Pub Date : 2025-12-01Epub Date: 2025-08-14DOI: 10.1016/j.sca.2025.100153
Tejinder Singh Lakhwani, Yerasani Sinjana
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.
{"title":"A metaheuristic approach for optimizing drone routing in healthcare supply chains","authors":"Tejinder Singh Lakhwani, 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":"2025-12-01","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}
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.
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.
{"title":"A comparative study of multi-algorithm optimization for inventory analytics in supply chains","authors":"Oussama Zabraoui, Yahya Hmamou , Anas Chafi , 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":"2025-12-01","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}
Pub Date : 2025-12-01Epub Date: 2025-09-29DOI: 10.1016/j.sca.2025.100166
Ramakrishna Garine , Ripon K. Chakrabortty
Timely delivery is a critical performance metric in supply chain management, yet achieving consistent on-time delivery has become increasingly challenging in the face of global uncertainties and complex logistics networks. Recent disruptions, such as pandemics, extreme weather events, and geopolitical conflicts, have exposed vulnerabilities in supply chains, resulting in frequent delivery delays. While traditional heuristics and simple statistical methods have proven inadequate to capture the myriad factors that contribute to delays in modern supply chains, Machine learning (ML) and Deep Learning (DL) approaches have emerged as powerful tools to improve the accuracy and reliability of delivery delay prediction. Consequently, this study presents a hybrid predictive framework that integrates DL models with Reinforcement Learning (RL) to improve binary classification of order status (on-time vs. late). We first benchmark several DL architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Stacked LSTM, enhanced with regularization and extended training epochs, alongside a fine-tuned eXtreme Gradient Boost (XGBoost) model. These models are evaluated using accuracy, precision, recall, and the F1-score, with Bi-LSTM and Stacked LSTM achieving strong generalization performance. Building on this, we deploy a Proximal Policy Optimization (PPO) agent that incorporates deep learning outputs as part of its observation space. The RL agent uses a reward-based feedback loop to improve adaptability under dynamic conditions. Experimental results show that the hybrid DL-RL model achieves superior classification accuracy and an F1-score greater than 0.99, outperforming standalone methods. Although the PPO agent alone struggled with detecting minorities due to imbalance, integrating DL features mitigated this limitation. The findings support the use of hybrid architectures for real-time order status prediction and provide a scalable pathway for intelligent supply chain decision making. Future work will address class imbalance and enhance policy robustness through cost-sensitive and explainable RL strategies.
{"title":"A deep learning and policy optimization approach for supply chain order classification","authors":"Ramakrishna Garine , Ripon K. Chakrabortty","doi":"10.1016/j.sca.2025.100166","DOIUrl":"10.1016/j.sca.2025.100166","url":null,"abstract":"<div><div>Timely delivery is a critical performance metric in supply chain management, yet achieving consistent on-time delivery has become increasingly challenging in the face of global uncertainties and complex logistics networks. Recent disruptions, such as pandemics, extreme weather events, and geopolitical conflicts, have exposed vulnerabilities in supply chains, resulting in frequent delivery delays. While traditional heuristics and simple statistical methods have proven inadequate to capture the myriad factors that contribute to delays in modern supply chains, Machine learning (ML) and Deep Learning (DL) approaches have emerged as powerful tools to improve the accuracy and reliability of delivery delay prediction. Consequently, this study presents a hybrid predictive framework that integrates DL models with Reinforcement Learning (RL) to improve binary classification of order status (on-time vs. late). We first benchmark several DL architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Stacked LSTM, enhanced with regularization and extended training epochs, alongside a fine-tuned eXtreme Gradient Boost (XGBoost) model. These models are evaluated using accuracy, precision, recall, and the F1-score, with Bi-LSTM and Stacked LSTM achieving strong generalization performance. Building on this, we deploy a Proximal Policy Optimization (PPO) agent that incorporates deep learning outputs as part of its observation space. The RL agent uses a reward-based feedback loop to improve adaptability under dynamic conditions. Experimental results show that the hybrid DL-RL model achieves superior classification accuracy and an F1-score greater than 0.99, outperforming standalone methods. Although the PPO agent alone struggled with detecting minorities due to imbalance, integrating DL features mitigated this limitation. The findings support the use of hybrid architectures for real-time order status prediction and provide a scalable pathway for intelligent supply chain decision making. Future work will address class imbalance and enhance policy robustness through cost-sensitive and explainable RL strategies.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220331","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}
Pub Date : 2025-12-01Epub Date: 2025-09-26DOI: 10.1016/j.sca.2025.100168
Kosar Akhavan Chayjan , Jafar Razmi , Saman Hassanzadeh Amin
