Supply Chain Analytics最新文献

{"title":"A comparative study of multi-algorithm optimization for inventory analytics in supply chains","authors":"Oussama Zabraoui,&nbsp;Yahya Hmamou ,&nbsp;Anas Chafi ,&nbsp;Salaheddine Kammouri Alami","doi":"10.1016/j.sca.2025.100154","DOIUrl":"10.1016/j.sca.2025.100154","url":null,"abstract":"<div><div>Effective management of inventory is essential for achieving high service levels, minimizing costs, and maintaining the overall resilience of retail supply chains—particularly in complex, real-world environments. Conventional strategies often prove inadequate because they rely on rigid assumptions or single-technique models that fail to accommodate practical challenges such as fluctuating demand, unpredictable lead times, and disruptions in supply.</div><div>To bridge this gap, our research undertakes a comprehensive comparison of multiple approaches — including Reinforcement Learning (RL), Genetic Algorithms (GA), Deep Learning (DL), Machine Learning (ML), and heuristic techniques — evaluated within a consistent and realistic testing framework based on the Walmart M5 dataset. This dataset offers a robust benchmark, containing multi-store, multi-item sales data that captures seasonal trends, event-driven demand variations, and price sensitivity. We introduce and evaluate an innovative hybrid methodology that combines a Genetic Algorithm with a Deep Q-Network (GA–DQN). The GA component conducts a broad, global search to optimize static inventory parameters such as reorder points and safety stock, while the DQN module learns adaptive, state-aware ordering strategies that can respond to dynamic, uncertain conditions. Our results show that this hybrid GA–DQN model achieves a significant improvement over a standalone DQN baseline—raising the service level from 61% to 94% and simultaneously lowering overall inventory costs. The framework we propose is modular and includes three key components: demand forecasting using Long Short-Term Memory (LSTM) networks to capture temporal sales patterns; GA-based optimization to fine-tune static policy parameters; and RL-driven adaptive control to support responsive, real-time ordering decisions. This integrated approach delivers a scalable, data-driven solution well-suited to the demands of modern retail supply chains, effectively addressing issues such as supplier unreliability, demand uncertainty, and the management of perishable goods.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902459","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}

{"title":"An evaluation of traceability dynamics in dairy supply chains through causal modeling in emerging economies","authors":"Shahab Bayatzadeh ,&nbsp;Hamidreza Talaie","doi":"10.1016/j.sca.2025.100156","DOIUrl":"10.1016/j.sca.2025.100156","url":null,"abstract":"<div><div>Traceability capability to track the history, location, and application of dairy products is crucial for ensuring food safety, quality, and transparency across supply chains. However, its development in emerging economies, particularly in Iran, remains limited due to infrastructural and technological challenges. This study addresses this gap by identifying and analyzing the key factors that influence traceability in Iran’s dairy sector, which plays a critical role in national nutrition and public health. Using a hybrid approach, the fuzzy Delphi method was first applied to refine a set of 19 factors extracted from the literature, validating 14 context-relevant elements based on expert consensus. Subsequently, the fuzzy DEMATEL method, designed to model causal relationships under uncertainty, was used to determine interdependencies among these factors. The results highlight food safety and quality, supply chain process management, data analysis and forecasting, and data integration as the most influential drivers of traceability. Meanwhile, competitive advantage, sourcing transparency, and environmental sustainability were found to be dependent outcomes. This research contributes a contextualized, expert-based framework tailored to the Iranian dairy industry and offers practical implications for improving transparency, reducing waste, and building consumer trust. The methodology and findings are transferable to other developing country contexts facing similar challenges.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878409","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}

{"title":"A metaheuristic approach for optimizing drone routing in healthcare supply chains","authors":"Tejinder Singh Lakhwani,&nbsp;Yerasani Sinjana","doi":"10.1016/j.sca.2025.100153","DOIUrl":"10.1016/j.sca.2025.100153","url":null,"abstract":"<div><div>Healthcare logistics continue to encounter significant challenges in the timely and reliable delivery of blood bags, mainly due to urban traffic congestion, rugged terrain, and the perishability of medical supplies. Conventional transportation systems frequently fall short of meeting the stringent temporal and thermal requirements inherent to healthcare supply chains. Unmanned Aerial Vehicles (UAVs), or drones, offer a compelling alternative; however, their effective deployment is hindered by constraints such as limited payload capacity, restricted flight range, narrow delivery time windows, and evolving regulatory frameworks. This study proposes the HybridNGS algorithm, a hybrid metaheuristic framework that integrates Nearest Neighbour (NN) for solution initialization, Genetic Algorithm (GA) for global search, and Simulated Annealing (SA) for local refinement, to address the Drone Routing Problem (DRP) in blood logistics. The model incorporates domain-specific constraints, including blood-type compatibility, energy-aware routing, and cold-chain preservation. Empirical evaluations using synthetic and real-world datasets comprising 20–100 hospitals reveal that HybridNGS consistently outperforms benchmark approaches such as GRASP and TSP-D, achieving up to 20 % cost savings, 15 % reduction in drone usage, and notable energy efficiency. The algorithm demonstrates strong scalability and robustness under variable demand and environmental conditions. It is a viable solution for enhancing accessibility, reliability, and sustainability in routine and emergency healthcare delivery systems.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902460","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}

