1. OSCM Publications
  2. Volume 18, Issue 2, 2025

Volume 18, Issue 2, 2025

  1. Enhancing Supply Chain Visibility in the Fishing Industry: The Role of Emerging Technologies and Their Affordances
    • Maryam Mirzaei
    • Benjamin Dehe
    • Kasuni Weerasinghe
    • Hari Srivastava
    Abstract Full PDF Cite DOI: 10.31387/oscm0610462
    This study explores the role of emerging technologies in enhancing supply chain visibility (SCV) within New Zealand's fishing industry, a sector challenged by the perishable, temperature-sensitive nature of its products, subject to long supply routes, and strict regulatory standards. In this context, visibility within the supply chain transcends mere tracking; it encompasses real-time data access on product status, environmental conditions, and logistical status. Leveraging the technology affordance theory, the research examines how tools such as GPS trackers, RFID tags, and IoT devices influence decision-making and operational efficiency by offering actionable data insights. Through a qualitative case study, including interviews and workshops with industry experts, the article identifies key technological affordances: real-time tracking, proactive management, and data-driven decision-making. The theory of technology affordances provides insights into the interactions between technology and the socio-technical environment, highlighting the opportunities and challenges industry managers face. Our analysis reveals that while these technologies substantially improve monitoring and sustainability practices, their effectiveness is tempered by user knowledge, contextual conditions, and organizational collaboration. Moreover, the study highlights the complex interactions between technology, user expectations, and supply chain outcomes, emphasizing the need for integrated, user-centric platforms to maximize SCV benefits. This research contributes by providing a nuanced understanding of the factors linking SCV technologies, functionalities, and decisions through their adoption and impact. The mapping offers practical insights for supply chain managers to enhance transparency, resilience, and efficiency. Ultimately, the study encourages continued innovation in data- driven SCV solutions to address the unique challenges of perishable goods supply chains.
    @article{maryammirzaei-2025-1673,
  title={Enhancing Supply Chain Visibility in the Fishing Industry: The Role of Emerging Technologies and Their Affordances},
  author={Maryam  Mirzaei and Benjamin  Dehe and Kasuni  Weerasinghe and Hari  Srivastava},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={146--160},
  doi={10.31387/oscm0610462}
}
    Maryam  Mirzaei, Benjamin  Dehe, Kasuni  Weerasinghe, Hari  Srivastava (2025). Enhancing Supply Chain Visibility in the Fishing Industry: The Role of Emerging Technologies and Their Affordances. Operations and Supply Chain Management: An International Journal, 18(2), 146-160. https://doi.org/10.31387/oscm0610462
  2. Forecasting and Multi-Objective Optimization Model for Supplier Selection and Order Allocation at Lubricants Distributor
    • Huu-Tin To
    • Mai-Ha Phan
    Abstract Full PDF Cite DOI: 10.31387/oscm0610466
    Some distributors face challenges in selecting suppliers and determining the quantity of orders to meet customer demand adequately. The root cause of this issue lies in the lack of application of accurate forecasting techniques and insufficient consideration of uncertainty factors. Therefore, this study proposes a model that incorporates forecasting techniques and accounts for the uncertainty of input parameters. First, applying and comparing ARIMA, Holt-Winter, and ANN methods to forecast future demand for datasets collected that are both trendy and seasonal. The forecasted demand is one of the input parameters for the proposed Multi-objective Supplier Selection and Order Allocation model. Use the Mixed Integer Linear Programming (MILP) method to resolve uncertainty in supplier purchasing prices and the weighted sum approach to combine the objective functions. The proposed model establishes Supplier Selection and Order Allocation planning for the distributor in the lubricant industry, resulting in a total cost decrease of 12.30% and a damaged product decrease of 5.63% within four weeks compared to the actual. Moreover, the model is capable of solving more complex scenarios, including cases with up to 15 suppliers and 10 product types over 8 weeks across various scenarios. However, the solution time is still considerable, suggesting that exploring metaheuristic optimization algorithms could be beneficial to improve the efficiency of the model in handling such large-scale problems, as well as considering additional constraints related to sustainability and other uncertainty factors to better reflect real-world conditions.