Inflation poses significant challenges to supply chain operations by raising procurement and operational costs, dampening customer demand, and complicating decision-making for suppliers and retailers. This study investigates the optimization of supply chain strategies under inflationary pressures, addressing the inadequacy of traditional ordering and pricing approaches. We model a supply chain comprising one supplier and two retailers exposed to inflation-driven price volatility. Using an analytical optimization framework, eight scenarios are evaluated based in retailers’ adoption of hedging strategies through option contracts versus optimal order quantity strategies, while considering lead time dynamics and retailer competition. The results indicate that inflation profoundly influences optimal order quantities, supplier capacity, and the profitability of all supply chain participants. Full collaboration yields profit growth exceeding 1900 % compared to non-cooperative settings, whereas partial collaboration still results in gains of more than 25 %. Conversely, the least efficient scenarios incur profit losses of up to 95 %, highlighting the substantial penalty of insufficient coordination. Notably, the joint adoption of hedging strategies by both retailers yields the highest supply chain profit, particularly in environments characterized by longer lead times or elevated inflation rates. Hedging enables retailers to stabilize prices, sustain customer demand, and shield customers from inflation’s adverse effects. Furthermore, collaboration among retailers enhances overall supply chain resilience. This research offers actionable insights for practitioners aiming to aiming mitigate inflationary risks, emphasizing the essential roles of analytical planning, hedging, and coordination in supply chain management under inflationary conditions.
{"title":"An analytical investigation of inflation’s effects on supply chain strategies","authors":"Kosar Akhavan Chayjan , Jafar Razmi , Saman Hassanzadeh Amin","doi":"10.1016/j.sca.2025.100168","DOIUrl":"10.1016/j.sca.2025.100168","url":null,"abstract":"<div><div>Inflation poses significant challenges to supply chain operations by raising procurement and operational costs, dampening customer demand, and complicating decision-making for suppliers and retailers. This study investigates the optimization of supply chain strategies under inflationary pressures, addressing the inadequacy of traditional ordering and pricing approaches. We model a supply chain comprising one supplier and two retailers exposed to inflation-driven price volatility. Using an analytical optimization framework, eight scenarios are evaluated based in retailers’ adoption of hedging strategies through option contracts versus optimal order quantity strategies, while considering lead time dynamics and retailer competition. The results indicate that inflation profoundly influences optimal order quantities, supplier capacity, and the profitability of all supply chain participants. Full collaboration yields profit growth exceeding 1900 % compared to non-cooperative settings, whereas partial collaboration still results in gains of more than 25 %. Conversely, the least efficient scenarios incur profit losses of up to 95 %, highlighting the substantial penalty of insufficient coordination. Notably, the joint adoption of hedging strategies by both retailers yields the highest supply chain profit, particularly in environments characterized by longer lead times or elevated inflation rates. Hedging enables retailers to stabilize prices, sustain customer demand, and shield customers from inflation’s adverse effects. Furthermore, collaboration among retailers enhances overall supply chain resilience. This research offers actionable insights for practitioners aiming to aiming mitigate inflationary risks, emphasizing the essential roles of analytical planning, hedging, and coordination in supply chain management under inflationary conditions.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266091","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}
Pub Date : 2025-12-01Epub Date: 2025-11-13DOI: 10.1016/j.sca.2025.100177
Seyed Pendar Toufighi , Amir Mohammad Norouzzadeh , Iman Ghasemian Sahebi , Jan Vang
The construction industry is a major contributor to environmental degradation, underscoring the urgency of adopting Green Supply Chain Management (GSCM) to advance sustainability. Despite its importance, GSCM adoption in construction remains limited, particularly in developing countries. This study systematically identifies and models the barriers hindering GSCM implementation in the construction industry, using Iran as a case study. A mixed-method approach was applied, integrating expert interviews with Interpretive Structural Modeling (ISM) and MICMAC analysis to capture both qualitative insights and quantitative interdependencies. Sixteen interrelated barriers were identified and hierarchically structured into a ten-level model. Results indicate that the lack of governmental support and incentives (B3) acts as the most critical driver at the top of the hierarchy, influencing nearly all other factors. Barriers such as the absence of green experts (B1), lack of green suppliers (B4), and limited knowledge and awareness (B12) were found to hold high driving power, while issues like stakeholder collaboration (B7) and managerial commitment (B8) were highly dependent outcomes. The ISM-MICMAC framework thus highlights how systemic and structural deficiencies shape GSCM adoption. By offering a data-driven structural model tailored to the construction context, this study provides both theoretical advancement and practical guidance for policymakers and industry leaders seeking to prioritize interventions that enhance sustainability in Construction Supply Chains (CSCs).
