Supply Chain Analytics最新文献_第2页

A supply chain analytics approach for optimizing milk collection routing in multi-depot networks 在多仓库网络优化牛奶收集路线的供应链分析方法

Supply Chain Analytics Pub Date : 2025-04-17 DOI: 10.1016/j.sca.2025.100123

Mattia Neroni , Marta Rinaldi

{"title":"A supply chain analytics approach for optimizing milk collection routing in multi-depot networks","authors":"Mattia Neroni , Marta Rinaldi","doi":"10.1016/j.sca.2025.100123","DOIUrl":"10.1016/j.sca.2025.100123","url":null,"abstract":"<div><div>This study presents a supply chain model for optimizing milk collection routing in multi-depot networks. The problem consists of a fleet of vehicles that leaves their depots (i.e., typically the driver’s houses), visits an assigned set of farms to collect the raw milk, and delivers it to the processing plant. This problem has not yet been formulated explicitly in the literature, and it can be classified in the middle between the Team Orienteering Problem (TOP) and the Multi-Depot Vehicle Routing Problem (MDVRP) with heterogeneous vehicles. However, it cannot be reduced to any previously mentioned problems before introducing slight modifications and additional constraints to the mathematical formulation. We introduce a new formulation and propose six heuristic algorithms to minimize the distance covered in milk collection in the dairy sector. The proposed solutions are validated by using new benchmarks and tested in a set of real case applications. Computational experiments on real-life data are performed to investigate the performance of the heuristics varying the milk demand. The results demonstrate the applicability of the proposed approach to the real world and identify the best algorithm in terms of solution quality and computational time.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A meta-analysis assessment of adaptive and transformative approaches to supply chain resilience 供应链弹性的适应性和变革性方法的元分析评估

Supply Chain Analytics Pub Date : 2025-04-16 DOI: 10.1016/j.sca.2025.100124

Ruhaimatu Abudu , Emmanuel Anu Thompson , Frank Selase Dzawu , Alfredo Roa-Henriquez

{"title":"A meta-analysis assessment of adaptive and transformative approaches to supply chain resilience","authors":"Ruhaimatu Abudu , Emmanuel Anu Thompson , Frank Selase Dzawu , Alfredo Roa-Henriquez","doi":"10.1016/j.sca.2025.100124","DOIUrl":"10.1016/j.sca.2025.100124","url":null,"abstract":"<div><div>Amid rising global disruptions, including pandemics, geopolitical conflicts, and economic shocks, supply chain resilience has become a strategic imperative. Despite growing attention, limited synthesis exists on how resilience strategies affect supply chain performance under varying conditions. This study addresses that gap through a meta-analysis of 52 empirical studies comprising 236 independent samples and 22,955 observations. Two key strategies, Adaptive Resilience (AR), focused on rapid recovery, and Transformative Resilience (TR), centered on long-term structural adaptation, were examined concerning resilience antecedents, contextual moderators, and outcome metrics. AR was found to be more closely associated with short-term operational recovery, while TR showed stronger links to sustainability and innovation. When applied jointly, these strategies yielded significantly improved performance outcomes compared to their separate implementation. Supply chain complexity emerged as a critical moderating factor, shaping the effectiveness of each strategy based on network characteristics. This study contributes a comprehensive, evidence-based framework that links resilience strategies to their drivers and impacts. Practical implications are also offered by guiding managers on tailoring resilience investments according to the type of disruption and structural features of their supply chains. The findings support the design of more agile and robust supply chains capable of withstanding future global uncertainties.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical risk mitigation framework for steel fabrication supply chains using fuzzy inference and house of risk 基于模糊推理和风险屋的钢铁制造供应链风险缓解分析框架

Supply Chain Analytics Pub Date : 2025-04-10 DOI: 10.1016/j.sca.2025.100122

Fadhil Adita Ramadhan , Agus Mansur , Nashrullah Setiawan , Mohd Rizal Salleh

{"title":"An analytical risk mitigation framework for steel fabrication supply chains using fuzzy inference and house of risk","authors":"Fadhil Adita Ramadhan , Agus Mansur , Nashrullah Setiawan , Mohd Rizal Salleh","doi":"10.1016/j.sca.2025.100122","DOIUrl":"10.1016/j.sca.2025.100122","url":null,"abstract":"<div><div>This study integrates the House of Risk (HOR) approach with the Fuzzy Inference System (FIS) to manage supply chain risks in steel fabrication by addressing market uncertainties and operational challenges to enhance stability and productivity. The study begins with risk identification using HOR and the calculation of fuzzy aggregate risk priority (FARP) based on severity and frequency. A Mamdani based FIS is then applied to prioritize risks and develop mitigation strategies, leveraging data from expert interviews and literature reviews. The findings highlight supplier order failures as the top risk with the highest FARP score, leading to the proposal of 50 mitigation actions, including managed inventory systems and supplier diversification, to strengthen supply chain resilience and reduce vulnerabilities. However, this study is limited to the steel fabrication industry and relies on expert opinions and secondary data, which may affect generalizability. Future research can apply this approach to other industries and incorporate realtime data for validation. The proposed mitigation strategies offer actionable insights for supply chain managers, helping companies improve operational stability and adapt effectively to market uncertainties. By introducing an integrated HOR and FIS approach, this study provides a dynamic and systematic framework for comprehensive supply chain risk management, offering original insights to the field.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A text mining study of competencies in modern supply chain management with skillset mapping 现代供应链管理能力的文本挖掘研究与技能集映射

Supply Chain Analytics Pub Date : 2025-04-05 DOI: 10.1016/j.sca.2025.100117

Parminder Singh Kang , Rickard Enstroem , Bhawna Bhawna , Owen Bennett

{"title":"A text mining study of competencies in modern supply chain management with skillset mapping","authors":"Parminder Singh Kang , Rickard Enstroem , Bhawna Bhawna , Owen Bennett","doi":"10.1016/j.sca.2025.100117","DOIUrl":"10.1016/j.sca.2025.100117","url":null,"abstract":"<div><div>This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing, and machine learning techniques, including Latent Dirichlet Allocation and Term Frequency-Inverse Document Frequency. The findings reveal that job postings primarily emphasize traditional operational skills such as logistics, inventory control, and customer relationship management. However, there is a noticeable underrepresentation of advanced technological competencies, such as machine learning, data analytics, and automation, which are increasingly critical in today's supply chain environment. This gap highlights the need for greater alignment between job market demands and supply chain management's evolving digital transformation landscape. The study identifies key themes, including technical, managerial, and soft skills integration, emphasizing adaptability, data literacy, and strategic decision-making. The results suggest a misalignment between the competencies highlighted in job advertisements and the skills necessary for managing the complexities of a digitalized supply chain. This research offers practical recommendations for industry leaders to refine hiring strategies, academic institutions to modernize curricula, and job platforms to better showcase emerging skill requirements. Addressing this gap is essential to equip supply chain professionals with the tools and expertise to meet the challenges of a technology-driven future.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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 : 2025-04-04 DOI: 10.1016/j.sca.2025.100121

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

{"title":"Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery","authors":"G. Sakthi Balan , V. Santhosh Kumar , S. Aravind Raj","doi":"10.1016/j.sca.2025.100121","DOIUrl":"10.1016/j.sca.2025.100121","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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