Supply Chain Analytics最新文献

{"title":"An integrated analytics approach to multi-project scheduling and material procurement with coordinated hub location","authors":"Sasan Mazaheri ,&nbsp;Mahsa Ahmadi ,&nbsp;Ali Heidari ,&nbsp;Mohammad Hakimi ,&nbsp;Mohammad Khalilzadeh","doi":"10.1016/j.sca.2025.100191","DOIUrl":"10.1016/j.sca.2025.100191","url":null,"abstract":"<div><div>This study presents an integrated framework combining multi-project scheduling, material procurement, and the hub location problem to simultaneously minimize project completion times and overall project and logistics costs. To address the challenge of allocating high-cost renewable resources, we incorporate rental options that balance the trade-off between additional rental expenses and potential project delays. A multi-objective optimization model is developed, integrating the scheduling of multiple projects with coordinated material procurement. To reduce logistics costs and improve delivery efficiency, consolidation hubs are introduced where materials from various suppliers are aggregated before being dispatched to project sites. The model considers the availability of renewable rental resources and storage space capacity while scheduling project activities. Due to the problem's computational complexity, two metaheuristic algorithms NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOSFS (Multi-objective Stochastic Fractal Search) are employed to obtain near-optimal solutions for large-scale scenarios. The proposed approach is validated through a real-world case study involving a bridge construction project and various benchmark instances of different sizes. Results indicate that while NSGA-II performs better on one performance metric, MOSFS consistently outperforms NSGA-II across most criteria, particularly in large-scale problems. The main contributions of this research include integrating project scheduling, material procurement, and hub location within a single unified framework. The model also incorporates renewable rental resources and realistic, type-specific storage capacity constraints that directly affect material flow and the initiation of project activities.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076801","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 analytics-based structural modeling study of circular practices in sustainable supply chains","authors":"Mahsa Farrokhi ,&nbsp;Mauro Gatti ,&nbsp;Aram Bahrini","doi":"10.1016/j.sca.2026.100196","DOIUrl":"10.1016/j.sca.2026.100196","url":null,"abstract":"<div><div>The importance of sustainability has significantly increased for many companies in recent years. However, limited studies have explored the integrated impact of circular economy practices, dynamic capabilities, and fields of action on supply chain sustainability. This study addresses this gap by applying circular economy principles within retail‑equipment supply chains to evaluate their impact on the economic, environmental, and social dimensions of sustainability. A comprehensive review of the relevant literature on circular economy, dynamic capabilities, fields of action, and sustainable supply chains facilitated the identification of 51 key indicators. To fulfill the research objectives, a retail-equipping manufacturing company was selected as the focal point of analysis, and data was collected through a structured questionnaire. We analyzed the data via partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, estimating a reflective–reflective hierarchical component model on n = 131 responses to a 51-item instrument covering circular economy fields, dynamic capabilities, and sustainability outcomes. Results support seven hypotheses: both fields of action (H1–H3) and dynamic capabilities (H4–H6) positively affect economic, environmental, and social performance, and their joint effect is strong (H7: β = 0.73; R² = 0.54). The study presents an integrated, empirically validated model that links circular economy dynamic capabilities and fields of action, thereby extending dynamic capabilities theory into the sustainability domain. The findings suggest that circular economy principles not only support environmental protection and cost efficiency but also enhance organizational agility, stakeholder satisfaction, and resilience. While the specific outcomes may vary across industries, this study offers a robust foundation for future research and practical insights to guide strategic decision-making for companies aiming to transition to more sustainable, circular supply chains. The model provides actionable diagnostics to prioritize investments in design, reverse logistics, and capability development aligned with triple-bottom-line goals in manufacturing.