{"title":"A structural analysis of barriers to sustainable construction supply chains","authors":"Seyed Pendar Toufighi , Amir Mohammad Norouzzadeh , Iman Ghasemian Sahebi , Jan Vang","doi":"10.1016/j.sca.2025.100177","DOIUrl":"10.1016/j.sca.2025.100177","url":null,"abstract":"<div><div>The construction industry is a major contributor to environmental degradation, underscoring the urgency of adopting Green Supply Chain Management (GSCM) to advance sustainability. Despite its importance, GSCM adoption in construction remains limited, particularly in developing countries. This study systematically identifies and models the barriers hindering GSCM implementation in the construction industry, using Iran as a case study. A mixed-method approach was applied, integrating expert interviews with Interpretive Structural Modeling (ISM) and MICMAC analysis to capture both qualitative insights and quantitative interdependencies. Sixteen interrelated barriers were identified and hierarchically structured into a ten-level model. Results indicate that the lack of governmental support and incentives (B3) acts as the most critical driver at the top of the hierarchy, influencing nearly all other factors. Barriers such as the absence of green experts (B1), lack of green suppliers (B4), and limited knowledge and awareness (B12) were found to hold high driving power, while issues like stakeholder collaboration (B7) and managerial commitment (B8) were highly dependent outcomes. The ISM-MICMAC framework thus highlights how systemic and structural deficiencies shape GSCM adoption. By offering a data-driven structural model tailored to the construction context, this study provides both theoretical advancement and practical guidance for policymakers and industry leaders seeking to prioritize interventions that enhance sustainability in Construction Supply Chains (CSCs).</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528591","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}
Pub Date : 2025-12-01Epub Date: 2025-09-25DOI: 10.1016/j.sca.2025.100167
Georgios Gelastopoulos, Christos Keramydas
Global supply chains are increasingly complex and vulnerable, requiring new approaches for detecting and managing risks. Text mining, a branch of natural language processing, can extract insights from unstructured online data such as news, reports, and social media. This paper presents a systematic review of 33 peer-reviewed studies on text mining in supply chain risk management (SCRM). The review addresses four research questions: (i) which types of online data are used and how their characteristics affect reliability and timeliness, (ii) which techniques are applied and with what trade-offs, (iii) how text mining contributes to risk identification, prediction, and mitigation, and (iv) what gaps and opportunities remain for future research. A bibliometric analysis is also conducted to highlight publication trends, contributors, and thematic clusters. The findings reveal that Twitter and news sources dominate, while methods range from sentiment analysis and topic modeling to advanced neural models such as BERT. Applications emphasize risk identification and visibility, with emerging work in predictive analytics and decision support. A conceptual framework is proposed linking unstructured data to risk management decisions. This review contributes to the literature by underscoring the value of real-time textual for improving visibility, agility, and resilience in complex supply chains.