    @article{huu-tinto-2025-1677,
  title={Forecasting and Multi-Objective Optimization Model for Supplier Selection and Order Allocation at Lubricants Distributor},
  author={Huu-Tin  To and Mai-Ha  Phan},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={161--173},
  doi={10.31387/oscm0610466}
}
    Huu-Tin  To, Mai-Ha  Phan (2025). Forecasting and Multi-Objective Optimization Model for Supplier Selection and Order Allocation at Lubricants Distributor. Operations and Supply Chain Management: An International Journal, 18(2), 161-173. https://doi.org/10.31387/oscm0610466
  3. Decision Making Model for Frozen Distribution using Cross-dock
    • Minh-Phuong Vu
    • Hoai-Phuc Tran
    • Huu-Thanh Nguyen
    • Mai-Ha Phan
    Abstract Full PDF Cite DOI: 10.31387/oscm0610467
    Within the cross-dock facility, suppliers transport goods on pallets; however, there are instances when the received amount does not align with the allocation demand, resulting in either surplus or shortage. This situation raises the challenge of determining the appropriate customer to handle the excess or shortfall while also addressing any potential penalties. In the case of frozen deliveries, time sensitivity is of utmost importance, necessitating swift and efficient procedures. However, Ho Chi Minh City's dynamic environment poses challenges when delivery schedules deviate, making it necessary to accept such variations. The problem also involves optimizing vehicle allocation and managing early or late deliveries, along with over-deliveries or under-deliveries. The objective of this study is to create an enhanced schedule and strategy for vehicle allocation that effectively reduces three cost components: transportation expenses, penalties associated with untimely deliveries, and fines for inaccuracies in product quantities. A robust model will be introduced to balance these costs effectively, providing valuable insights for frozen product distribution. The model was initially tested on a sample problem using the Trial-and-Error method with CPLEX. Given the large data set and time constraints in real-world scenarios, Simulated Annealing is used to optimize the solution faster, making it more practical for implementation.
    @article{minh-phuongvu-2025-1678,
  title={Decision Making Model for Frozen Distribution using Cross-dock},
  author={Minh-Phuong  Vu and Hoai-Phuc  Tran and Huu-Thanh  Nguyen and Mai-Ha  Phan},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={174--184},
  doi={10.31387/oscm0610467}
}
    Minh-Phuong  Vu, Hoai-Phuc  Tran, Huu-Thanh  Nguyen, Mai-Ha  Phan (2025). Decision Making Model for Frozen Distribution using Cross-dock. Operations and Supply Chain Management: An International Journal, 18(2), 174-184. https://doi.org/10.31387/oscm0610467
  4. Investigating Digital Collaboration: Human Factors in Artificial Intelligence-Driven Collaboration Platforms for Resilient Port
    • Rio Theodore Natalianto Lasse
    • Raja Oloan Saut Gurning
    • Imam Baihaqi
    • Bahana Wiradanti
    Abstract Full PDF Cite DOI: 10.31387/oscm0610465
    The adoption of digital technology in maritime transport is currently essential and inevitable. Major global ports embracing these advancements to improve efficiency and resilience. However, the next frontier involves fostering digital collaboration among stakeholders in the maritime transport ecosystem. As Artificial Intelligence (AI) becomes critical in enhancing productivity, its integration into digital technologies is key to creating resilient ports capable of mitigating disruptions. While technological aspects have received ample attention, there is a lack of research addressing the human factors involved in AI driven digital collaboration platforms—particularly in Indonesian ports, which face unique geographical and disaster-related challenges due to the nation’s archipelagic nature. This study explores the human factors influencing the successful implementation of digital collaboration in Indonesian ports. It identifies human factor barriers that hinder the effective use of AI-driven technologies. Using qualitative interviews and simple quantitative survey, this research investigates the strategies necessary to address these human-centric challenges and optimize the role of individuals in digital transformation. Qualitative data is analyzed using thematic analysis, while quantitative data is presented using the Importance-Performance Matrix and Adoption Matrix. The findings present actionable strategies to overcome human factor barriers. A regulatory framework is proposed aimed at enhancing the resilience of Indonesia's port system. This paper offers a practical, human-centered approach to digital collaboration, ensuring that Indonesia’s port ecosystem becomes more adaptable to future disruptions and sustainable in a rapidly evolving digital landscape.
