Supply Chain Analytics最新文献_第8页

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 : 2025-04-01 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

A sustainability and profitability optimization model in three-stage green supply chains under uncertainty with competitive and cooperative game dynamics 不确定条件下具有竞争与合作博弈动力学的三阶段绿色供应链可持续性与盈利能力优化模型

Supply Chain Analytics

Pub Date : 2025-04-01 DOI: 10.1016/j.sca.2025.100114

Manojit Das , Biswajit Muchi , Shariful Alam , Dipak Kumar Jana

This research explores the integration of sustainability into green supply chain management (GSCM) under uncertainty by focusing on third-party logistics (TPL) services. We propose a three-stage green supply chain (TS-GSC) involving two manufacturers producing substitute green products, a TPL provider, and two retailers. Four scenarios are constructed to analyze the impact of competitive and cooperative dynamics on product pricing, greening degree,

{CO}_{2}

emissions, and delivery time. This study globally maximizes each stakeholder’s expected net profit in every decision-making scenario by applying fuzzy parameters’ defuzzification with fuzzy possibility measures. The results highlight that cooperation between retailers can lead to increased

{CO}_{2}

emissions and longer delivery time, while cooperative manufacturers enhance product greening but raise prices. Competition tends to lower prices and a compromised product greening. The scenario with two competing manufacturers and two competing retailers maximizes profitability and balances pricing, greening, emissions, and delivery time. The study provides managerial insights for achieving consumer satisfaction, profitability, and sustainability in the TS-GSC system.

本研究以第三方物流（TPL）服务为研究对象，探讨不确定性下可持续发展与绿色供应链管理（GSCM）的整合。我们提出了一个三阶段的绿色供应链（TS-GSC），包括两个生产替代绿色产品的制造商、一个第三方物流供应商和两个零售商。本文构建了四个情景，分析了竞争与合作动态对产品定价、绿化程度、二氧化碳排放和交货时间的影响。本研究通过模糊参数去模糊化和模糊可能性测度，使各决策情景下各利益相关者的期望净利润全局最大化。结果表明，零售商之间的合作会导致二氧化碳排放量的增加和交货时间的延长，而制造商之间的合作会提高产品的绿色化程度，但会提高价格。竞争往往会降低价格，损害产品的绿色化。两家相互竞争的制造商和两家相互竞争的零售商的情景最大化了盈利能力，并平衡了定价、环保、排放和交货时间。本研究为TS-GSC系统实现消费者满意度、盈利能力和可持续性提供了管理见解。

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引用次数: 0

A multi-objective supply chain optimization model for reliable remanufacturing problems with M/M/m/k queues M/M/ M/ k队列可靠再制造问题的多目标供应链优化模型

Supply Chain Analytics

Pub Date : 2025-03-27 DOI: 10.1016/j.sca.2025.100118

Vahid Hajipour , Shermineh Hadad Kaveh , Fatih Yiğit , Ali Gharaei

Product recovery is critical in reducing costs, enhancing profitability, and improving supply chain responsiveness to customer demands. Remanufacturing returned products, as part of the circular economy, is a central strategy in achieving these goals. This study presents a model that optimizes the remanufacturing process using in-house workstations and outsourcing to maximize supply chain profitability, reduce queue lengths, and ensure machine reliability. The remanufacturing system is modeled as an M/M/m/k queuing system, considering real-world supply chain constraints such as budget limitations, station capacity, and machine reliability. Supply chain optimization is achieved by maintaining efficiency while examining different remanufacturing policies and pricing strategies. The results show that expanding remanufacturing capacity enhances supply chain profitability, even with moderate increases in queue length. We provide valuable insights for supply chain managers aiming to optimize their remanufacturing processes and balance cost, efficiency, and reliability.