{"title":"A systematic review of text mining analytics for supply chain risk management using online data","authors":"Georgios Gelastopoulos, Christos Keramydas","doi":"10.1016/j.sca.2025.100167","DOIUrl":"10.1016/j.sca.2025.100167","url":null,"abstract":"<div><div>Global supply chains are increasingly complex and vulnerable, requiring new approaches for detecting and managing risks. Text mining, a branch of natural language processing, can extract insights from unstructured online data such as news, reports, and social media. This paper presents a systematic review of 33 peer-reviewed studies on text mining in supply chain risk management (SCRM). The review addresses four research questions: (i) which types of online data are used and how their characteristics affect reliability and timeliness, (ii) which techniques are applied and with what trade-offs, (iii) how text mining contributes to risk identification, prediction, and mitigation, and (iv) what gaps and opportunities remain for future research. A bibliometric analysis is also conducted to highlight publication trends, contributors, and thematic clusters. The findings reveal that Twitter and news sources dominate, while methods range from sentiment analysis and topic modeling to advanced neural models such as BERT. Applications emphasize risk identification and visibility, with emerging work in predictive analytics and decision support. A conceptual framework is proposed linking unstructured data to risk management decisions. This review contributes to the literature by underscoring the value of real-time textual for improving visibility, agility, and resilience in complex supply chains.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220334","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}
Artificial intelligence (AI) is increasingly utilized in healthcare logistics, including automated systems for collecting hazardous medical waste from hospitals under strict time and capacity constraints. This study compares three routing algorithms: (1) Google Maps Destination using an application programming interface (API), (2) hybrid clustering with Deep Q-Network (DQN), and (3) a hybrid method combining clustering, the fractional knapsack strategy, and DQN. These algorithms aim to optimize route planning and scheduling for medical waste collection vehicles operating under real-world constraints such as limited vehicle capacity and fixed service windows. The routing problem is modeled as both a capacitated vehicle routing problem (CVRP) and a CVRP with time windows (CVRPTW), capturing complexities. A multi-trip routing strategy is integrated into the promising algorithms to assess its impact on performance metrics, including capacity utilization, travel distance, total operational time, and number of trips. Experimental results indicate hybrid approach with clustering, fractional knapsack, and DQN outperforms others. It achieved capacity utilization rates of 96.47 percent for CVRP and 76.01 % for CVRPTW, requiring six vehicles, a 25 % reduction compared to the Google Maps API method, while matching the performance of clustering with DQN under time constraints. The CVRP model improved capacity utilization by 28.9 % over Google Maps API and 85.1 % over clustering with DQN. Although travel distance increased slightly (0.61 % in CVRP and 7.2 % in CVRPTW), total operational time was reduced by 7.6 and 4.6 %. The proposed model also minimized extra trips, requiring none for CVRP and only one for CVRPTW, compared to two additional trips needed by clustering with DQN in both scenarios. These findings highlight the hybrid approach as a robust, efficient solution for medical waste transportation under complex conditions.
{"title":"A deep reinforcement learning and fractional packing framework for routing and scheduling in healthcare waste supply chains","authors":"Norhan Khallaf , Osama Abdel‑Raouf , Mohiy Hadhoud , Mohamed Dawam , Ahmed Kafafy","doi":"10.1016/j.sca.2025.100164","DOIUrl":"10.1016/j.sca.2025.100164","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly utilized in healthcare logistics, including automated systems for collecting hazardous medical waste from hospitals under strict time and capacity constraints. This study compares three routing algorithms: (1) Google Maps Destination using an application programming interface (API), (2) hybrid clustering with Deep Q-Network (DQN), and (3) a hybrid method combining clustering, the fractional knapsack strategy, and DQN. These algorithms aim to optimize route planning and scheduling for medical waste collection vehicles operating under real-world constraints such as limited vehicle capacity and fixed service windows. The routing problem is modeled as both a capacitated vehicle routing problem (CVRP) and a CVRP with time windows (CVRPTW), capturing complexities. A multi-trip routing strategy is integrated into the promising algorithms to assess its impact on performance metrics, including capacity utilization, travel distance, total operational time, and number of trips. Experimental results indicate hybrid approach with clustering, fractional knapsack, and DQN outperforms others. It achieved capacity utilization rates of 96.47 percent for CVRP and 76.01 % for CVRPTW, requiring six vehicles, a 25 % reduction compared to the Google Maps API method, while matching the performance of clustering with DQN under time constraints. The CVRP model improved capacity utilization by 28.9 % over Google Maps API and 85.1 % over clustering with DQN. Although travel distance increased slightly (0.61 % in CVRP and 7.2 % in CVRPTW), total operational time was reduced by 7.6 and 4.6 %. The proposed model also minimized extra trips, requiring none for CVRP and only one for CVRPTW, compared to two additional trips needed by clustering with DQN in both scenarios. These findings highlight the hybrid approach as a robust, efficient solution for medical waste transportation under complex conditions.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158648","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}
Pub Date : 2025-12-01Epub Date: 2025-11-15DOI: 10.1016/j.sca.2025.100175
Ruhaimatu Abudu , Beatrice Agbeko
Energy supply chains increasingly adopt digital quality management systems, but research on their performance impact remains limited, especially in emerging markets. While individual digital quality dimensions exist in literature, his study provides empirical validation of an integrated framework of seven dimensions specifically for energy supply chain contexts. Using survey data from 120 supply chain professionals at Ghana National Gas Company, we examine relationships between digital quality analytics implementation and supply chain performance through factor analysis and multiple regression. Results suggest digital quality analytics implementation is associated with 65.3% of supply chain performance variance within this organizational context (R = 0.653, F = 30.13, p 0.001), with all seven factors showing significant positive relationships. Digital customer analytics proves the strongest predictor ( = 0.243), followed by blockchain integration ( = 0.171) and data-driven improvement ( = 0.156). Digital maturity shows no moderation association, suggesting consistent effectiveness across organizational readiness levels. Implementation patterns across maturity groups align with institutional theory predictions about technology adoption in emerging markets. While findings are based on a single organization and require broader validation, results offer a preliminarily tested framework that may inform digital quality analytics in similar energy supply chain contexts, extending quality management theory and suggesting potential guidance for digital transformation efforts in similar organizational settings.
