1016/j.sca.2025.100135

Ge Zheng , Alexandra Brintrup

In today’s globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy.

在当今的全球化贸易中，供应链形成了跨越多个组织甚至国家的复杂网络，这使得它们极易受到中断的影响。最近的全球危机凸显了这些脆弱性，凸显了提高供应链可视性和复原力的迫切需要。然而，由于隐私、安全和监管方面的考虑，数据共享的限制往往会阻碍组织或国家之间实现全面可见性。此外，大多数现有的研究都集中在单个企业或产品层面的网络上，忽视了现实世界供应链中不同实体之间的多方面相互作用，从而限制了对供应链动态的整体理解。为了应对这些挑战，我们提出了一种集成联邦学习（FL）和图卷积神经网络（GCNs）的新方法，通过供应链知识图中的关系预测来增强供应链可见性。FL通过促进信息共享而无需交换原始数据、确保遵守隐私法规和维护数据安全，实现了各国之间的协作模型培训。GCNs使框架能够在知识图中捕获复杂的关系模式，从而实现准确的链接预测，从而发现隐藏的连接，并提供对供应链网络的全面洞察。实验结果验证了所提出方法的有效性，证明了其能够准确预测国家级供应链知识图中的关系。这种增强的可见性支持可操作的见解，促进前瞻性风险管理，并有助于制定有弹性和适应性的供应链战略，确保供应链更好地应对全球经济的复杂性。

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引用次数: 0

A game-theoretic framework for optimizing supply chain coordination and production 供应链协调与生产优化的博弈论框架

Supply Chain Analytics

Pub Date : 2025-06-19 DOI: 10.1016/j.sca.2025.100139

Masoud Narenji , Armin Mahmoodi , Milad Jasemi , Seyed Mojtaba Sajadi , Maryam Amini

This research introduces a groundbreaking competition concept for supply chains, utilizing the Stackelberg game method to address internal entity interactions. In practical scenarios, chain components often partially cooperate, prioritizing individual benefits without a holistic understanding of the entire chain and market dynamics. Achieving complete chain coordination is challenging, expensive, and requires high-level agreement. Our study presents a simultaneous competition model for two supply chains and their internal entities, considering heterogeneous customers in price and time-sensitive classes. Each chain serves regular and special customers with varied delivery times and pricing. This research aims to investigate how competition among supply chains under various conditions impacts metrics like performance, market share and profits. These conditions include collaboration strategy (Centralized or Decentralized Structure) and production approach (Shared or Dedicated Capacity for specific customers). We employed scenario analysis with the Stackelberg Game framework to study strategic and policy choices' impact on supply chain conditions. We identified 10 distinct scenarios for analysis. Using the Stackelberg model, we iteratively solved the developed models until they reached equilibrium in price and delivery time. Our findings suggest that chains benefit more from a cooperative strategy with a Centralized Structure. Market behavior influences the chosen production approach, where adopting a dedicated capacity policy can lead to increased market share and profits if the market leader does so. Alternative strategies result in competitive stances and reduced returns for both chains.

本研究为供应链引入了一个开创性的竞争概念，利用Stackelberg博弈方法来解决内部实体互动问题。在实际情况中，链的组成部分经常部分合作，优先考虑个人利益，而没有对整个链和市场动态的整体理解。实现完整的链协调是具有挑战性的，昂贵的，并且需要高层协议。我们的研究提出了两个供应链及其内部实体的同时竞争模型，考虑了价格和时间敏感类的异质客户。每家连锁店都以不同的送货时间和价格为普通顾客和特殊顾客服务。本研究旨在调查不同条件下供应链之间的竞争如何影响绩效、市场份额和利润等指标。这些条件包括协作策略（集中式或分散式结构）和生产方法（针对特定客户的共享或专用容量）。我们采用情境分析和Stackelberg博弈框架来研究战略和政策选择对供应链状况的影响。我们确定了10个不同的场景进行分析。利用Stackelberg模型，对已开发的模型进行迭代求解，直到它们在价格和交货时间上达到均衡。我们的研究结果表明，连锁店从中心化结构的合作策略中获益更多。市场行为影响所选择的生产方式，如果市场领导者这样做，采用专用产能政策可以增加市场份额和利润。可选的策略导致竞争的立场和减少回报的两个链。

