Supply Chain Analytics最新文献_第7页

A stochastic programming approach to the location of distribution centers for multinational enterprises under demand uncertainty 需求不确定性下跨国企业配送中心选址的随机规划方法

Supply Chain Analytics

Pub Date : 2025-09-01 Epub Date: 2025-07-11 DOI: 10.1016/j.sca.2025.100147

Kuancheng Huang , Wei-Ting Chen , Yu-Ching Wu , Jan-Ren Chen

Multinational enterprises (MNEs) often collaborate with local agents to establish initial distribution channels due to their need for market-specific knowledge and experience. As the market matures and upstream suppliers and production plans are solidified, MNEs may transition to developing their distribution systems and supply chain networks. Integrating the transportation network among upstream material suppliers, production facilities, and distribution centers (DCs) becomes crucial at this stage. Since transportation costs constitute a significant portion of enterprise expenses, optimizing upstream transportation is essential for MNEs following this market entry strategy. This study aims to optimize the location decisions of DCs while assuming that suppliers, plants, and retailers have fixed locations. A critical focus is the integration of upstream transportation operations, specifically between suppliers and plants and between plants and DCs, to minimize inefficient empty backhauls. Additionally, demand uncertainty is factored into this long-term strategic design problem. A stochastic programming (SP) model is developed, and a solution procedure based on the Genetic Algorithm (GA) is designed to handle practical-scale problems. Numerical experiments demonstrate that the GA method achieves a solution quality with less than a 1 % gap compared to the optimal solution while also significantly reducing computation time.

跨国企业（MNEs）往往与当地代理商合作建立最初的分销渠道，因为他们需要特定市场的知识和经验。随着市场的成熟和上游供应商和生产计划的固化，跨国公司可能会转向发展他们的分销系统和供应链网络。在这个阶段，整合上游物料供应商、生产设施和配送中心（DCs）之间的运输网络变得至关重要。由于运输成本占企业费用的很大一部分，因此优化上游运输对于跨国公司遵循这种市场进入战略至关重要。本研究旨在优化配送中心的选址决策，同时假设供应商、工厂和零售商有固定的地点。一个关键的焦点是上游运输业务的整合，特别是供应商和工厂之间以及工厂和配送中心之间的整合，以最大限度地减少低效的空载。此外，需求的不确定性也被考虑到这个长期战略设计问题中。建立了随机规划（SP）模型，并设计了基于遗传算法（GA）的求解程序来处理实际问题。数值实验表明，该方法与最优解相比，求解质量差距小于1 %，同时显著减少了计算时间。

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引用次数: 0

An optimization framework for emergency supply chains prioritizing elderly populations during pandemics 大流行期间优先考虑老年人的应急供应链优化框架

Supply Chain Analytics

Pub Date : 2025-09-01 Epub Date: 2025-05-20 DOI: 10.1016/j.sca.2025.100131

Behzad Mosallanezhad , Neale R. Smith , Fatemeh Gholian-Jouybari , Mostafa Hajiaghaei-Keshteli

Pandemics have severely disrupted supply chains, making it challenging to meet the demands of the elderly and other vulnerable populations. This study addresses the importance of developing a sustainable emergency supply chain network that ensures timely and fair resource allocation for elderly communities. Therefore, an age-structured Susceptible-Infected-Recovered (SIR) system dynamics framework is utilized to simulate pandemic development and estimate age-specific demand for highly-demand items. Then, a multi-objective stochastic mathematical model is proposed to optimize cost, decrease unfulfilled demand, and reduce environmental effects. A numerical example inspired by the recent COVID-19 pandemic in Mexico is introduced, which focuses on the distribution of personal protective equipment (PPE), medical supplies, and test kits to hospitals, pharmacies, and other demand points. This approach couples the estimated demand from the system dynamics model and then optimizes the stochastic model. The results present optimal decisions for allocation, inventory, product flow, distribution, and waste management under different scenarios. A sensitivity analysis for the demand parameter is also performed, showing that total cost, unmet demand, and environmental effects increase as demand rises. The study demonstrates the model's capacity to enhance supply chain resilience and adaptability, providing valuable insights to improve emergency responses for at-risk populations.