{"title":"A data-driven analysis of quality management impacts on energy supply chain performance","authors":"Ruhaimatu Abudu , Beatrice Agbeko","doi":"10.1016/j.sca.2025.100175","DOIUrl":"10.1016/j.sca.2025.100175","url":null,"abstract":"<div><div>Energy supply chains increasingly adopt digital quality management systems, but research on their performance impact remains limited, especially in emerging markets. While individual digital quality dimensions exist in literature, his study provides empirical validation of an integrated framework of seven dimensions specifically for energy supply chain contexts. Using survey data from 120 supply chain professionals at Ghana National Gas Company, we examine relationships between digital quality analytics implementation and supply chain performance through factor analysis and multiple regression. Results suggest digital quality analytics implementation is associated with 65.3% of supply chain performance variance within this organizational context (R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.653, F<span><math><msub><mrow></mrow><mrow><mn>7</mn><mo>,</mo><mn>112</mn></mrow></msub></math></span> = 30.13, p <span><math><mo><</mo></math></span> 0.001), with all seven factors showing significant positive relationships. Digital customer analytics proves the strongest predictor (<span><math><mi>β</mi></math></span> = 0.243), followed by blockchain integration (<span><math><mi>β</mi></math></span> = 0.171) and data-driven improvement (<span><math><mi>β</mi></math></span> = 0.156). Digital maturity shows no moderation association, suggesting consistent effectiveness across organizational readiness levels. Implementation patterns across maturity groups align with institutional theory predictions about technology adoption in emerging markets. While findings are based on a single organization and require broader validation, results offer a preliminarily tested framework that may inform digital quality analytics in similar energy supply chain contexts, extending quality management theory and suggesting potential guidance for digital transformation efforts in similar organizational settings.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579045","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}
Sustainable supply chains are essential for promoting environmental responsibility, economic efficiency, and social well-being. They help reduce carbon footprints, optimize resource use, and support circular economy initiatives. Economically, they enhance efficiency, lower costs, and mitigate risks related to resource scarcity and environmental regulations. Socially, they ensure ethical sourcing, fair labor practices, and corporate social responsibility. By balancing these dimensions, sustainable supply chains contribute to business resilience while aligning with global sustainability goals, such as the UN Sustainable Development Goals (SDGs). In the age of Artificial Intelligence (AI), rapid technological advancements have significantly transformed supply chain operations, necessitating greater flexibility and the integration of AI-driven techniques. The application of AI in supply chain management has proven highly beneficial, offering enhanced efficiency, predictive capabilities, and improved sustainability. Recent advancements, including Large Language Models (LLMs), are also playing a transformative role in enhancing decision-making and risk management across supply chains. Numerous researchers have highlighted AI's potential in advancing circular economy initiatives by optimizing resource utilization and minimizing waste. However, despite the growing academic interest, research in this domain remains fragmented and lacks a coherent structure. To address this gap, this paper conducts a comprehensive bibliometric analysis to map the current research landscape, identify key themes, and highlight future directions. Bibliographic records were retrieved from the Web of Science database, covering the period from 1997 to 2024. A total of 1070 records were initially gathered for analysis. The findings of this study provide valuable insights into the evolution of research in AI-driven sustainable supply chains, uncover emerging trends, and suggest potential avenues for future exploration. Specifically, the analysis reveals an annual publication growth rate of 23.37 % from 1997 to 2024, with China, India, and the USA as the top contributing countries. Core research themes include AI-enabled logistics optimization, circular economy practices, and supply chain resilience under global disruptions. By offering a structured overview of the field, this study aims to support scholars and practitioners in navigating the intersection of AI and sustainability in supply chain management.
