http://www.ijsom.com/ International Journal of Supply and Operations Management en daily 1 Sat, 01 Nov 2025 00:00:00 +0330 Sat, 01 Nov 2025 00:00:00 +0330 http://www.ijsom.com/article_2964.html Notwithstanding 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. http://www.ijsom.com/article_2965.html Artificial 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_2966.html This 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_2969.html In 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_2968.html Mobile 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_2967.html This 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_2970.html Procurement 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_2971.html Objective: With the increasing penetration of distributed energy resources (DERs), peer-to-peer (P2P) energy trading has emerged as a promising mechanism to enhance renewable energy utilization and market efficiency. This study aims to design a P2P electricity market for grid-connected microgrids that coordinates local trading with retail and wholesale markets while accounting for geographical distance and demand response programs. Methods: A two-stage optimization framework is proposed. In the first stage, a mixed-integer linear programming (MILP) model determines the optimal neighborhood set of prosumers by maximizing renewable energy consumption and minimizing the geographical distance between trading peers. In the second stage, a mixed-integer nonlinear programming (MINLP) model is developed to optimize energy exchanges, battery storage, and pricing decisions, with the objectives of maximizing retailer profit and minimizing prosumer costs. The model incorporates time-based and incentive-based demand response programs and is validated using real residential data from Iran. Results: The numerical results show that limiting P2P transactions to geographically closer peers improve local renewable energy utilization. Sensitivity analysis on time-based DR programs indicates that the optimal pricing mechanism applies real-time pricing (RTP) to both the retail and P2P markets. Conclusion: The proposed two-stage P2P optimization framework enhances renewable energy utilization by prioritizing local trading and RTP-based pricing. Results indicate that applying real-time pricing in both retail and P2P markets increases renewable energy share and economic efficiency, while providing actionable insights for sustainable microgrid and P2P market design. http://www.ijsom.com/article_2972.html Objective: Accurate forecasting of customer demand is necessary to optimize the efficiency of a supply chain, maximize profits through reduced inventory costs, and increase customer satisfaction. This research presents a new machine learning methodology based on the CRISP-DM for customer order forecasting that is both interpretive and interpretable and validates it with a real-world application from the Ofogh Kourosh Company, which offers the largest number of physical retail locations in Iran. Methods: The dataset analyzed for this research contained 844,275 sales transactions from 40 separate physical locations. Six advanced ensemble machine learning models were developed to forecast customer order demand. A beneficial factor of this research was the ability to automate hyperparameter tuning of the six predictive models using the Optuna framework. The performance of the predictive models was then evaluated using MAE, RMSE, MSE, and R² metrics. Results: Based on R² score, LightGBM was the most accurate predictive model with an R² score of 0.536. Feature importance analysis from LightGBM demonstrated that the three factors that would most determine customer order demand were the percentage of discount, price, and store location. Conclusion: This research contributes both theoretically and practically to the development of a forecast model that is regionally, culturally, and contextually relevant within the Iranian retail marketplace. Compared to the literature, this study uses actual transactional data with ML models to narrow the theory-practice gap. Future research should emanate from this development, incorporating external influences such as climate, advertising, and macroeconomic influences for even greater forecast accuracy http://www.ijsom.com/article_2973.html Objective: This study aims to address post-earthquake emergency response challenges by emphasizing the critical role of timely debris removal operations in ensuring rapid accessibility for the rescue team thereby reducing casualties, and mitigating the operational risks faced by rescuers in post-disaster environments under uncertain conditions. The objective is to develop a decision-making approach to determine the visiting order of critical nodes, the travel path between consecutive critical nodes, and the blocked edges to be cleared during debris removal operations, whose effectiveness remains stable across all plausible realizations of uncertain parameters while dealing with multiple objectives. Methods: To deal with uncertainty, a robust routing mathematical model is presented to help debris removal teams to find suitable routes subject to three objective functions including minimizing debris removal team’s travelling time plus debris removal operations time, minimizing the risk of rescuers in critical regions and maximizing the total benefit gained by accessing to damaged and critical regions of the city thereby reducing the loss of lives. To solve the proposed multi-objective model while simultaneously handling the uncertainty of parameters, a robust multi-objective optimization approach with augmented epsilon constraint is proposed in this paper. To test the efficiency of the proposed model of this study, real data taken from Rudbar-Manjil devastating Earthquake (20 June 1990, Iran) is used as a case study. The results identified the most effective routes and operational sequences for debris removal teams under uncertainty, with a fuzzy decision-making method selecting the preferred Pareto-optimal solution. Results: The analysis determined the optimal visiting sequence of critical nodes for debris removal operations. For each pair of consecutive critical nodes, the most efficient routes were identified for the debris removal teams. Additionally, the specific road segments on which debris clearance should be performed were mapped and prioritized. Sensitivity analysis confirmed the robustness of the proposed model across different budgets of uncertainties.Conclusion:  This research provides a practical framework for optimizing debris removal operations under real-world uncertainties and supporting robust decision-making, which can improve the efficiency of disaster response and inform planning for future emergency management scenarios. The findings indicate that the model is versatile and can be adapted to other disaster scenarios by adjusting geographical parameters, resource constraints, and uncertainty modeling.  