Kharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Work System Synergy and Project Performance in Oil and Gas Construction: A Contingency and Dynamic Capabilities Perspective464478296410.22034/ijsom.2025.109496.2443ENNsikanJohnDepartment of Business Management, James Hope University Business School, Lagos, Nigeria0000-0001-7682-3909PeterNwaguruDepartment of Project Management, European Global School, Paris-FranceBenjaminAmehDepartment of Management, Royal Roads University, Victoria, BC, CanadaJournal Article20220113Notwithstanding decades of research on project integration, limited empirical evidence exists on how internal coordination mechanisms influence performance outcomes in complex, project-based environments, particularly within developing economies. This study addresses this gap by examining how administrative integration and synergistic alignment- two dimensions of work system synergy impact project operational performance in the oil and gas construction sector. Drawing on the Contingency and Dynamic Capabilities Theories, the study develops and tests a conceptual model that links integration mechanisms to executional outcomes such as project timeliness, cost efficiency, and stakeholder satisfaction. Survey data were obtained from 33 oil and gas construction projects firms in Nigeria, and Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the hypothesized relationships. Findings indicate that both administrative integration and synergistic alignment positively influence project operational performance. These results highlight the role of integration as an adaptive and reconfigurable capability that enables firms to manage complexity and uncertainty effectively. This study contributes to project and operations management literature by extending the understanding of how internal integration strategies function as dynamic capabilities in resource-constrained, project-based environments. It also offers practical implications for managers seeking to improve executional outcomes through coordinated governance and aligned workflows.<strong><em> </em></strong>http://www.ijsom.com/article_2964_76e344bad40cd40e20f9c65934fd362e.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Artificial Intelligence Capabilities and Their Influence on Supply Chain Resilience and Performance: Insights from Agri-Food Firms in an Emerging Economy479498296510.22034/ijsom.2025.110879.3456ENChuyen TrungTranFaculty of Business Management0009-0004-1666-3287PremkumarRajagopalMalaysia University of Science and Technology (MUST)Trinh Tran XuanPhanNam Can Tho University, Can Tho, Vietnam0009-0002-1411-890XJournal Article20250826Artificial Intelligence (AI) is emerging as a crucial tool to enhance supply chain performance and resilience in global agricultural supply chains, which are being disrupted by market volatility and climate change. This study examines how AI capabilities are used in agribusinesses in Vietnam's Mekong Delta, a growing industry. The study intends to examine how supply chain collaboration (SC), environmental uncertainty (EU), and AI technological compatibility (AT) affect Willingness to Adopt AI (WA), as well as how these factors affect Supply Chain Resilience (SR) and Supply Chain Performance (SP). Utilizing the Resource-Based View (RBV) and the Technology–Organization–Environment (TOE) framework, the study sent a questionnaire to 223 businesses, obtaining a 94% valid response rate. This study employs SmartPLS software to perform analysis based on the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings indicate that AT, SC, and EU are significant determinants of the readiness for WA, with T-Values of 4.822, 6.697, and 4.378, respectively. At the same time, WA has the strongest impact on SR (T-Value = 8.031) and also influences SP (T-Value = 4.482). The results emphasize the potential of AI to reduce disruptions and enhance operational efficiency, particularly in emerging markets. The study is constrained by its geographical focus on the Mekong Delta and the reliance on cross-sectional survey data, which limit generalizability and dynamic analysis. Future studies should broaden their focus and investigate certain AI technologies to enhance comprehension of AI applications inside agricultural supply chains.http://www.ijsom.com/article_2965_3a01afd64e3a955b93f646a6afc82720.