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引用次数: 0

A machine learning framework for classifying customer advocacy in sustainable supply chains 一个用于在可持续供应链中对客户倡导进行分类的机器学习框架

Supply Chain Analytics

Pub Date : 2025-06-13 DOI: 10.1016/j.sca.2025.100137

Brintha Rajendran , Angappa Gunasekaran , Manivannan Babu

Sustainable supply chain management plays a pivotal role in shaping corporate reputation and enhancing customer loyalty in the contemporary market. This study uniquely integrates regional, demographic, and psychographic data with advanced machine learning methodologies, including clustering, decision trees, and association rule mining, to classify and predict customer advocacy based on Environmental, Social, and Governance (ESG) performance indicators and supply chain risk management. Unlike previous research, the analysis explicitly segments customers by their distinct ESG trust perceptions and advocacy behaviours, providing nuanced insights into how varying demographic and regional characteristics influence customer support for sustainable practices. Results reveal that customer advocacy patterns significantly differ across segments, particularly highlighting groups with strong environmental concerns and positive evaluations of governance practices. The study’s comprehensive approach not only advances theoretical understanding by integrating diverse customer attributes but also delivers precise, actionable recommendations for supply chain managers to foster targeted and effective sustainable initiatives.

在当代市场中，可持续供应链管理在塑造企业声誉和提高客户忠诚度方面发挥着关键作用。本研究独特地将区域、人口统计和心理数据与先进的机器学习方法（包括聚类、决策树和关联规则挖掘）相结合，根据环境、社会和治理（ESG）绩效指标和供应链风险管理对客户倡导进行分类和预测。与之前的研究不同，该分析明确地根据不同的ESG信任观念和倡导行为对客户进行了细分，为不同的人口统计和区域特征如何影响客户对可持续实践的支持提供了细致的见解。结果显示，客户倡导模式在各个部门之间存在显著差异，特别是强调具有强烈环境关注和对治理实践进行积极评价的群体。该研究的综合方法不仅通过整合不同的客户属性来推进理论理解，而且为供应链管理者提供了精确的、可操作的建议，以促进有针对性和有效的可持续举措。

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引用次数: 0

An analytical framework for evaluating the impact of Artificial Intelligence technologies in supply chains 一个评估人工智能技术对供应链影响的分析框架

Supply Chain Analytics

Pub Date : 2025-06-11 DOI: 10.1016/j.sca.2025.100129

Eduardo e Oliveira , Maria Teresa Pereira , Alcibíades P. Guedes

This study introduces a novel framework for analyzing the impact of technologies through their effect and maturity, allowing for a clear presentation of the literature review results. We then conduct a literature review on applying Artificial Intelligence (AI) to Supply Chain (SC), focusing on evaluating the impact of existing technologies. The proposed framework is based on three axes: (1) maturity axis, which evaluates the readiness level of each technology and its current spread of use, (2) effect axis, which measures the disruption it can bring in terms of performance improvement and the number of potential applications, and (3) full axis, which combines the previous two axes. The proposed novel framework allows researchers to look at the existing literature differently. It makes it easier for practitioners to read and understand the impact of such AI technologies on SC. For the literature review that validates the framework, we have analyzed 24 literature review papers and 118 application papers on this topic. We have grouped the application papers into 90 technologies and used the proposed framework to evaluate them. From the analysis and discussion, we confirm some previous conclusions made in the literature as well as discover new gaps, and we suggest research avenues to be explored.

本研究引入了一个新的框架，通过技术的效果和成熟度来分析技术的影响，从而清晰地呈现文献综述的结果。然后，我们对将人工智能（AI）应用于供应链（SC）进行了文献综述，重点是评估现有技术的影响。提出的框架基于三个轴：(1)成熟度轴，它评估每种技术的准备程度及其当前的使用范围；(2)效果轴，它衡量它在性能改进和潜在应用程序数量方面可能带来的破坏；(3)完整轴，它结合了前两个轴。提出的新框架允许研究人员以不同的方式看待现有文献。它使从业者更容易阅读和理解此类人工智能技术对供应链的影响。对于验证该框架的文献综述，我们分析了24篇文献综述论文和118篇关于该主题的应用论文。我们将这些应用论文分为90种技术，并使用提议的框架对它们进行评估。从分析和讨论中，我们确认了先前文献中得出的一些结论，并发现了新的空白，并提出了有待探索的研究途径。