产品回收对于降低成本、提高盈利能力和改善供应链对客户需求的响应是至关重要的。作为循环经济的一部分，回收产品的再制造是实现这些目标的核心战略。本研究提出一个利用内部工作站和外包来优化再制造过程的模型，以最大限度地提高供应链的盈利能力，减少排队长度，并确保机器的可靠性。再制造系统建模为M/M/ M/ k排队系统，考虑到现实世界的供应链约束，如预算限制、站点容量和机器可靠性。供应链优化是通过在保持效率的同时研究不同的再制造政策和定价策略来实现的。结果表明，即使队列长度适度增加，再制造能力的扩大也能提高供应链的盈利能力。我们为供应链管理者提供有价值的见解，帮助他们优化再制造流程，平衡成本、效率和可靠性。

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引用次数: 0

A system dynamics approach for leveraging blockchain technology to enhance demand forecasting in supply chain management 利用区块链技术提高供应链管理需求预测的系统动力学方法

Supply Chain Analytics

Pub Date : 2025-03-26 DOI: 10.1016/j.sca.2025.100115

SeyyedHossein Barati

This study investigates the impact of blockchain technology on demand forecasting and the associated costs in supply chain management using system dynamics modeling. With the increasing complexity and challenges of demand prediction in modern supply chains, the potential of blockchain to enhance the accuracy of demand forecasting and reduce related costs has become a critical area of interest. The research employs system dynamics to model the interrelationships between key factors such as blockchain adoption, data accuracy, transaction transparency, and supply chain performance. The findings highlight that blockchain integration significantly improves demand forecasting accuracy by ensuring real-time data sharing, reducing information asymmetry, and enhancing decision-making processes. Moreover, the simulation results show that blockchain adoption can reduce forecasting errors, thereby lowering operational costs. This research contributes to the existing literature by demonstrating the practical benefits of blockchain in supply chain operations, offering valuable insights for practitioners and researchers. It also provides a foundation for future studies to explore the scalability of blockchain in different sectors and its broader applications in optimizing supply chain functions.

本研究利用系统动力学模型探讨区块链技术对供应链管理中需求预测及相关成本的影响。随着现代供应链中需求预测的复杂性和挑战的增加，区块链在提高需求预测准确性和降低相关成本方面的潜力已成为一个重要的研究领域。该研究采用系统动力学来模拟区块链采用、数据准确性、交易透明度和供应链绩效等关键因素之间的相互关系。研究结果强调，区块链集成通过确保实时数据共享、减少信息不对称和增强决策过程，显著提高了需求预测的准确性。此外，仿真结果表明，采用区块链可以减少预测误差，从而降低运营成本。本研究通过展示区块链在供应链运营中的实际效益，对现有文献做出了贡献，为从业者和研究人员提供了有价值的见解。为进一步研究区块链在不同领域的可扩展性及其在供应链功能优化中的更广泛应用奠定了基础。

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引用次数: 0

A scoping review of export supply chain efficiency frameworks for perishable horticultural products 易腐园艺产品出口供应链效率框架的范围审查

Supply Chain Analytics

Pub Date : 2025-03-13 DOI: 10.1016/j.sca.2025.100112

Sabrina Haque, Delwar Akbar, Susan Kinnear, Azad Rahman

Exporting perishable horticulture products is a complex undertaking which can create inefficiencies within export supply chains (ESCs). It is vital to identify an efficient ESC framework to enhance the competitiveness of horticultural exports. Whilst prior research has focussed on selected efficiency indicators such as logistics or consumer perceptions, a holistic framework specific to horticultural produce is yet to be developed. This study develops such a framework by identifying the key dimensions and indicators of an efficient horticulture ESC using a systems approach. A scoping review was conducted with a total of 62 studies meeting the inclusion criteria. Key efficiency indicators were grouped under seven dimensions of an ESC: economic, time, management, network, innovation, market and eco-efficiency. Careful management of these domains is required to achieve an efficient ESC for perishable horticulture that can deliver on consumer satisfaction and meet sustainability outcomes. We show that management, time, and network efficiencies should be applied at every stage of ESC, whereas other efficiency dimensions are required only at certain stages. This research fills a gap in understanding efficient horticultural ESC frameworks for stakeholders such as researchers, industry bodies, growers, distributors, processors, exporters, retailers and policymakers.