    @article{riotheodorenataliantolasse-2025-1676,
  title={Investigating Digital Collaboration: Human Factors in Artificial Intelligence-Driven Collaboration Platforms for Resilient Port},
  author={Rio Theodore Natalianto  Lasse and Raja Oloan Saut  Gurning and Imam  Baihaqi and Bahana  Wiradanti},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={185--200},
  doi={10.31387/oscm0610465}
}
    Rio Theodore Natalianto  Lasse, Raja Oloan Saut  Gurning, Imam  Baihaqi, Bahana  Wiradanti (2025). Investigating Digital Collaboration: Human Factors in Artificial Intelligence-Driven Collaboration Platforms for Resilient Port. Operations and Supply Chain Management: An International Journal, 18(2), 185-200. https://doi.org/10.31387/oscm0610465
  5. The Impacts of Big Data Analytics and Artificial Intelligence on Supply Chain Strategic Performance: An Empirical Study
    • Asmaa Es-satty
    • Mohamed Naimi
    • Radouane Lemghari
    • Chafik Okar
    Abstract Full PDF Cite DOI: 10.31387/oscm0610464
    The present paper investigates the impacts of Big Data Analytics and Artificial Intelligence (BDA-AI) technologies on the Supply Chain Strategic Performance (SCSP), focusing on reliability, environmental and social performance. It also examines the moderating role of Organizational Culture (OC). Using the Supply Chain Operations Reference (SCOR) model and Dynamic Capabilities Theory (DCT) as theoretical frameworks, the study employed a structured questionnaire distributed to Supply Chain (SC) professionals across various industrial sectors essentially based in Morocco. In fact, a total of 200 professionals were initially targeted using the virtual snowball sampling method. That is, 97 survey responses were effectively collected and then analyzed. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The obtained results demonstrate that BDA-AI technologies positively and significantly influence Supply Chain Reliability (SCRE). However, while a positive impact on Environmental Performance (ENPER) and Social Performance (SOPER) was observed, it was not statistically significant. Moreover, OC significantly moderates the effect of BDA-AI on SCRE. To sum up, this paper contributes to the existing literature by providing empirical evidence of the strategic benefits of BDA powered by AI in Supply Chain Management (SCM). Furthermore, it sheds light on the importance of OC as a critical factor in realizing the full potential of such technologies in digital transformation context.
    @article{asmaaes-satty-2025-1675,
  title={The Impacts of Big Data Analytics and Artificial Intelligence on Supply Chain Strategic Performance: An Empirical Study},
  author={Asmaa  Es-satty and Mohamed  Naimi and Radouane  Lemghari and Chafik  Okar},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={201--214},
  doi={10.31387/oscm0610464}
}
    Asmaa  Es-satty, Mohamed  Naimi, Radouane  Lemghari, Chafik  Okar (2025). The Impacts of Big Data Analytics and Artificial Intelligence on Supply Chain Strategic Performance: An Empirical Study. Operations and Supply Chain Management: An International Journal, 18(2), 201-214. https://doi.org/10.31387/oscm0610464
  6. Evaluating Industry 4.0 Technologies for Supply Chain Optimization: A Best-Worst Method Approach
    • Samah Mahmah
    • Salah Oulfarsi
    • Ikram Ait Hammou
    Abstract Full PDF Cite DOI: 10.31387/oscm0610463
    The emergence of Industry 4.0 has brought transformative changes to supply chain management, introducing advanced technologies that challenge and reshape traditional operational practices. These innovations are designed to enhance supply chain efficiency, agility, and resilience, offering organizations the opportunity to overcome existing challenges and stay competitive. This study seeks to evaluate the adoption levels of the key Industry 4.0 practices in Moroccan supply chains, using the Best Worst Method (BWM) to systematically rank them based on their levels of implementation and their strategic importance to improving supply chain performance. Data was gathered from 15 expert respondents with diverse expertise in supply chain management and Industry 4.0 technologies. The analysis identifies the most widely adopted practices, such as IoT and big data analytics, alongside those with lower levels of integration, such as additive manufacturing and autonomous robots. The findings offer critical insights for organizations transitioning toward digitalized supply chains. By contributing to the academic discourse on the prioritization and implementation of Industry 4.0 practices, this research offers a valuable resource for scholars and decision-makers working to navigate the complexities of digital transformation in supply chains.