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076800","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 to supply chain inventory optimization with rebates, discounts, and emission controls","authors":"Ankur Saurav,&nbsp;Chandra Shekhar,&nbsp;Vijender Yadav","doi":"10.1016/j.sca.2025.100183","DOIUrl":"10.1016/j.sca.2025.100183","url":null,"abstract":"<div><div>This study develops a sustainable production–inventory model that integrates advance booking, rebate incentives, and green investments under carbon cap-and-trade regulations, aiming to optimize profit while ensuring environmental sustainability. The model accounts for time- and price-sensitive demand influenced by discounts and advertising, and organizes the inventory cycle into four phases: advance booking before production, production with ongoing bookings, normal sales, and a deterioration phase supported by rebate strategies. The study introduces two key innovations: a dual-stage advance booking system with associated maintenance costs and deterioration control via preservation technologies. Carbon emissions arising from production, holding, transportation, and deterioration are compared to a regulatory cap, with penalties for exceeding the limit and credit trading for staying within it. Green technology investments further support emission reduction. The model’s nonlinear optimization problem is solved using the Grey Wolf Optimization algorithm to determine optimal pricing, production quantities, investment levels, and cycle times. Numerical results highlight that integrating sustainability measures, smart pricing strategies, and customer incentives enhances profitability while minimizing environmental impact. This model provides valuable insights for firms and policymakers aiming to align operational efficiency with sustainability objectives under regulatory frameworks.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694208","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 risk-averse multi-objective analytics framework for green supply chain design under uncertainty","authors":"Shahryar Ghorbani ,&nbsp;Javad Nematian ,&nbsp;Figen Yıldırım ,&nbsp;Selahattin Armağan Vurdu","doi":"10.1016/j.sca.2026.100199","DOIUrl":"10.1016/j.sca.2026.100199","url":null,"abstract":"<div><div>In response to increasing concerns about environmental issues, businesses, and industries face pressure to mitigate their negative environmental impacts. Consequently, firms must reevaluate their operations to align with environmental standards. To address both economic and environmental objectives, industries need to green their supply chains. However, uncertainties in the real world, such as economic instability, add complexity to this greening process. This study proposes a novel risk-averse two-stage stochastic model for green supply chain (GSC) design under uncertainty, integrating Conditional Value at Risk (CVaR) with multi-objective programming. The model uses discrete Fuzzy Random Variables (FRVs) to capture both randomness and fuzziness in cost and emission parameters. To solve the model, we apply possibility theory and Fuzzy Chance-Constrained Programming (FCCP) to derive deterministic equivalents for optimistic, pessimistic, and hybrid decision-making attitudes. Numerical results from a flour supply chain case in Iran show that higher risk aversion increases both cost and CO₂ CVaR, while possibility levels affect outcomes differently across models. The approach provides managers with a flexible tool for balancing economic and environmental goals under uncertainty.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394676","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 analytics-driven hybrid method for multi-item demand forecasting in supply chains","authors":"Md. Limonur Rahman Lingkon ,&nbsp;Md. Sanowar Hossain ,&nbsp;Ripon K. Chakrabortty","doi":"10.1016/j.sca.2026.100194","DOIUrl":"10.1016/j.sca.2026.100194","url":null,"abstract":"<div><div>This study proposes a new deep learning (DL)–based approach for multi-item demand forecasting in multi-wave distribution networks. In modern merchandising supply systems, traditional forecasting techniques such as moving averages and autoregressive integrated moving average (ARIMA) models are often inadequate for capturing the dynamic nature of sales and operations planning, as they struggle with non-stationary demand, evolving market conditions, and the growing complexity of supply chain networks, resulting in forecasts that are neither sufficiently accurate nor timely. To address these limitations, this study evaluates a hybrid forecasting framework that combines poly-linear regression (PLR) with a transformer-encoder extended long short-term memory (TE-LSTM) architecture to identify latent demand patterns from large and heterogeneous datasets. An empirical analysis compares the proposed PLR–TE–LSTM model with baseline approaches such as standard LSTM across multiple products and distribution locations, demonstrating consistently superior forecasting performance in multi-product, multi-distribution center settings. The study further examines the operational impact of improved forecasting by evaluating alternative inventory review strategies under a fixed-quantity replenishment policy, showing meaningful improvements in forecast accuracy, order fulfillment performance, inventory holding costs, and service levels. The results indicate that the proposed DL framework enhances inventory-related decision-making by reducing excess inventory and stockout risks, thereby improving efficiency and responsiveness in complex distribution networks.