Supply Chain Analytics最新文献

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

{"title":"An analytical review of predictive methods for delivery delays in supply chains","authors":"Norman Müller, Peter Burggräf, Fabian Steinberg, Carl René Sauer, Maximilian Schütz","doi":"10.1016/j.sca.2025.100130","DOIUrl":"10.1016/j.sca.2025.100130","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169020","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 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 : 2025-05-20 DOI: 10.1016/j.sca.2025.100131

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

{"title":"An optimization framework for emergency supply chains prioritizing elderly populations during pandemics","authors":"Behzad Mosallanezhad , Neale R. Smith , Fatemeh Gholian-Jouybari , Mostafa Hajiaghaei-Keshteli","doi":"10.1016/j.sca.2025.100131","DOIUrl":"10.1016/j.sca.2025.100131","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125177","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

Optimization-based model of a circular supply chain for coffee waste 基于优化的咖啡废弃物循环供应链模型

Supply Chain Analytics Pub Date : 2025-04-30 DOI: 10.1016/j.sca.2025.100126

Hanieh Zohourfazeli , Ali Sabaghpourfard , Amin Chaabane , Armin Jabbarzadeh

{"title":"Optimization-based model of a circular supply chain for coffee waste","authors":"Hanieh Zohourfazeli , Ali Sabaghpourfard , Amin Chaabane , Armin Jabbarzadeh","doi":"10.1016/j.sca.2025.100126","DOIUrl":"10.1016/j.sca.2025.100126","url":null,"abstract":"<div><div>Spent coffee grounds (SCG) waste poses significant environmental challenges, including greenhouse gas emissions and contamination risks. However, the existing reverse logistics (RL) systems remain inefficient, costly, and prone to contamination. Although previous studies have explored RL strategies, economically viable logistics models for small-scale SCG operations remain underdeveloped. However, the role of digitalization in optimizing SCG collection has not yet been explored. This study addresses these gaps by developing and evaluating sustainable business models that integrate circular economy principles with Industry 4.0. A mixed-integer linear programming (MILP) model was formulated to optimize the location, allocation, and routing decisions for “circular coffee shops, ” which serve as local collection and preprocessing nodes. Using real data from 1000 coffee shops in Montreal, three case scenarios were analyzed to assess the impact of pre-drying technologies and smart logistics on cost reduction and environmental performance. The results show that, while smart bins and real-time data analytics improve network efficiency and sustainability, the strategic placement of pre-drying technologies significantly reduces transportation and processing costs. By introducing a novel framework that integrates digitalization and collaborative waste management, this study advances SCG valorization and minimizes waste-related environmental impact. The findings offer actionable strategies for municipalities and food service stakeholders, providing a scalable, data-driven approach to promote the adoption of circular economy principles in urban organic waste management.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928288","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 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 : 2025-04-25 DOI: 10.1016/j.sca.2025.100125

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

{"title":"A bibliometric analysis of industry 5.0 and healthcare supply chain research: Emerging opportunities and future challenges","authors":"Rajesh Matha , Subhodeep Mukherjee , Rashmi Ranjan Panigrahi , Avinash K Shrivastava","doi":"10.1016/j.sca.2025.100125","DOIUrl":"10.1016/j.sca.2025.100125","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907779","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 analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains 基于模糊学习的分析驱动经济订单数量模型

Supply Chain Analytics Pub Date : 2025-04-22 DOI: 10.1016/j.sca.2025.100120

M. Palanivel , M. Venkadesh , S. Vetriselvi , M. Suganya

{"title":"An analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains","authors":"M. Palanivel , M. Venkadesh , S. Vetriselvi , M. Suganya","doi":"10.1016/j.sca.2025.100120","DOIUrl":"10.1016/j.sca.2025.100120","url":null,"abstract":"<div><div>This study presents an advanced Economic Order Quantity inventory model that integrates intuitionistic fuzzy sets and fuzzy learning to enhance decision-making under environmental uncertainty. The model systematically incorporates green technology adoption and accounts for the uncertain impact of emerging technologies on carbon emissions. The proposed framework embeds carbon reduction incentives and tax policies into the inventory decision-making process by leveraging real-time data from environmental regulations and technological advancements. Additionally, the study explores the role of fuzzy learning in optimizing supply chain networks, enabling improved environmental performance, and minimizing carbon emissions. Integrating intuitionistic fuzzy sets, fuzzy learning, green technology, and carbon emission reduction strategies provides a mathematically rigorous approach to developing adaptive inventory models that achieve economic efficiency and environmental sustainability. Numerical experiments are validated by MATLAB software. Based on the numerical experiments, sensitivity analyses are performed on key model parameters to validate the effectiveness of the proposed methodology. The findings are further reinforced by computational simulations and mathematical insights, demonstrating the practical applicability and robustness of the model.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"10 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869763","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 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