可持续供应链对于促进环境责任、经济效率和社会福祉至关重要。它们有助于减少碳足迹，优化资源利用，并支持循环经济倡议。从经济上讲，它们提高了效率，降低了成本，减轻了与资源短缺和环境法规相关的风险。在社会方面，他们确保合乎道德的采购、公平的劳动实践和企业的社会责任。通过平衡这些方面，可持续供应链有助于提高企业弹性，同时与联合国可持续发展目标（sdg）等全球可持续发展目标保持一致。在人工智能（AI）时代，快速的技术进步极大地改变了供应链运营，需要更大的灵活性和人工智能驱动技术的整合。人工智能在供应链管理中的应用已被证明是非常有益的，可以提高效率、预测能力和改善可持续性。包括大型语言模型（llm）在内的最新进展也在加强供应链决策和风险管理方面发挥着变革性作用。许多研究人员都强调了人工智能在通过优化资源利用和减少浪费来推进循环经济举措方面的潜力。然而，尽管学术界对该领域的兴趣日益浓厚，但该领域的研究仍然是碎片化的，缺乏连贯的结构。为了解决这一差距，本文进行了全面的文献计量分析，以绘制当前的研究景观，确定关键主题，并强调未来的方向。文献记录检索自Web of Science数据库，时间跨度为1997 - 2024年。最初总共收集了1070条记录进行分析。本研究的结果为人工智能驱动的可持续供应链研究的演变提供了有价值的见解，揭示了新兴趋势，并提出了未来探索的潜在途径。具体来说，分析显示，从1997年到2024年，年出版增长率为23.37 %，其中中国、印度和美国是贡献最大的国家。核心研究主题包括人工智能支持的物流优化、循环经济实践和全球中断下的供应链弹性。通过提供该领域的结构化概述，本研究旨在支持学者和从业者在供应链管理中导航人工智能和可持续性的交叉点。
{"title":"An analytical review of artificial intelligence applications in sustainable supply chains","authors":"Amirhossein Ghasemi Abyaneh , Hossein Ghanbari , Emran Mohammadi , Amirali Amirsahami , Masoud Khakbazan","doi":"10.1016/j.sca.2025.100173","DOIUrl":"10.1016/j.sca.2025.100173","url":null,"abstract":"<div><div>Sustainable supply chains are essential for promoting environmental responsibility, economic efficiency, and social well-being. They help reduce carbon footprints, optimize resource use, and support circular economy initiatives. Economically, they enhance efficiency, lower costs, and mitigate risks related to resource scarcity and environmental regulations. Socially, they ensure ethical sourcing, fair labor practices, and corporate social responsibility. By balancing these dimensions, sustainable supply chains contribute to business resilience while aligning with global sustainability goals, such as the UN Sustainable Development Goals (SDGs). In the age of Artificial Intelligence (AI), rapid technological advancements have significantly transformed supply chain operations, necessitating greater flexibility and the integration of AI-driven techniques. The application of AI in supply chain management has proven highly beneficial, offering enhanced efficiency, predictive capabilities, and improved sustainability. Recent advancements, including Large Language Models (LLMs), are also playing a transformative role in enhancing decision-making and risk management across supply chains. Numerous researchers have highlighted AI's potential in advancing circular economy initiatives by optimizing resource utilization and minimizing waste. However, despite the growing academic interest, research in this domain remains fragmented and lacks a coherent structure. To address this gap, this paper conducts a comprehensive bibliometric analysis to map the current research landscape, identify key themes, and highlight future directions. Bibliographic records were retrieved from the Web of Science database, covering the period from 1997 to 2024. A total of 1070 records were initially gathered for analysis. The findings of this study provide valuable insights into the evolution of research in AI-driven sustainable supply chains, uncover emerging trends, and suggest potential avenues for future exploration. Specifically, the analysis reveals an annual publication growth rate of 23.37 % from 1997 to 2024, with China, India, and the USA as the top contributing countries. Core research themes include AI-enabled logistics optimization, circular economy practices, and supply chain resilience under global disruptions. By offering a structured overview of the field, this study aims to support scholars and practitioners in navigating the intersection of AI and sustainability in supply chain management.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416158","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}
Pub Date : 2025-12-01Epub Date: 2025-09-19DOI: 10.1016/j.sca.2025.100161
Robertas Damaševičius , Rytis Maskeliūnas
This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices.
{"title":"An analytical approach to blockchain-driven identity management in sustainable forest supply chains","authors":"Robertas Damaševičius , Rytis Maskeliūnas","doi":"10.1016/j.sca.2025.100161","DOIUrl":"10.1016/j.sca.2025.100161","url":null,"abstract":"<div><div>This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"12 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121269","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}
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