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037467","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 analysis of generative artificial intelligence for supply chain transformation","authors":"Zied Bahroun ,&nbsp;Afef Saihi ,&nbsp;Rami As’ad ,&nbsp;Moayad Tanash","doi":"10.1016/j.sca.2025.100188","DOIUrl":"10.1016/j.sca.2025.100188","url":null,"abstract":"<div><div>Global supply chains face persistent disruptions from geopolitical shocks, sustainability pressures, and volatile demand, creating an increasing need for resilient and transparent operations. Generative Artificial Intelligence (GAI), including Large Language Models (LLMs), Generative Adversarial Networks (GANs), and multimodal generative systems, is emerging as a new decision layer that can generate scenarios, synthetic data, and actionable textual insights rather than only point predictions. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review analyzes 98 peer-reviewed studies on GAI applications in Supply Chain Management (SCM) and, to the best of the authors’ knowledge, provides the first combined thematic and Supply Chain Operations Reference (SCOR) model-based mapping of these applications. Publication activity shows a sharp upward trend, with fewer than five papers published before 2021 and 45 published in 2024 alone. Nearly four-fifths of the reported applications focus on the Plan and Enable processes, while the Make and Return processes account for only 4 % and 1 % of the coded functions, respectively. Although LLM- and Generative Pre-trained Transformer (GPT)-based models underpin over 40 % of the implementations, approximately 45 % of the studies do not fully specify their underlying architectures, indicating methodological immaturity. Reported benefits are concentrated in demand forecasting and risk analysis, supplier screening, logistics visibility, and sustainability analytics; however, most evidence remains at the prototype level and rarely reports system-wide Key Performance Indicators (KPIs). The review concludes with a targeted research agenda that emphasizes longitudinal evaluation, hybrid GAI-driven optimization with digital twin architectures, and governance-by-design frameworks to support the responsible and scalable adoption of GAI in supply chains.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799697","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 analytical framework for enhancing hospital pharmacy supply chain performance using fuzzy rough set theory","authors":"Detcharat Sumrit,&nbsp;Sudarat Katthamaruesee","doi":"10.1016/j.sca.2025.100187","DOIUrl":"10.1016/j.sca.2025.100187","url":null,"abstract":"<div><div>The pharmaceutical supply chain is vital to hospital operations but faces persistent challenges, including drug shortages, regulatory constraints, and inventory inefficiencies. This study explores the application of the Triple-A Supply Chain (TASC) framework agility, adaptability, and alignment to enhance hospital pharmacy performance. A novel hybrid multi-criteria decision-making (MCDM) model is proposed, integrating the Ordinal Priority Approach (OPA) and Aczel–Alsina Weighted Assessment (ALWAS), supported by fuzzy rough set (FRS) theory. This approach improves the reliability of expert judgment under uncertainty, addressing limitations of traditional deterministic models. OPA results identify “data visibility” (agility), “policy and regulatory alignment” (alignment), and “contingency planning” (adaptability) as the most influential TASC criteria. ALWAS analysis highlights “patient-centric inventory coverage,” “stockout frequency of high-priority medications,” and the “critical drug stock availability index” as the most significantly impacted performance indicators. These findings underscore the importance of transparent information flows, regulatory coherence, and resilience planning in achieving responsive and reliable pharmacy operations. Theoretically, the study bridges the resource-based view (RBV) and dynamic capabilities (DC), positioning TASC dimensions as strategic intangible assets that foster adaptability and competitive advantage in uncertain environments. Managerially, the results offer actionable insights for hospital leaders to enhance agility, embed contingency protocols, and align operations with institutional and regulatory priorities. The integration of advanced decision-making tools with strategic supply chain principles provides a comprehensive framework for performance improvement. Beyond hospital pharmacies, the proposed framework offers conceptual and practical value, with potential applications in broader healthcare contexts such as vaccine logistics, emergency preparedness, and digital health systems.