http://www.ijsom.com/article_2974.html Objective: Despite the growing integration of gamification in digital commerce, its impact on consumer stickiness remains underexplored, particularly in emerging markets. This study develops and empirically tests a framework examining how specific gamification elements in e-commerce platforms—badge upgrades, random rewards, and gamified design—affect consumer stickiness through perceived value (hedonic and utilitarian) and social interaction. The research aims to clarify the mechanisms through which gamification enhances customer loyalty and continued platform engagement in the Vietnamese context.Methods: A questionnaire-based survey was conducted with 310 consumers who had participated in gamified activities on e-commerce platforms in Vietnam. The study integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine linear relationships and Artificial Neural Networks (ANN) to capture nonlinear interactions within the proposed model. This dual-stage analytical approach enhances the robustness and predictive power of the findings.Results: The findings show that gamified design and badge upgrades positively influence both perceived hedonic and utilitarian values, while random rewards significantly affect perceived hedonic value only. Social interaction is significantly influenced by gamified design but not by badge upgrades or random rewards. Perceived value and social interaction, in turn, contribute to consumer stickiness on e-commerce platforms.Conclusion: The study confirms that different gamification elements generate distinct effects on consumer perceptions and stickiness. By highlighting the mediating roles of hedonic and utilitarian values as well as social interaction, the research contributes to the literature on smart e-commerce and gamification. The findings suggest that businesses should strategically design gamification features that simultaneously enhance functional benefits and experiential enjoyment to strengthen long-term customer retention on digital platforms. http://www.ijsom.com/article_2975.html Objective: This study evaluates the impact of integrating Digital Twin technology with Lean methodologies on the operational efficiency of a firm in the Peruvian metal‑mechanic sector, focusing on a mill‑bolt production line. The company currently experiences substantial operational challenges, including high variability in production times, an inadequate facility layout, and limited technological integration. These deficiencies contribute to a low overall efficiency level of 44.04%. The purpose of this research is to demonstrate how the combined application of Digital Twin–based modeling and Lean process improvement strategies can enhance system performance, reduce operational inefficiencies, and strengthen organizational productivity. Methods: This study employs an applied research approach using a quasi‑experimental design. Data was collected through informal conversations with production operators and the review of historical production records. The methodological process was structured in two phases. In the first phase, model validation was conducted through pilot experimentation focused on Lean methodologies. In the second phase, the proposed enhancements were evaluated and validated through computational simulations, enabling a controlled assessment of their impact on system performance.  Results: The findings indicate an increase in operational efficiency from 44.04% to 61.66%, demonstrating the effectiveness of integrating Digital Twin technology with Lean methodologies. These results support the significance of the combined model in enhancing system performance and reducing operational inefficiencies Conclusion: The results of this study underscore the substantial impact that a comprehensive, integrated intervention can have on the operational efficiency of metal‑mechanic production environments. The research highlights the critical value of uniting traditional process‑improvement approaches with emerging digital tools. This integration not only enhances decision‑making and process control but also strengthens the organization’s capacity for continuous improvement and long‑term competitiveness. http://www.ijsom.com/article_2976.html Objective: Supply chain management in dynamic environments requires advanced digital technologies to enhance transparency, security, and operational efficiency. Blockchain technology has emerged as a promising solution for improving traceability and trust in intelligent logistics ecosystems. The objective of this study is to evaluate and compare blockchain platform alternatives using a structured multi-criteria decision-making framework in order to support technology selection in modern logistics systems. Methods: This research applies a comparative multi-criteria decision-making approach integrating Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), AHP-TOPSIS, and Fuzzy AHP-Fuzzy TOPSIS methods. A hierarchical evaluation model was developed. Expert judgments and literature-based criteria were used to determine weights and assess the relative performance of blockchain platform alternatives. Results: The evaluation results demonstrate consistent rankings across both crisp and fuzzy decision models. Sensitivity analysis further confirms the robustness of the ranking results under different weighting scenarios. Conclusion: The findings highlight the importance of scalability, interoperability, and transparency when selecting blockchain platforms for intelligent logistics ecosystems. The proposed framework provides decision-makers with a systematic evaluation tool that can support strategic technology adoption and improve decision quality in supply chain digital transformation initiatives.