{"title":"A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics","authors":"Mehdi Soltani Tehrani,&nbsp;Siamak Noori,&nbsp;Ehsan Dehghani","doi":"10.1016/j.sca.2025.100155","DOIUrl":"10.1016/j.sca.2025.100155","url":null,"abstract":"<div><div>The escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an efficient supply chain. This study introduces a comprehensive two-stage optimization framework to advance the design and operation of biofuel supply chains. In the initial stage, a novel hybrid methodology integrates data envelopment analysis with artificial neural networks to identify optimal sites for agricultural waste collection facilities. This approach combines the performance assessment strengths of data envelopment analysis with the predictive capabilities of neural networks, enabling a data-informed site selection process. The second stage employs a mixed-integer linear programming model to optimize a closed-loop biofuel supply chain under uncertain conditions, targeting both cost reduction and minimized carbon emissions. A probabilistic scenario-based approach is utilized to address uncertainties, enhancing the model’s real-world applicability. Additionally, the Lagrangian relaxation technique is implemented to achieve precise solutions while preserving computational efficiency. For large-scale scenarios, the study leverages the non-dominated sorting genetic algorithm and multi-objective simulated annealing to generate near-optimal solutions. A practical case study validates the proposed framework and provides decision-makers with clear and actionable strategies to optimize site planning, reduce operational costs, and enhance environmental sustainability in biofuel supply chain management.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852993","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}

{"title":"An artificial intelligence framework for recycling dormant and obsolete inventory in supply Chains","authors":"Youssef Raouf ,&nbsp;Zoubida Benmamoun ,&nbsp;Hanaa Hachimi","doi":"10.1016/j.sca.2025.100152","DOIUrl":"10.1016/j.sca.2025.100152","url":null,"abstract":"<div><div>In the automotive sector, excess inventory increases costs and prevents progress toward Sustainable Development Goals, particularly those related to Industry, Innovation, and Infrastructure (SDG 9) and Responsible Consumption and Production (SDG 12). This study introduces an innovative approach to converting obsolete or recycled dormant inventory into a stock that meets customer demand and is marketable by examining the real case of an automotive manufacturer. The optimization, driven by an artificial intelligence tool, transforms at-risk inventory into demand-responsive stock. Results indicate that the tool can modernize up to 84,31 % of the affected inventory, offering substantial benefits, including reduced storage costs and the freeing up of strategic space for new opportunities. This method enhances supply chain resilience and sustainability by reducing waste, improving resource efficiency, and boosting adaptability to disruptions. This paper explores how supply chain innovations in this field address economic, environmental, and social imperatives. It draws on quantitative research into the role of advanced analytics and artificial intelligence technologies in inventory innovation to advance global goals for more resilient and sustainable supply chains.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841529","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}

{"title":"A machine learning approach to dynamic pricing in multi-channel transportation supply chains","authors":"Amirsalar Ghaffari ,&nbsp;Mohsen Afsharian ,&nbsp;Ata Allah Taleizadeh","doi":"10.1016/j.sca.2025.100151","DOIUrl":"10.1016/j.sca.2025.100151","url":null,"abstract":"<div><div>Effective pricing strategies are critical for optimizing revenue and maintaining competitiveness in transportation supply chains, particularly in multi-channel environments. This paper presents a machine learning-driven dynamic pricing approach designed to optimize ticket prices across multiple transportation modes, service classes, and sales channels. In particular, the proposed approach integrates predictive analytics and optimization techniques to estimate customer demand, price sensitivity, and revenue potential while accounting for operational constraints such as capacity limits and market share requirements. Machine learning enables the optimization model to dynamically adjust pricing strategies based on historical demand patterns and real-time market fluctuations. To demonstrate its applicability, the approach is applied in a case study in the transportation sector, illustrating its role in optimizing pricing decisions. Additionally, sensitivity analysis highlights the model’s robustness against capacity changes, demand fluctuations, and pricing constraints. The findings emphasize the role of supply chain analytics in enhancing pricing strategies, making them more adaptive, data-driven, and resilient to market dynamics.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772430","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}