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引用次数: 0

A Markov decision process model for enhancing resilience in food supply chains during natural disasters 自然灾害中提高粮食供应链恢复力的马尔可夫决策过程模型

Supply Chain Analytics

Pub Date : 2025-06-08 DOI: 10.1016/j.sca.2025.100136

Mengfei Chen , Mohamed Kharbeche , Mohamed Haouari , Weihong Guo Grace

Natural disasters like hurricanes, earthquakes, and floods devastate food supply chains and can threaten food security and public health. These disruptions, from production to consumption, lead to shortages, increased waste, and heightened vulnerability among food-insecure populations. This study addresses the need for effective emergency strategies to ensure food continuity and equity during crises. A Markov Decision Process (MDP)-based model is proposed to enhance food supply chain resilience under disaster conditions. The model involves a two-stage decision-making process: Stage 1 focuses on strategic decisions for immediate response, such as facility reconstruction, and Stage 2 handles tactical decisions during relief efforts, such as transportation routes and product flow. The objective functions of our model include minimizing response time and costs and ensuring equity of food accessibility. A resilience assessment approach is proposed to evaluate the performance of Pareto solutions. The proposed method is applied to the Qatar beef supply chain during a flooding scenario, demonstrating practical effectiveness. Sensitivity analysis is conducted to identify critical thresholds for establishing alternative distribution centers, which helps to optimize responses based on facility capacity. This research improves disaster preparedness and response, ensuring that food supply chains can adapt and recover quickly while enhancing the equity of people’s access to food and nutrition. A case study on Qatar’s beef supply chain under flood conditions shows that the proposed method achieves up to 95 % reduction in response time cost, a 9 % improvement in system resilience, and maintains over 99.5 % food accessibility under severe disruption scenarios.

飓风、地震和洪水等自然灾害会破坏粮食供应链，威胁粮食安全和公众健康。从生产到消费的这些中断导致粮食短缺、浪费加剧，并加剧了粮食不安全人口的脆弱性。本研究探讨了制定有效应急战略的必要性，以确保危机期间粮食的连续性和公平性。提出了一种基于马尔可夫决策过程（MDP）的食品供应链应变模型。该模型涉及两个阶段的决策过程：第一阶段侧重于立即响应的战略决策，如设施重建，第二阶段处理救灾工作中的战术决策，如运输路线和产品流动。我们模型的目标功能包括最小化响应时间和成本，并确保粮食可及性的公平性。提出了一种弹性评价方法来评价Pareto解的性能。提出的方法在洪水情景下应用于卡塔尔牛肉供应链，证明了实际有效性。通过敏感性分析，确定建立备选配送中心的临界阈值，从而优化基于设施容量的响应。这项研究改善了备灾和救灾工作，确保粮食供应链能够快速适应和恢复，同时提高人们获得粮食和营养的公平性。对洪水条件下卡塔尔牛肉供应链的案例研究表明，所提出的方法可将响应时间成本降低95% %，系统恢复力提高9% %，并在严重中断情况下保持99.5% %以上的食物可及性。

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引用次数: 0

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

Supply Chain Analytics

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

Naveen Saran , Nishtha Kesswani

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

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

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引用次数: 0

A two-period game-theoretic model for the equilibrium decisions in the presence of strategic inventory in supply chain management 供应链管理中存在战略库存时均衡决策的两期博弈论模型

Supply Chain Analytics

Pub Date : 2025-06-03 DOI: 10.1016/j.sca.2025.100127

Subrata Saha

Investment efforts by the upstream manufacturer and the downstream retailer to stimulate sales are prevalent in different industries. Likewise, the downstream retailers could hold inventory strategically as a bargaining chip to induce the upstream manufacturer to reduce future wholesale prices in the supply chain. Anticipating such strategic behavior of the retailer, the manufacturer responds by increasing the early period wholesale price, which might weaken the impact of the investment efforts due to a rising price. Our central research questions are: Under what conditions do the retailer and the manufacturer benefit from investment effort, and how does strategic inventory affect the efficiency of the supply chain? Therefore, we develop a two-period game-theoretic model and characterize the equilibrium decisions and profits for inventory holding and investment effort patterns. Results demonstrate that the retailer’s inventory holding decision can significantly reduce the impact of the manufacturer’s investment effort, and the manufacturer even receives lower profits compared to the scenario where strategic inventory is not withheld. The finding contrasts with the existing research, which suggests that the manufacturer always receives higher profit if the retailer holds inventory in a supply chain. Further, we investigate the incremental or detrimental effect of base demand on the second period. We find that the retailer’s strategic inventory can hurt both members of the supply chain even if there is a sizable increment in market size in the second period, and both members could be worse off.