出口易腐烂的园艺产品是一项复杂的工作，可能导致出口供应链（esc）效率低下。确定一个有效的ESC框架以提高园艺出口的竞争力至关重要。虽然先前的研究侧重于选定的效率指标，如物流或消费者观念，但尚未开发出专门针对园艺产品的整体框架。本研究通过使用系统方法确定高效园艺ESC的关键维度和指标，开发了这样一个框架。对符合纳入标准的62项研究进行了范围审查。关键效率指标分为ESC的七个维度：经济、时间、管理、网络、创新、市场和生态效率。需要对这些领域进行仔细管理，以实现易腐园艺的有效ESC，从而实现消费者满意度和可持续性成果。我们表明，管理、时间和网络效率应应用于ESC的每个阶段，而其他效率维度仅在某些阶段需要。这项研究填补了研究人员、行业机构、种植者、分销商、加工商、出口商、零售商和政策制定者等利益相关者在理解有效的园艺ESC框架方面的空白。

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引用次数: 0

An integrated fuzzy-AHP and fuzzy-DEMATEL approach for analyzing sustainable supply chain factors in the mining industry 基于模糊层次分析法和模糊dematel方法的矿业可持续供应链因素分析

Supply Chain Analytics

Pub Date : 2025-03-13 DOI: 10.1016/j.sca.2025.100113

Arpan Paul, Siba Sankar Mahapatra

Rapid industrialization necessitates the utmost balance among economic, environmental, and social performance of manufacturing industries for long-term sustainability. The equity among the performance criteria can be maintained through the adoption of sustainability in the industries’ supply chain. As mining industry is viewed as one of the most polluting industries, it becomes apparent to rectify mining activities through the integration of sustainability into its supply chain. However, the sustainability drive must not compromise long-term economic performance. To address this issue, a case-based study is attempted in the present work to identify and analyze the drivers and barriers responsible for implementing sustainability in the supply chain of Indian mining industries. An integrated approach of the fuzzy analytical hierarchy process (F-AHP) and fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) has been proposed to determine the priority and interdependency between the sustainability implementation factors. Also, sensitivity analysis has been carried out to understand the factors significantly influencing system sustainability. The study reveals that “environmental certification and government regulation” and “lack of regulations on sustainability”’ are the most crucial driver and barrier, respectively, to adopt sustainability in the Indian mining industry. The findings help policy planners provide a framework for promoting sustainable practices. Also, the study provides a robust methodology that can be applied to similar industries interested in enhancing sustainability adoption.

快速工业化要求制造业在经济、环境和社会绩效之间取得最大程度的平衡，以实现长期可持续发展。通过在工业供应链中采用可持续发展理念，可以保持绩效标准之间的公平性。由于采矿业被视为污染最严重的行业之一，因此显然需要通过将可持续发展融入供应链来纠正采矿活动。然而，可持续发展的驱动力绝不能损害长期经济效益。为解决这一问题，本研究尝试以案例为基础，识别和分析在印度采矿业供应链中实施可持续发展的驱动因素和障碍。研究提出了一种模糊分析层次过程（F-AHP）和模糊决策试验和评估实验室（F-DEMATEL）的综合方法，以确定可持续性实施因素的优先顺序和相互依存关系。此外，还进行了敏感性分析，以了解对系统可持续性有重大影响的因素。研究显示，"环境认证和政府监管 "和 "缺乏可持续发展方面的法规 "分别是印度采矿业采用可持续发展的最关键驱动因素和障碍。研究结果有助于政策规划者提供促进可持续发展实践的框架。此外，这项研究还提供了一种稳健的方法，可应用于有兴趣加强可持续性应用的类似行业。

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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 : 2025-02-27 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 multi-objective supply chain model for disaster relief optimization using neutrosophic programming and blockchain-based smart contracts 利用中性编程和区块链智能合约优化救灾的多目标供应链模型