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Cost Analysis in Product-Service Systems: A Systematic Literature Review499523296610.22034/ijsom.2025.110613.3262ENHamid RezaForouzanpourIndustrial management & Information technology department, Faculty of management & accounting, Shahid Beheshti University (SBU), Tehran, Iran.AshkanAyough0000-0001-7706-2101A.Alem-TabrizDepartment of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, IranMostafaZandiehDepartment of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, G.C., Tehran, Iran.Journal Article20241026This paper delves into the cost analysis methods of Product-Service Systems (PSS). With Earth's limited resources and the rise of systems like the circular economy, the development of product-service systems, as exemplars of sustainability-driven servitization solutions, is inevitable. By reviewing subject areas related to cost analysis in product-service systems literature published in quality journals, this study aims to identify and discuss current themes, how cost analysis is addressed in different PSS business model implementation tactics, and propose areas for future research. In this research, a narrow but in-depth bibliometric analysis of product-service systems cost analysis has been conducted. By examining research relevant to the scope of this paper, some primary areas for future research including value-based pricing using utility theory, calculation of Eco-Cost Value, and utilizing simulation models for uncertainty management, have been extracted. A narrow but in-depth bibliometric analysis of product-service systems cost analysis can add value to the future development of this research area by recognizing leading scholars in the field.http://www.ijsom.com/article_2966_b2fbe798909b69896c33ee22577bda6f.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Interaction of Flight Scheduling and Ticket Pricing: A Modern Data-Driven Approach Based on Distributionally Robust Optimization and Bi-Level Programming524546296910.22034/ijsom.2025.110846.3431ENAliPapiSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranMir SamanPishvaeeSchool of Industrial Engineering, Iran University of Science and Technology0000-0001-6389-6308HadiSahebiDepartment of Industrial Engineering, Faculty of System and e-Commerce Engineering, Iran University of Science &amp; Technology, Tehran, IranJournal Article20250710In airline planning, tactical decisions related to flight schedule design and fleet assignment play a pivotal role in enhancing operational efficiency and maximizing revenue. On the other hand, ticket pricing, directly influencing market share, is inherently affected by the tactical flight timetable, market uncertainties, and passenger choice behavior. To jointly optimize tactical scheduling decisions and ticket pricing policies, and create optimal interaction between them, this paper proposes a modern data-driven decision-making framework that blends Distributionally Robust Optimization (DRO) with Bi-Level Programming (BLP). In this framework, leveraging historical data and machine learning algorithms, a distributional ambiguity set is first constructed to model uncertainty within the DRO framework. The BLP formulation then captures the interaction between flight scheduling (upper level) and ticket pricing (lower level). Additionally, passengers’ choice behavior is incorporated using a Multinomial Logit (MNL) discrete choice model. To address the computational complexity, a column-and-constraint generation (CCG) algorithm is adopted, enabling model decomposition and enhancing computational efficiency. Finally, the proposed model and solution framework are validated through a case study and a series of numerical experiments. Numerical results demonstrate that, compared to classical approaches, the proposed framework significantly improves market share and airline revenue, ensures robustness against uncertainty and passenger behavior variability, and enhances computational tractability.http://www.ijsom.com/article_2969_246efd8c1ec613b21a0cee1879770a78.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101A New Multi-Objective Optimization Algorithm to Solve the Load Balancing Problem in Mobile Cloud Computing547562296810.22034/ijsom.2025.110770.3376ENSaraAlipourDepartment of Computer Engineering, University of Birjand, Birjand, IranHamidSaadatfarDepartment of Computer Engineering, University of Birjand, Birjand, IranMahdiKhazaie PoorComputer Engineering Department, Birjand Branch, Islamic Azad University, Birjand, IranJournal Article20250420Mobile Cloud Computing (MCC) has emerged as a promising paradigm to overcome the computational and energy limitations of mobile devices by offloading intensive tasks to the cloud. However, determining optimal task offloading and scheduling strategies remains a challenging multi-objective optimization problem due to the heterogeneous nature of cloud resources and constraints such as execution time, energy consumption, and bandwidth. This paper proposes a novel Multi-Parallel Objective Imperialist Competitive Algorithm (MPICA) to efficiently address task scheduling in MCC environments. By leveraging parallel processing, MPICA enhances exploration and exploitation in the solution space, leading to improved convergence speed and load balancing. The performance of MPICA was evaluated against three benchmark algorithms: Round Robin (RR), Genetic Algorithm (GA), and the standard Imperialist Competitive Algorithm (ICA). Simulation results demonstrate that MPICA achieves up to reduction in makespan and improvement in energy efficiency, while maintaining better scalability in large-scale task sets. These findings highlight the potential of MPICA as a robust and scalable solution for multi-objective task scheduling in MCC scenarios.http://www.ijsom.com/article_2968_6f2c22b8af262951b7836201f8a2f11f.