大流行严重扰乱了供应链，使满足老年人和其他弱势群体的需求变得困难。本研究探讨了发展可持续的应急供应链网络的重要性，以确保及时和公平地为老年社区分配资源。因此，使用年龄结构的易感-感染-康复（SIR）系统动力学框架来模拟大流行的发展并估计对高需求物品的特定年龄需求。在此基础上，提出了优化成本、减少未满足需求和降低环境影响的多目标随机数学模型。本文介绍了一个受最近墨西哥COVID-19大流行启发的数值例子，该例子侧重于向医院、药房和其他需求点分发个人防护装备（PPE）、医疗用品和检测包。该方法将系统动力学模型中的估计需求耦合起来，对随机模型进行优化。结果给出了在不同场景下分配、库存、产品流、分配和废物管理的最佳决策。对需求参数进行敏感性分析，表明总成本、未满足需求和环境影响随着需求的增加而增加。该研究证明了该模型增强供应链弹性和适应性的能力，为改善风险人群的应急响应提供了有价值的见解。

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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 : 2025-09-01 Epub 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 multi-objective analytical framework for sustainable blood supply chain optimization 可持续血液供应链优化的多目标分析框架

Supply Chain Analytics

Pub Date : 2025-09-01 Epub Date: 2025-06-27 DOI: 10.1016/j.sca.2025.100142

Agus Mansur , Taufiq Hidayat , Novrianty Rizky , Ivan Darma Wangsa

This study presents a multi-objective optimization model for blood supply chain (BSC) management, aiming to maximize total profit and fulfillment rate and minimize carbon emissions. The model is formulated as a mixed-integer linear program (MILP) and solved using the weighted sum method. The BSCM is structured as a multi-echelon network involving blood mobiles, local blood centers, regional blood banks (RBBs), hospitals, and healthcare facilities. Assumptions include deterministic demand and fixed blood shelf life. A case study in East Kalimantan, Indonesia, shows a total revenue of Indonesian Rupiah (IDR) of 13.07 billion and a total cost of IDR 8.58 billion, resulting in a profit of IDR 4.49 billion. The fulfillment rates for hospitals and healthcare facilities are 109.13 % and 154.57 %, respectively. Total emissions reach 203.94-kilogram CO₂ equivalent (kg CO₂e), mainly from production. Sensitivity analysis highlights the impact of demand, capacity, and pricing on supply chain performance. Furthermore, transshipment among RBBs plays a vital role in balancing inventory levels, though excessive transshipment may lead to increased costs and emissions.

提出了一种以利润最大化、履约率最大化、碳排放最小化为目标的血液供应链（BSC）管理多目标优化模型。该模型采用混合整数线性规划（MILP）形式，采用加权和法求解。BSCM的结构是一个多层次的网络，包括血液流动、地方血液中心、区域血库、医院和医疗机构。假设包括确定性需求和固定的血液保质期。印度尼西亚东加里曼丹的一个案例研究显示，总收入为130.7亿印尼盾，总成本为85.8亿印尼盾，利润为44.9亿印尼盾。医院和保健设施的履约率分别为109.13% %和154.57 %。总排放量达到203.94千克二氧化碳当量（kg CO2e），主要来自生产。敏感性分析强调需求、产能和定价对供应链绩效的影响。此外，rbb之间的转运在平衡库存水平方面起着至关重要的作用，尽管过度转运可能导致成本和排放增加。

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引用次数: 0

An optimization framework for sustainable closed-loop supply chains with green investment and recovery policy 具有绿色投资和回收政策的可持续闭环供应链优化框架

Supply Chain Analytics

Pub Date : 2025-09-01 Epub Date: 2025-07-08 DOI: 10.1016/j.sca.2025.100146

Wakhid Ahmad Jauhari , Dhea Naomi Kenlaksita , Nughthoh Arfawi Kurdhi , Dana Marsetiya Utama