    @article{samahmahmah-2025-1674,
  title={Evaluating Industry 4.0 Technologies for Supply Chain Optimization: A Best-Worst Method Approach},
  author={Samah  Mahmah and Salah  Oulfarsi and Ikram Ait  Hammou},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={215--223},
  doi={10.31387/oscm0610463}
}
    Samah  Mahmah, Salah  Oulfarsi, Ikram Ait  Hammou (2025). Evaluating Industry 4.0 Technologies for Supply Chain Optimization: A Best-Worst Method Approach. Operations and Supply Chain Management: An International Journal, 18(2), 215-223. https://doi.org/10.31387/oscm0610463
  7. An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System
    • Chunan Tong
    Abstract Full PDF Cite DOI: 10.31387/oscm0610469
    As supply chain management continues to evolve, efficient inventory management systems have become increasingly crucial. However, traditional manual methods often struggle to meet the complexities of modern market demands, particularly when it comes to data accuracy, delays in monitoring, and the heavy reliance on subjective experience for forecasting. This study introduces an intelligent, semi-automated barcode-based inventory management system designed to overcome these challenges. The system integrates barcode technology with a distributed architecture, combined with big data analytics and machine learning for real-time tracking and accurate inventory predictions. Its performance has been validated through multiple simulation tests, where it has outperformed traditional RFID technology in certain cases. Through careful system design, technology exploration, and validation, this research demonstrates the significant potential of this intelligent system in improving inventory management efficiency and accuracy
    @article{chunantong-2025-1679,
  title={An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System},
  author={Chunan  Tong},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={223--239},
  doi={10.31387/oscm0610469}
}
    Chunan  Tong (2025). An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System. Operations and Supply Chain Management: An International Journal, 18(2), 223-239. https://doi.org/10.31387/oscm0610469
  8. The Impacts of Digitisation on Freight Forwarders within the Shipping Industry
    • Lucy Dowling
    • Abubaker Haddud
    Abstract Full PDF Cite DOI: 10.31387/oscm0610470
    Freight forwarders within the shipping industry have been noted as being conservative in embracing and using digital tools and lag behind other industries in their quest for digital transformation. The study aims to examine the impacts of digitization by understanding and analysing employees’ levels of agreement regarding their knowledge of technologies and the associated barriers/ challenges and drivers/ benefits of digitisation. An online survey was used to collect data from 120 participants from container freight forwarders and the survey consisted of four main sections, the results of which were analysed through statistical software to test their validity and reliability. Through this research, we were able to confirm the high level of support and knowledge that employees of container freight forwarders hold regarding digitisation. Areas of key focus were determined, such as strong management required and investment in training and skills to make the digital transformation and the company's efforts a success. To the best of our knowledge, this study is the first of its kind to examine specific container freight forwarders’ opinions on the impacts of digitisation. The results will benefit freight forwarders and other stakeholders within the shipping industry who want to be aware of the challenges and the beneficial impacts.
    @article{lucydowling-2025-1680,
  title={The Impacts of Digitisation on Freight Forwarders within the Shipping Industry},
  author={Lucy  Dowling and Abubaker  Haddud},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={240--255},
  doi={10.31387/oscm0610470}
}
    Lucy  Dowling, Abubaker  Haddud (2025). The Impacts of Digitisation on Freight Forwarders within the Shipping Industry. Operations and Supply Chain Management: An International Journal, 18(2), 240-255. https://doi.org/10.31387/oscm0610470
  9. An AI-IoT Inventory Management Approach to Optimize Cold Storage Replenishment and Energy Cost
    • Gregorios Ferrari Pramudika
    • Ririn Diar Astanti
    • The Jin Ai
    Abstract Full PDF Cite DOI: 10.31387/oscm0610471
    To support retail sustainability, retailers must also focus on energy efficiency. Cold storage is a major factor in the retail sector's rising energy use. Improving cold storage saves energy. Poorly filled or used cold storage wastes energy and increases costs. This situation pertains to retail inventory management. Therefore, retail inventory management is crucial. This study aims to develop an Artificial Intelligence (AI)-and IoT (Internet of Things)-based inventory management model to improve cold storage replenishment efficiency while considering energy costs. The suggested solution includes an inventory management model and application that alerts retail managers to quickly replace cold storage items and keep inventory levels steady. The suggested inventory management approach comprises two phases: development and execution. The development phase begins with data collection, using sensors and a microcontroller to monitor cold storage. The suggested framework incorporates the following data: 1) cold storage door opening and closing times, 2) electrical energy consumption, 3) cold storage temperature, 4) product weight, and 5) product photos. The Convolutional Neural Network (CNN) is applied to categorize visual input for generating refill alerts. Experimental results suggest that the proposed strategy effectively reduces total energy costs per unit by 18.1% and total inventory costs by 6.9% when compared to the typical periodic review inventory management model.