Supply Chain Analytics

Pub Date : 2025-02-25 DOI: 10.1016/j.sca.2025.100107

Alisha Roushan , Amrit Das , Anirban Dutta , Uttam Kumar Bera

Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters, specifically flash floods triggered by cloudburst events. We propose a multi-objective supply chain model that minimizes both cost and time during emergencies by employing drones for rapid response and delivery to inaccessible areas. The model leverages Dijkstra’s algorithm to identify the shortest emergency routes and integrates Neutrosophic Compromise Programming (NCP) and the Weighted Sum Method (WSM) to optimize drone deployment for cost-effectiveness and timely intervention. Pentagonal Type-2 Fuzzy Variables (PT2FV) manage uncertainty and accurately represent real-world disasters. The study also introduces a smart contract framework to enhance transparency and accountability in logistics and rescue operations. These smart contracts govern the assignment of drone-based delivery tasks, ensuring that supplies are optimally allocated and transported via the most efficient routes. The system verifies task completion and maintains a transparent record of the logistics process. The robustness of the model is validated through sensitivity analysis, while the smart contract system is confirmed through unit testing, demonstrating its reliability under varied conditions. This work aligns with Industry 5.0, integrating human-centric decision-making, drones, intelligent systems, and blockchain-based smart contracts to automate and effectively manage disaster, facilitating seamless collaboration between humans and machines.

高效的供应链模式对于确保灾害管理中的快速医疗干预和及时提供基本用品至关重要。本研究的重点是优化气象灾害，特别是由暴雨事件引发的山洪灾害的救灾工作。我们提出了一个多目标供应链模型，在紧急情况下，通过使用无人机快速响应和交付到难以到达的地区，最大限度地减少成本和时间。该模型利用Dijkstra算法来确定最短的应急路线，并集成中性妥协规划（NCP）和加权和方法（WSM）来优化无人机部署，以实现成本效益和及时干预。五边形2型模糊变量（PT2FV）管理不确定性并准确地代表现实世界的灾难。该研究还引入了一个智能合约框架，以提高物流和救援行动的透明度和问责制。这些智能合约管理着无人机配送任务的分配，确保物资通过最有效的路线得到最佳分配和运输。该系统验证任务的完成情况，并保持物流过程的透明记录。通过灵敏度分析验证模型的鲁棒性，通过单元测试验证智能合约系统，验证其在不同条件下的可靠性。这项工作与工业5.0相一致，整合了以人为中心的决策、无人机、智能系统和基于区块链的智能合约，以自动化和有效地管理灾难，促进人与机器之间的无缝协作。

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引用次数: 0

Exploring the potential of industry 4.0 in manufacturing and supply chain systems: Insights and emerging trends from bibliometric analysis 探索工业4.0在制造和供应链系统中的潜力：来自文献计量分析的见解和新兴趋势

Supply Chain Analytics

Pub Date : 2025-02-24 DOI: 10.1016/j.sca.2025.100108

Assiya Zahid , Patrice Leclaire , Lamia Hammadi , Roberta Costa-Affonso , Abdessamad El Ballouti

The synergy between Industry 4.0, production, and supply chain management is transforming industrial ecosystems, enabling more intelligent, automated, and interconnected systems. This study presents a comprehensive bibliometric analysis of academic literature on Industry 4.0 technologies within these domains from 2011 to 2024. It aims to identify key research trends, major contributors, and emerging themes shaping this field. A systematic search in the Scopus database initially retrieved 679 records, with 104 selected based on inclusion and exclusion criteria. The analysis follows the PRISMA methodology for systematic reviews and utilizes Biblioshiny and VOSviewer to examine co-authorship networks, keyword co-occurrences, and citation patterns, providing a detailed assessment of research performance. The results indicate a significant increase in research output, particularly in the integration of Internet of Things, Artificial Intelligence, and big data analytics into production and supply chain systems. These technologies contribute to enhanced operational efficiency, product quality, and value chain management. The study also identifies the most influential authors, institutions, and countries. Furthermore, the findings highlight the increasing importance of emerging technologies such as IoT and blockchain in promoting sustainability, alongside the rising recognition of social and environmental dimensions in supply chain management. By mapping research trends and identifying key contributions, this bibliometric review offers valuable insights for researchers, practitioners, and policymakers. It underscores the transformative potential of Industry 4.0 in reshaping production and supply chains while outlining future research directions, particularly regarding technological integration, sustainability challenges, and the necessity of global cooperation to advance smart and sustainable supply chains.