Sustainability in closed-loop supply chains (CLSCs) is becoming a significant focus due to increasing environmental pressures and carbon regulations. While numerous studies have examined aspects such as carbon emissions, green technology, and the quality of used products, gaps remain in integrating these elements, particularly concerning the influence of collection quality on emissions, various recovery policies, and contract-based coordination mechanisms for sharing green technology investments. This study aims to develop a comprehensive supply chain model by integrating these factors through three main mechanisms: centralized coordination, decentralized, and green technology revenue investment sharing (GRIS) contracts. The model employs a mathematical formulation that considers green technology investment, collection rate, the quality of used products, and carbon emissions. Simulations were conducted with sensitivity analysis to evaluate the impact of parameters such as carbon tax, selling price sensitivity coefficient, green technology investment, and collection effort on system performance. Results indicate that the centralized coordination model excels in maximizing total profit and operational stability when compared to the decentralized model. However, it is more sensitive to changes in parameters. GRIS contracts offer flexibility in profit redistribution between producers and retailers without compromising the system efficiency. The findings also indicate that investments in green technology and collection efforts significantly contribute to enhanced collection quality and reduced carbon emissions, with more pronounced effects in the centralized model. This research offers a comprehensive approach to tackling sustainability challenges in CLSC, providing practical insights for industry stakeholders and policymakers in developing strategies that promote both economic and environmental sustainability.

由于日益增长的环境压力和碳法规，闭环供应链（CLSCs）的可持续性正成为一个重要的焦点。虽然有许多研究考察了碳排放、绿色技术和废旧产品质量等方面，但在整合这些要素方面仍然存在差距，特别是在收集质量对排放的影响、各种回收政策和共享绿色技术投资的基于合同的协调机制方面。本研究旨在通过集中式协调、分散式协调和绿色技术收益投资共享（GRIS）合同三种主要机制，整合这些因素，建立一个综合供应链模型。该模型采用了一个数学公式，考虑了绿色技术投资、收集率、废旧产品质量和碳排放。采用敏感性分析方法，对碳税、销售价格敏感性系数、绿色技术投资和征收力度等参数对系统性能的影响进行了仿真分析。结果表明，集中式协调模式在总利润最大化和运行稳定性方面优于分散式协调模式。然而，它对参数的变化更为敏感。GRIS合同在不影响系统效率的情况下，为生产者和零售商之间的利润再分配提供了灵活性。研究结果还表明，对绿色技术和收集工作的投资显著有助于提高收集质量和减少碳排放，其中集中式模式的效果更为明显。本研究为解决CLSC的可持续性挑战提供了一个全面的方法，为行业利益相关者和决策者制定促进经济和环境可持续性的战略提供了实用的见解。

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引用次数: 0

A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry 因果机器学习与提高汽车行业供应链弹性和效率的传统方法的比较评估

Supply Chain Analytics

Pub Date : 2025-06-01 Epub Date: 2025-04-01 DOI: 10.1016/j.sca.2025.100116

Ishansh Gupta, Adriana Martinez, Sergio Correa, Hendro Wicaksono

Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.

高效的供应商升级对于维持汽车行业供应链的平稳运行至关重要，因为中断可能导致严重的生产延迟和财务损失。许多公司仍然依赖传统的升级方法，这种方法可能缺乏现代技术所提供的精确性和适应性。本研究对一家德国领先汽车公司的供应商升级决策策略进行了比较分析，评估了因果机器学习（CML）、传统机器学习（ML）和当前升级实践。本研究采用解释序贯混合方法，结合层次分析法（AHP）与25位行业专家的深度访谈。这些方法基于几个性能指标进行评估：准确性、业务影响、解释能力、人为偏差、压力测试和恢复时间。研究结果表明，CML优于传统ML和现有方法，提供了更好的风险预测、可解释性和决策支持。此外，研究还通过技术接受模型（TAM）探讨了这些技术的内部接受程度。结果突出了CML在提高供应链弹性和效率方面的变革潜力。通过弥合预测分析和可解释的人工智能之间的差距，本研究为寻求使用高级分析优化供应商管理的公司提供了有价值的指导。

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引用次数: 0

An integrated multi-criteria decision-making model for identifying complexity drivers in the oil and gas supply chain 一种集成的多标准决策模型，用于识别油气供应链中的复杂性驱动因素

Supply Chain Analytics

Pub Date : 2025-06-01 Epub Date: 2025-02-27 DOI: 10.1016/j.sca.2025.100104

Sujan Piya , Yahya Al-Hinai , Nasr Al Hinai , Mohammad Khadem , Mohammad Shamsuzzaman

The oil and gas industry, with numerous supply chain partners, significantly contributes to the world economy. This industry's operations involve complex processes and interactions with different stakeholders, leading to many drivers contributing to its complexity. This study identifies seventeen complexity drivers in the oil and gas supply chain based on an extensive literature review and the Pareto principle. The identified drivers were then analyzed using an integrated Analytical Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approaches. The analysis reveals that the procurement system is the most important driver, followed by process synchronization among supply chain partners. Government regulation is the least influential driver in creating complexity in the oil and gas supply chain. Further analysis indicated that seven of the seventeen identified drivers were classified as causes, while the remaining ones fell under the effect group. The results of this study are expected to help decision-makers devise strategies based on the drivers with significant impact to minimize complexity and mitigate its effects on the oil and gas industry supply chain.