{"title":"A simulation-based optimization approach for sustainable energy supply chain transitions","authors":"Zakka Ugih Rizqi","doi":"10.1016/j.sca.2025.100150","DOIUrl":"10.1016/j.sca.2025.100150","url":null,"abstract":"<div><div>Emissions are emitted throughout the energy supply chain. In demand sector, transitioning from fossil fuel-based vehicles to Electric Vehicles (EVs) is a key step. However, the growth of EVs will lead to higher emissions if, in supply sector, the energy sources rely mainly on Non-Renewable Energy Sources (NRES), forcing to transition to Renewable Energy Sources (RES). While managing these transitions, it is also important to make sure that energy requirements can still be fulfilled which is driven by population dynamics. Thus, this complex interdependence indicates the importance of using systemic framework, although most previous studies focusing on one of transitions which can lead to policy misalignment and unintended trade-offs of sustainability performance. This research proposes a System Dynamics model for assessing the impact of those three interplaying factors on sustainability performance including macroeconomic, job availability, and total emissions, followed by Response Surface Methodology (RSM) for performing efficient optimization. A case study from the United States is used. Through simulation and statistical analysis, 8 insightful propositions are generated. Subsequently, the metamodels based on second-order regression are developed, forming multi-objective non-linear programming revealing the optimal growth rates for sustainable transition which are very useful for helping the policy makers to make more informed decisions toward a sustainable energy system.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721700","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}

{"title":"A systematic review of food accessibility in the food supply chains","authors":"Bochi Liu ,&nbsp;Mengfei Chen ,&nbsp;Mohamed Kharbeche ,&nbsp;Laoucine Kerbache ,&nbsp;Mohamed Haouari ,&nbsp;Xi Gu ,&nbsp;Wenyuan Wang ,&nbsp;Weihong Guo Grace","doi":"10.1016/j.sca.2025.100149","DOIUrl":"10.1016/j.sca.2025.100149","url":null,"abstract":"<div><div>Food accessibility, covering physical, economic, and social dimensions, is a core pillar of food security and depends strongly on food supply chains (FSCs). Previous reviews usually examined FSCs without discussing accessibility, or discussed accessibility outside the FSC context. We close that gap by making the first systematic review that explicitly links the two topics. We screened 136 studies and conducted bibliometric-performance and science-mapping analyses to identify research topics and trends. We synthesized diverse definitions and measurements of food accessibility, analyzed barriers affecting food accessibility, and established a three-tier taxonomy that maps specific barriers onto the three dimensions of food accessibility and five barrier classes. For each barrier class, we traced the causal chain and summarized the interventions reported in the literature. A brief comparison between sub-Saharan Africa and Western Europe shows that barriers and interventions vary by region. Based on these findings, we present a decision matrix that links barriers to actionable interventions and analytical tools. The review identifies three research gaps: (1) multidimensional measurement of accessibility, (2) stronger attention to equity, and (3) wider use of analytics-driven decision support tools. These insights offer strategic guidance for future research and practice aimed at enhancing food accessibility through FSC innovations.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710954","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}

{"title":"A causal artificial intelligence model for payment delay optimisation in supply chain financing","authors":"Lingxuan Kong ,&nbsp;Alexandra Brintrup","doi":"10.1016/j.sca.2025.100138","DOIUrl":"10.1016/j.sca.2025.100138","url":null,"abstract":"<div><div>Supply chain financing (SCF) has become a popular approach for small- and medium-sized enterprises (SMEs) to improve financial resilience. Payment delays within SCF have emerged as a critical operational challenge for both suppliers and SCF providers. This paper aims to integrates causal AI modelling to proposed a framework to discover and optimise the operational treatments for mitigating payment delays in SCF. The proposed framework combines causal machine learning methods such as backdoor adjustment with Inverse Probability Weighting (IPW), and Double Machine Learning models (DoubleML). The proposed causal AI framework implements data-driven learning for the processes of causal discovery, causal effect estimation, and the optimisation of policy trees. This proposed framework aims to establish a cohesive method designed to identify potential treatment effects and assist in making operational decisions to mitigate payment delays. The effectiveness of the proposed framework is demonstrated through a case study on aerospace supply chain network. The generalisability and the industrial insights associated with the case study results have been analysed.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687167","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}

{"title":"An explainable decision model for selecting facility locations in supply chain networks","authors":"Tin-Chih Toly Chen ,&nbsp;Yu-Cheng Wang ,&nbsp;Yi-Chi Wang","doi":"10.1016/j.sca.2025.100148","DOIUrl":"10.1016/j.sca.2025.100148","url":null,"abstract":"<div><div>Suitable facility location selection for customer-required capacity localization is an emerging topic in semiconductor supply chain management. However, this topic has not been thoroughly investigated. For this reason, an explainable artificial intelligence (XAI)-interpreted fuzzy group decision-making (FGDM) approach is proposed in this study to assist a wafer foundry company in selecting suitable facility locations for customer-required capacity localization. The XAI-interpreted FGDM approach aims to overcome the shortcomings of existing visualization tools and techniques for explaining the facility location selection process. To this end, several new visualization tools and methods have been proposed, including hanging gradient bar charts, gradient bidirectional scatterplots, and hanging gradient bar charts for traceable aggregation. After applying the XAI-interpreted FGDM approach to a real case, the new XAI tools enhanced the explainability of the facility location selection process and results. The advantage over the existing XAI tools was up to 36 %. In addition, Shapley additive explanations (SHAP) analysis results showed that the factors that impact the assessment results most may be inconsistent with the original judgments of domain experts.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662268","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}