Supply Chain Analytics最新文献_第2页

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 : 2025-04-03 DOI: 10.1016/j.sca.2025.100119

S. Priyan

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 : 2025-04-01 DOI: 10.1016/j.sca.2025.100116

Ishansh Gupta, Adriana Martinez, Sergio Correa, Hendro Wicaksono

{"title":"A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry","authors":"Ishansh Gupta, Adriana Martinez, Sergio Correa, Hendro Wicaksono","doi":"10.1016/j.sca.2025.100116","DOIUrl":"10.1016/j.sca.2025.100116","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A sustainability and profitability optimization model in three-stage green supply chains under uncertainty with competitive and cooperative game dynamics","authors":"Manojit Das , Biswajit Muchi , Shariful Alam , Dipak Kumar Jana","doi":"10.1016/j.sca.2025.100114","DOIUrl":"10.1016/j.sca.2025.100114","url":null,"abstract":"<div><div>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, <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> 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 <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> 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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A multi-objective supply chain optimization model for reliable remanufacturing problems with M/M/m/k queues","authors":"Vahid Hajipour , Shermineh Hadad Kaveh , Fatih Yiğit , Ali Gharaei","doi":"10.1016/j.sca.2025.100118","DOIUrl":"10.1016/j.sca.2025.100118","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A system dynamics approach for leveraging blockchain technology to enhance demand forecasting in supply chain management","authors":"SeyyedHossein Barati","doi":"10.1016/j.sca.2025.100115","DOIUrl":"10.1016/j.sca.2025.100115","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A scoping review of export supply chain efficiency frameworks for perishable horticultural products","authors":"Sabrina Haque, Delwar Akbar, Susan Kinnear, Azad Rahman","doi":"10.1016/j.sca.2025.100112","DOIUrl":"10.1016/j.sca.2025.100112","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An integrated fuzzy-AHP and fuzzy-DEMATEL approach for analyzing sustainable supply chain factors in the mining industry","authors":"Arpan Paul, Siba Sankar Mahapatra","doi":"10.1016/j.sca.2025.100113","DOIUrl":"10.1016/j.sca.2025.100113","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An integrated multi-criteria decision-making model for identifying complexity drivers in the oil and gas supply chain","authors":"Sujan Piya , Yahya Al-Hinai , Nasr Al Hinai , Mohammad Khadem , Mohammad Shamsuzzaman","doi":"10.1016/j.sca.2025.100104","DOIUrl":"10.1016/j.sca.2025.100104","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A multi-objective supply chain model for disaster relief optimization using neutrosophic programming and blockchain-based smart contracts","authors":"Alisha Roushan , Amrit Das , Anirban Dutta , Uttam Kumar Bera","doi":"10.1016/j.sca.2025.100107","DOIUrl":"10.1016/j.sca.2025.100107","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"Exploring the potential of industry 4.0 in manufacturing and supply chain systems: Insights and emerging trends from bibliometric analysis","authors":"Assiya Zahid , Patrice Leclaire , Lamia Hammadi , Roberta Costa-Affonso , Abdessamad El Ballouti","doi":"10.1016/j.sca.2025.100108","DOIUrl":"10.1016/j.sca.2025.100108","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0