    @article{gregoriosferraripramudika-2025-1681,
  title={An AI-IoT Inventory Management Approach to Optimize Cold Storage Replenishment and Energy Cost},
  author={Gregorios Ferrari  Pramudika and Ririn Diar  Astanti and The Jin  Ai},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={256--274},
  doi={10.31387/oscm0610471}
}
    Gregorios Ferrari  Pramudika, Ririn Diar  Astanti, The Jin  Ai (2025). An AI-IoT Inventory Management Approach to Optimize Cold Storage Replenishment and Energy Cost. Operations and Supply Chain Management: An International Journal, 18(2), 256-274. https://doi.org/10.31387/oscm0610471
  10. The Role of AI in Driving Accountability and Transparency in Global Supply Chains: The Fragmented Bridge Between Research and Practice
    • Boutayna Elghomri
    • Faycal Messaoudi
    • Nihal Touti
    Abstract Full PDF Cite DOI: 10.31387/oscm0610472
    Amid growing concerns over ethical sourcing, regulatory compliance, and operational transparency, global supply chains are under mounting pressure to adopt intelligent and accountable systems. Artificial Intelligence (AI) has emerged as a pivotal enabler in addressing these challenges, particularly within the context of digital transformation and sustainability. This study conducts a comprehensive bibliometric analysis to examine the evolving role of AI in driving accountability and transparency within global supply chains, while critically exploring the persistent gap between academic research and practical implementation. Drawing on 421 peer-reviewed articles published between 1998 and January 2025, extracted from Scopus and Web of Science, the analysis utilizes VOSviewer for network visualization and clustering. The findings reveal an exponential growth of research in this domain, particularly after 2020, driven by technological advancements and increasing regulatory pressures for transparency and ethical governance. The geographical distribution of contributions highlights India, China, and the United States as dominant knowledge producers, alongside growing engagement from emerging economies such as Malaysia and Morocco. The keyword analysis and thematic clustering uncover four key research streams: foundational AI technologies (e.g., blockchain, machine learning, IoT), AI-driven decision-making, blockchain-AI integration for supply chain transparency, and AI-IoT synergy within sustainable and circular economy frameworks. Beyond mapping intellectual structures and research trends, this study identifies a persistent gap between theory and practice. While technological solutions have advanced rapidly, organizational readiness, governance challenges, and ethical considerations remain underexplored. Addressing these gaps requires future research to adopt more integrative frameworks that link technological innovation with managerial practices, regulatory frameworks, and socio-cultural contexts, particularly in emerging markets. This study contributes to the literature by providing a structured overview of global research developments and by offering valuable insights for academics, practitioners, and policymakers aiming to foster responsible, inclusive, and context-sensitive AI adoption in supply chain governance.
    @article{boutaynaelghomri-2025-1682,
  title={The Role of AI in Driving Accountability and Transparency in Global Supply Chains:  The Fragmented Bridge Between  Research and Practice},
  author={Boutayna  Elghomri and Faycal  Messaoudi and Nihal  Touti},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={275--287},
  doi={10.31387/oscm0610472}
}
    Boutayna  Elghomri, Faycal  Messaoudi, Nihal  Touti (2025). The Role of AI in Driving Accountability and Transparency in Global Supply Chains:  The Fragmented Bridge Between  Research and Practice. Operations and Supply Chain Management: An International Journal, 18(2), 275-287. https://doi.org/10.31387/oscm0610472
  11. A Stochastic Programming Model to Mitigate Disruption Effects on the Drug Distribution System Under an Autonomous Vehicle Fleet
    • Maryam Farahani
    • Seyed Hessameddin Zegordi
    • Ali Husseinzadeh Kashan
    • Ehsan Nikbakhsh
    Abstract Full PDF Cite DOI: 10.31387/oscm0610473
    One of the most important problems of supply chain management is distribution management. The fleet composition and size play a significant role in reducing distribution costs. Recently, infectious diseases such as the COVID-19 pandemic have affected human resources, leading to the employment of autonomous vehicles (AVs) getting more attention. This paper proposes a stochastic programming model for the fleet size and mixed vehicle routing problem (FSMVRP), including autonomous and conventional vehicles (CVs) under human resource disruption. An accelerated version of the Progressive Hedging Algorithm (PHA) is applied to solve that. According to numerical results, the obtained value of the stochastic solution (VSS) suggest the model could decrease the fleet cost by about 11 percent on average. Analysis of the expected value of perfect information (EVPI) shows that using the stochastic programming approach for fleet management could minimize the total cost of the fleet by 22%, on average. In addition, the sensitivity analysis shows that using AVs under infectious disease disruption is advisable to improve performance.