石油和天然气行业拥有众多的供应链合作伙伴，对世界经济做出了重大贡献。该行业的运营涉及复杂的流程和与不同利益相关者的互动，导致许多驱动因素增加了其复杂性。基于广泛的文献回顾和帕累托原则，本研究确定了油气供应链中的17个复杂性驱动因素。然后使用综合层次分析法（AHP）和决策试验与评估实验室（DEMATEL）方法对确定的驱动因素进行分析。分析表明，采购系统是最重要的驱动因素，其次是供应链合作伙伴之间的流程同步。政府监管是造成油气供应链复杂性的影响最小的因素。进一步的分析表明，17个确定的驱动因素中有7个被归类为原因，而其余的则属于影响组。这项研究的结果有望帮助决策者根据具有重大影响的驱动因素制定策略，以最大限度地降低复杂性并减轻其对油气行业供应链的影响。

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引用次数: 0

A bibliometric analysis of industry 5.0 and healthcare supply chain research: Emerging opportunities and future challenges 工业5.0和医疗保健供应链研究的文献计量分析：新出现的机会和未来的挑战

Supply Chain Analytics

Pub Date : 2025-06-01 Epub Date: 2025-04-25 DOI: 10.1016/j.sca.2025.100125

Rajesh Matha , Subhodeep Mukherjee , Rashmi Ranjan Panigrahi , Avinash K Shrivastava

This paper explores the emerging field of Industry 5.0 in the context of healthcare supply chains (HSC). It aims to improve resilience, sustainability, and efficiency through human-centred techniques and cutting-edge technology. This study focuses on HSC management and emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), and robotics. Using a bibliometric analysis of 142 academic publications, this paper identifies key publication trends, significant research contributions, and thematic clusters. The results show a steady increase in research interest since 2018, with a growth rate of 15 % year-on-year in publications and contributions from 20 countries, led by the United States of America, China and the United Kingdom. These suggest implementing Industry 5.0 technology to optimize operational processes, improve demand forecasting, and advance sustainable practices. Identified topic clusters highlight key aspects such as decision support systems, sustainability, resilience, and technological integration, demonstrating the potential of Industry 5.0 to transform healthcare logistics. Integrating human expertise with intelligent systems, Industry 5.0 addresses healthcare delivery challenges while ensuring high-quality patient care. Future research can build on this study’s contributions to explore the intersection of HSC management and technological advancements.

本文探讨了医疗保健供应链（HSC）背景下工业5.0的新兴领域。它旨在通过以人为本的技术和尖端技术提高弹性、可持续性和效率。本研究的重点是HSC管理和新兴技术，如人工智能（AI）、物联网（IoT）和机器人技术。通过对142份学术出版物的文献计量学分析，本文确定了主要的出版趋势、重要的研究贡献和专题集群。结果显示，自2018年以来，研究兴趣稳步增长，以美国、中国和英国为首的20个国家的出版物和贡献量同比增长15% %。这些建议采用工业5.0技术来优化操作流程，改进需求预测，并推进可持续实践。确定的主题集群突出了决策支持系统、可持续性、弹性和技术集成等关键方面，展示了工业5.0改变医疗保健物流的潜力。工业5.0将人类专业知识与智能系统相结合，解决了医疗保健交付方面的挑战，同时确保了高质量的患者护理。未来的研究可以在本研究的基础上探索HSC管理与技术进步的交集。

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引用次数: 0

An optimization-based analytics model for sustainable and blockchain-enabled supply chains in uncertain environments 一个基于优化的分析模型，用于不确定环境下的可持续和区块链支持的供应链