工业4.0、生产和供应链管理之间的协同作用正在改变工业生态系统，使系统更加智能、自动化和互联。本研究对这些领域从2011年到2024年有关工业4.0技术的学术文献进行了全面的文献计量分析。它的目的是确定关键的研究趋势，主要贡献者和新兴主题塑造这一领域。在Scopus数据库中进行系统搜索，最初检索了679条记录，根据纳入和排除标准选择了104条。分析遵循PRISMA方法进行系统评价，并利用Biblioshiny和VOSviewer来检查共同作者网络，关键词共同出现和引用模式，提供研究绩效的详细评估。研究结果表明，研究产出显著增加，特别是在将物联网、人工智能和大数据分析整合到生产和供应链系统方面。这些技术有助于提高运营效率、产品质量和价值链管理。该研究还确定了最具影响力的作者、机构和国家。此外，研究结果还强调了物联网和区块链等新兴技术在促进可持续发展方面的重要性日益增加，同时人们对供应链管理中社会和环境因素的认识也在不断提高。通过绘制研究趋势和确定关键贡献，本文献计量学综述为研究人员、从业人员和政策制定者提供了有价值的见解。报告强调了工业4.0在重塑生产和供应链方面的变革潜力，同时概述了未来的研究方向，特别是在技术整合、可持续性挑战以及推动智能和可持续供应链的全球合作必要性方面。

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引用次数: 0

An integrated pythagorean fuzzy delphi-AHP-CoCoSo approach for exploring barriers and mitigation strategies for sustainable supply chain in the food industry 探索食品行业可持续供应链障碍和缓解战略的综合 pythagorean 模糊德尔菲-AHP-CoCoSo 方法

Supply Chain Analytics

Pub Date : 2025-02-22 DOI: 10.1016/j.sca.2025.100105

Neha Gupta , Pratibha Garg , Nidhi Ahuja

This study analyses the barriers hindering the implementation of sustainable supply chain practices within the food industry while proposing effective mitigation strategies. Employing an extensive literature review as a foundation, the study utilizes the Pythagorean fuzzy Delphi approach to identify and finalize key barriers and strategies. The identified barriers are categorized into six main groups: technological, economic, regulatory, environmental, organizational, and market barriers. The study employs the Pythagorean fuzzy Analytic Hierarchy Process (AHP) technique to prioritize these barriers, assigning priority weights to each barrier. Subsequently, eight mitigation strategies are identified and ranked based on these priority weights using the Pythagorean fuzzy Combined Compromise Solution (CoCoSo) technique. The study reveals that a lack of professional expertise is the most significant barrier to implementing sustainability practices in the food supply chain. Sustainable technology development is recommended as the top mitigation strategy, followed by others. This research contributes valuable insights into the food industry’s challenges in adopting sustainable supply chain practices. It provides a roadmap for effective mitigation strategies to foster environmental responsibility and resilience within the sector.

本研究分析了阻碍食品行业实施可持续供应链实践的障碍，同时提出了有效的缓解策略。采用广泛的文献综述为基础，本研究利用毕达哥拉斯模糊德尔菲法来识别和确定关键障碍和策略。已确定的障碍可分为六大类：技术、经济、监管、环境、组织和市场障碍。该研究采用毕达哥拉斯模糊层次分析法（AHP）技术对这些障碍进行优先级排序，为每个障碍分配优先级权重。随后，使用毕达哥拉斯模糊组合折衷解决方案（CoCoSo）技术，根据这些优先级权重确定了八种缓解策略并对其进行了排序。研究表明，缺乏专业知识是在食品供应链中实施可持续性实践的最大障碍。建议将可持续技术发展作为首要缓解战略，其次是其他战略。这项研究为食品行业在采用可持续供应链实践方面的挑战提供了有价值的见解。它为有效的缓解战略提供了路线图，以促进该部门的环境责任和复原力。

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引用次数: 0