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938583","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 Bayesian learning approach for predictive resilience in engineer-to-order supply chains","authors":"Aicha Alaoua ,&nbsp;Mohammed Karim","doi":"10.1016/j.sca.2025.100190","DOIUrl":"10.1016/j.sca.2025.100190","url":null,"abstract":"<div><div>Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things (IoT)-driven adaptation, and Bayesian Neural Network (BNN) updating to conceptually investigate predictive resilience. Using industry-informed synthetic data that reflect realistic variability in lead times and operational disruptions, the framework is demonstrated through Monte Carlo simulation conducted across sixteen parameter scenarios under both moderate and high variability conditions, providing a proof-of-concept tool that illustrates potential operational benefits in EtO supply chains and establishes a foundation for future empirical validation. Results show that the baseline log-normal model performs adequately in stable conditions, its accuracy deteriorates under parameter shifts, and the IoT-adjusted framework reduces sensitivity to variability, while the BNN-enhanced model further improves robustness by jointly modeling aleatoric and epistemic uncertainty. The approach advances supply chain analytics by integrating statistical modeling, real-time IoT feedback, and Bayesian learning, offering theoretical insights and simulation-based, conceptual decision-support implications for supplier management and risk analysis.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938584","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 interpretive analysis of influential drivers for control tower adoption in supply chains","authors":"Magesh kumar M. Nadar ,&nbsp;Angappa Gunasekaran ,&nbsp;Vaibhav S. Narwane","doi":"10.1016/j.sca.2025.100178","DOIUrl":"10.1016/j.sca.2025.100178","url":null,"abstract":"<div><div>A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584400","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 data-driven and cognitive analytics framework for sustainable supply chain transformation in industry 6.0","authors":"Andrés Fernández-Miguel ,&nbsp;Susana Ortíz-Marcos ,&nbsp;Mariano Jiménez-Calzado ,&nbsp;Alfonso P. Fernández del Hoyo ,&nbsp;Fernando García-Muiña ,&nbsp;Davide Settembre-Blundo","doi":"10.1016/j.sca.2026.100197","DOIUrl":"10.1016/j.sca.2026.100197","url":null,"abstract":"<div><div>The transition from data-driven to cognitively adaptive supply chains represent a critical step toward Industry 6.0, where learning, coordination, and sustainability must be addressed jointly. Existing supply chain analytics approaches remain limited in capturing adaptive and systemic behaviors under uncertainty, particularly in resource- and energy-intensive industrial contexts. This study proposes a Cognitive and Data-Driven Framework for Supply Chains based on federated learning and synthetic data simulations grounded in aggregated industrial benchmarks. The framework introduces the Adaptivity Coefficient (A_c), a composite metric integrating learning velocity, anticipatory responsiveness, and technological exposure to quantify cognitive readiness at the network level. Results from simulation experiments show that cognitively adaptive supply chains achieve significant performance improvements compared to conventional predictive approaches. Specifically, cognitive coordination reduces cumulative disruption costs by 18–25 %, lowers emissions intensity by up to 15 %, and shortens recovery time by approximately 27 %. The analysis further demonstrates that adaptive learning expands the Pareto-efficient frontier, enabling simultaneous gains in cost efficiency, resilience, and environmental performance under varying levels of uncertainty. These findings suggest that cognitive adaptivity functions as a strategic capability rather than a purely technical feature. The study concludes by highlighting the managerial and policy implications of embedding cognitive learning into supply chain governance and by outlining pathways for future empirical validation in hard-to-abate manufacturing sectors.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"13 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394470","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}