Supply Chain Analytics

Pub Date : 2025-06-01 Epub Date: 2025-04-03 DOI: 10.1016/j.sca.2025.100119

S. Priyan

The carbon footprint is highly uncertain and directly impacts demand forecasting, with uncertainty arising from both positive and negative perspectives. This duality highlights the contrasting viewpoints of decision-makers during the decision-making process. This study employs generalized trapezoidal bipolar fuzzy numbers to manage uncertainty in carbon emissions and integrates blockchain technology to enhance demand forecasting in the supply chain. Additionally, we incorporate a warm-up process to minimize faulty items during production and consider investments in green technologies to reduce emissions from various activities. This paper provides insights into sustainability, operational efficacy, and profit maximization in uncertain ecological settings. We mathematically formulate the proposed scenario and uniquely calculate the concave combination of expected values from both positive and negative membership components. Optimality is derived, and a numerical analysis is performed to effectively clarify the theory, followed by an extensive sensitivity analysis of various parameters.

碳足迹具有高度不确定性，直接影响需求预测，其不确定性来自积极和消极两个角度。这种双重性凸显了决策者在决策过程中截然不同的观点。本研究采用广义梯形双极模糊数来管理碳排放的不确定性，并整合区块链技术来加强供应链中的需求预测。此外，我们还纳入了预热流程，以最大限度地减少生产过程中的次品，并考虑投资绿色技术以减少各种活动的排放。本文深入探讨了不确定生态环境下的可持续性、运营效率和利润最大化。我们用数学方法制定了建议的方案，并唯一计算了正负成员成分预期值的凹形组合。我们推导出了最优性，并进行了数值分析以有效阐明理论，随后还对各种参数进行了广泛的敏感性分析。

引用次数: 0

Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery 危机后供应链弹性和恢复的机器学习和人工智能方法和应用

Supply Chain Analytics

Pub Date : 2025-06-01 Epub Date: 2025-04-04 DOI: 10.1016/j.sca.2025.100121

G. Sakthi Balan , V. Santhosh Kumar , S. Aravind Raj

Resilient and adaptive strategies for recovery have been underscored by supply chain disruptions induced by natural disasters, pandemics, and wars. Supply chain resilience protects enterprises, communities, and humanitarian activities during pandemics and wars. This study investigates the utilization of artificial intelligence and machine learning methodologies to enhance supply chain resilience and recovery in the aftermath of these crises. Leveraging data-driven methodologies, these technologies provide opportunities to improve the overall resilience of the supply chain, optimize resource allocation, and enhance decision-making. Proposed newer measures to protect economies, national security, lives, and a more resilient future are discussed in this study. Machine learning and artificial intelligence can process vast amounts of data quickly to provide real-time insights into the state of the supply chain, including damage assessments, demand fluctuations, and disruptions to transportation routes. Machine learning and artificial intelligence in supply chain management have reduced demand forecasting errors by 10–20 % and enhanced disruption reaction times by 20–30 %. The delivery reliability was also enhanced by 10–20 % as the artificial intelligence can forecast the delays and recommend alternate routes. Machine learning and artificial intelligence provide insights, automation, and agility to rebuild and enhance supply chains after challenging circumstances. This work is unique in showing how to improve supply chain resilience at critical moments by combining technologies and adopting hybrid methodologies.

自然灾害、流行病和战争造成的供应链中断凸显了恢复弹性和适应性战略。供应链弹性在大流行和战争期间保护企业、社区和人道主义活动。本研究探讨了利用人工智能和机器学习方法来增强供应链在这些危机之后的弹性和恢复。利用数据驱动的方法，这些技术为提高供应链的整体弹性、优化资源分配和增强决策提供了机会。本研究讨论了为保护经济、国家安全、生命和更有弹性的未来而提出的新措施。机器学习和人工智能可以快速处理大量数据，提供对供应链状态的实时洞察，包括损害评估、需求波动和运输路线中断。供应链管理中的机器学习和人工智能将需求预测误差降低了10 - 20% %，并将中断反应时间提高了20 - 30% %。由于人工智能可以预测延误并推荐替代路线，因此交付可靠性也提高了10 - 20% %。机器学习和人工智能为在充满挑战的环境下重建和增强供应链提供了洞察力、自动化和敏捷性。这项工作在展示如何通过结合技术和采用混合方法在关键时刻提高供应链弹性方面是独一无二的。

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引用次数: 0