上游制造商和下游零售商为刺激销售而进行的投资努力在不同行业都很普遍。同样，下游零售商可以战略性地持有库存作为讨价还价的筹码，以诱使上游制造商降低供应链中未来的批发价格。预计到零售商的这种战略行为，制造商通过提高前期批发价格来应对，这可能会削弱由于价格上涨而导致的投资努力的影响。我们的核心研究问题是：在什么条件下零售商和制造商能从投资努力中获益，战略库存如何影响供应链的效率？因此，我们建立了一个两期博弈论模型，并描述了库存持有和投资努力模式的均衡决策和利润。结果表明，零售商的库存持有决策可以显著降低制造商投资努力的影响，与不保留战略库存的情况相比，制造商的利润甚至更低。这一发现与现有的研究结果形成了对比。现有研究表明，如果零售商在供应链中持有库存，制造商总是能获得更高的利润。此外，我们还研究了基础需求对第二阶段的增量或有害影响。我们发现，即使在第二阶段市场规模有相当大的增长，零售商的战略库存也会损害供应链的两个成员，并且两个成员的情况都可能更糟。

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引用次数: 0

An analytical review of predictive methods for delivery delays in supply chains 供应链中交货延迟预测方法的分析回顾

Supply Chain Analytics

Pub Date : 2025-05-22 DOI: 10.1016/j.sca.2025.100130

Norman Müller, Peter Burggräf, Fabian Steinberg, Carl René Sauer, Maximilian Schütz

Predicting delivery delays is crucial for companies, especially in times of increasing global uncertainty and vulnerable supply chains. Machine learning (ML) offers significant potential to improve the forecast performance and quality of delivery delay prediction. Although various prediction approaches have been proposed in research, a structured and comprehensive overview is lacking. This paper addresses this gap by conducting a systematic literature review on the direct prediction of delivery delays. The objective is to identify applied prediction approaches and data sources, assess their readiness for real-world implementation, and derive a research agenda. The findings reveal that current research often focuses on marginal optimization of prediction performance while lacking practical applicability. Furthermore, most studies emphasize classifying deliveries as on time or delayed, rather than predicting the actual delay magnitude. Regarding the data used for prediction, combining enterprise resource planning (ERP) data with data from logistics improves prediction performance. However, environmental and location data, which could be easily integrated into ERP-based ML models, are rarely considered. This indicates a misalignment in current research, emphasizing the need for models combining practical applicability with predictive accuracy. Further research is required to address these identified deficits. Therefore, the present paper proposes a research agenda, to prioritize the most important deficits. These include, among others the industrial application, optimal prediction timing and ideal data combinations to achieve high prediction accuracy. It also highlights the need for integrated decision support systems that provide prediction-based recommendations, enhancing the practical value of predictive models in supply chain management.

预测交货延迟对企业来说至关重要，尤其是在全球不确定性增加、供应链脆弱的情况下。机器学习（ML）为提高交付延迟预测的预测性能和质量提供了巨大的潜力。虽然研究中提出了各种预测方法，但缺乏结构化和全面的概述。本文通过对直接预测交货延迟进行系统的文献回顾来解决这一差距。目标是确定应用的预测方法和数据源，评估其对现实世界实施的准备情况，并得出研究议程。研究结果表明，目前的研究往往侧重于预测性能的边际优化，缺乏实际适用性。此外，大多数研究强调将交付分类为准时或延迟，而不是预测实际的延迟程度。对于用于预测的数据，将企业资源规划（ERP）数据与物流数据相结合可以提高预测性能。然而，很少考虑环境和位置数据，这些数据可以很容易地集成到基于erp的ML模型中。这表明目前的研究存在偏差，强调需要将实际适用性与预测准确性结合起来的模型。需要进一步的研究来解决这些已确定的缺陷。因此，本文提出了一个研究议程，优先考虑最重要的赤字。其中包括工业应用，最佳预测时机和理想的数据组合，以实现高预测精度。它还强调需要集成的决策支持系统，提供基于预测的建议，提高预测模型在供应链管理中的实用价值。

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引用次数: 0