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Operations Research and Artificial Intelligence for Supply Chain Planning: A Systematic Literature Review563600296710.22034/ijsom.2025.110642.3282ENImaneEL MARIAMIUniversit&eacute; hassan 2, ENSEM, Casablanca, Morocco0009-0006-7769-7530Journal Article20241210This systematic review analyzes 55 peer-reviewed scientific articles published between 2020 and 2024, examining the application of Artificial Intelligence (AI) in supply chain optimization and planning. The study focuses on AI methodologies, their implementation across various industrial sectors, and their impact on enhancing operational efficiency, reducing logistics costs, and improving adaptability to market dynamics. It highlights how AI-driven approaches are transforming traditional supply chain management practices through real-time decision-making, predictive analytics, and automation. The review identifies key advancements in AI technologies, such as machine learning, deep learning, and reinforcement learning, along with their applications in demand forecasting, inventory management, and transportation planning. Additionally, it explores critical challenges and barriers to adoption, including data quality issues, technological integration complexities, and organizational readiness, while emphasizing existing research gaps. To address these gaps, the study proposes a novel AI-based framework, providing actionable insights for researchers, industry professionals, and policymakers aiming to drive innovation and resilience in supply chain management.http://www.ijsom.com/article_2967_4403f6af7fc4ac1bd2daa7dc981374d3.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135912420251101Mapping Procurement 4.0: A Heatmap Framework from a Systematic Literature Review601638297010.22034/ijsom.2025.110710.3328ENJohannesDirnberger-WildInstitute of Industrial Management, Department of Management, University of Applied Sciences JOANNEUM, Kapfenberg, Austria0009-0004-5373-5753JörgSchweigerInstitute of Industrial Management, Department of Management, University of Applied Sciences JOANNEUM, Kapfenberg, AustriaMartinTschandlInstitute of Industrial Management, Department of Management, University of Applied Sciences JOANNEUM, Kapfenberg, Austria0000-0002-0495-9556GuidoNassimbeniPolytechnic Department of Engineering and Architecture, Full Professor of Management Engineering, University of Udine, Italy0000-0002-1532-2980Journal Article20250220Procurement 4.0 represents a fundamental change in procurement, driven by the adoption of advanced Industry 4.0 technologies. However, despite growing academic interest and recognized benefits, including efficiency gains through process automation, technology implementation faces significant delays in procurement. As a result, opportunities, such as mitigating labor shortages, are underutilized. Persistent barriers, including limited awareness, and uncertainty regarding the most effective technologies, continue to impede progress. Against this backdrop, this paper proposes a conceptual heatmap framework to support the integration of advanced technologies into procurement. By systematically mapping Procurement 4.0 applications across sub-processes, the heatmap provides a comprehensive overview of use cases and reveals existing research gaps. A Systematic Literature Review (SLR), supplemented by thematic, content, and frequency analyses, examines 275 applications categorized by automation potential. The findings reveal dominant technology clusters in the academic debate, yet a persistent gap between research and practice remains. The most extensively studied cluster demonstrates only moderate automation, indicating that research tends to position technology as a decision-support tool rather than a driver of full automation. In this context autonomous procurement remains an aspirational goal rather than an established reality. The introduced heatmap offers researchers a systematic and current synthesis of key applications and unresolved research questions, while providing practitioners with a structured foundation for implementing Procurement 4.0 technologies.http://www.ijsom.com/article_2970_6bef121b9ee46c52e584984d638ce773.pdf