    @article{maryamfarahani-2025-1683,
  title={A Stochastic Programming Model to Mitigate Disruption Effects on the Drug Distribution System Under an Autonomous Vehicle Fleet},
  author={Maryam  Farahani and Seyed Hessameddin  Zegordi and Ali Husseinzadeh  Kashan and Ehsan  Nikbakhsh},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={288--298},
  doi={10.31387/oscm0610473}
}
    Maryam  Farahani, Seyed Hessameddin  Zegordi, Ali Husseinzadeh  Kashan, Ehsan  Nikbakhsh (2025). A Stochastic Programming Model to Mitigate Disruption Effects on the Drug Distribution System Under an Autonomous Vehicle Fleet. Operations and Supply Chain Management: An International Journal, 18(2), 288-298. https://doi.org/10.31387/oscm0610473
  12. AI in Supply Chain: Techniques, Applications, Real-World Cases and Benefits under SCOR Framework
    • Hoang Bao Pham
    • Petr Bris
    Abstract Full PDF Cite DOI: 10.31387/oscm0610474
    This article focuses on practical perspectives of Artificial Intelligence (AI) applications in Supply Chain Management by exploring commonly used AI techniques, use cases and benefits of applying AI in Supply Chain Management with real-world examples from multinational corporations like DHL, IBM, Walmart, Amazon, Google, among others. The findings are grouped according to the four stages of the SCOR (Supply Chain Operations Reference) framework, i.e plan, source, make, deliver, to facilitate visualization. We find that AI techniques including Neural Networks, Genetic Algorithms, Support Vector Machines, Reinforcement Learning, Fuzzy Logic, and Natural Language Processing are applied to enhance supply chain efficiencies, lower costs, increase profits, improve customer satisfaction, save operational time, reduce potential disruption, better suppliers/customers relationships, improve product quality, enhance safety, and shorten lead times... These stem from nine benefit groups, namely PLAN (demand forecasting, inventory optimization, supply risk mitigation), SOURCE (procurement, supplier selection), MAKE (product quality assurance, smart warehouse management, predictive maintenance), DELIVER (route optimization, dynamic pricing, and last mile delivery, and customer service). Limitations and future research directions are discussed.
    @article{hoangbaopham-2025-1684,
  title={AI in Supply Chain: Techniques, Applications, Real-World Cases and Benefits  under SCOR Framework},
  author={Hoang Bao  Pham and Petr  Bris},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={300--316},
  doi={10.31387/oscm0610474}
}
    Hoang Bao  Pham, Petr  Bris (2025). AI in Supply Chain: Techniques, Applications, Real-World Cases and Benefits  under SCOR Framework. Operations and Supply Chain Management: An International Journal, 18(2), 300-316. https://doi.org/10.31387/oscm0610474
  13. Artificial Intelligence, Cyber-Physical Systems, and Decision Making in Operations and Supply Chain Management
    • Mansur Arief
    • Zhiyuan Huang
    • Suntichai Kotcharin
    • Ivan Kristianto Singgih
    Abstract Full PDF Cite DOI: 10.31387/oscm0610475
    The confluence of artificial intelligence (AI), autonomous cyber-physical systems (CPS), and sophisticated decision-making technologies represents more than incremental advancement in operations and supply chain management (OSCM)-it signals a fundamental paradigm shift that redefines how organizations create, deliver, and capture value in interconnected global networks. This transformation occurs at a pivotal moment when supply chain vulnerabilities exposed by recent global disruptions intersect with unprecedented technological capabilities. This special issue advances the OSCM research frontier through methodologically diverse investigations that collectively reveal three critical insights regarding the complex interplay within technology-enabled supply chain transformation. As guest editors, we believe this collection establishes new foundations and provides actionable guidance for the OSCM community. The research demonstrates that the future of supply chain management lies in orchestrating intelligent systems that amplify human capabilities while remaining grounded in organizational realities. We extend our gratitude to the authors, reviewers, and editorial team whose contributions made this collection possible.
    @article{mansurarief-2025-1685,
  title={Artificial Intelligence, Cyber-Physical Systems, and Decision Making in Operations and Supply Chain Management},
  author={Mansur  Arief and Zhiyuan  Huang and Suntichai  Kotcharin and Ivan Kristianto  Singgih},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2025},
  volume={18},
  number={2},
  pages={318--319},
  doi={10.31387/oscm0610475}
}
    Mansur  Arief, Zhiyuan  Huang, Suntichai  Kotcharin, Ivan Kristianto  Singgih (2025). Artificial Intelligence, Cyber-Physical Systems, and Decision Making in Operations and Supply Chain Management. Operations and Supply Chain Management: An International Journal, 18(2), 318-319. https://doi.org/10.31387/oscm0610475
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