http://www.ijsom.com/ International Journal of Supply and Operations Management en daily 1 Sun, 01 Feb 2026 00:00:00 +0330 Sun, 01 Feb 2026 00:00:00 +0330 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 inefficienciesConclusion: 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. 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_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_2977.html Objective: Researchers established an EOQ model for degrading goods with trade credit policy under invariant, stock-linked, exponential, and linearly time-dependent demand. The analysis imposed a condition for quadratic time-dependent demand. The mathematical model is developed to obtain total profit by considering two different cases. One common observation is that the demands for the goods that are on display in the supermarket fluctuate. In this study demand is measured to be quadratic time-sensitive. The EOQ is generally applied to locate the most favorable order quantity in order to maximize the total supply cost.Methods: The EOQ model considers that the total order for an article is received into inventory at one specified time which is when the EOQ model assumes that the products are produced.Results: There are numerous costs acquired in the existent practice such as ordering cost, sales revenue, carrying cost, interest earned, and interest charged, etc.Conclusion: The implementation of the sensitivity test and an optimal solution helps to confirm how the mathematical model will generate total profit in two different ways. The EOQ method is used to identify the order quantity that maximizes total supply costs in the order’s viewpoints. This should assist with future management of degraded products under a trade credit scheme, as well as advance the accuracy and reliability of making Inventory-related decisions due to demand fluctuations. http://www.ijsom.com/article_11087.html Objective: Welding quality strongly affects product performance, durability, scrap rate, and production cost. This study aims to develop an integrated data-driven framework combining Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Design of Experiments (DOE) to optimize welding parameters and improve weld quality in an industrial manufacturing process. Methods: Historical production data from a turbine manufacturing company in Iran were analyzed. The investigated welding parameters included current intensity, welding position, welding speed, preheat temperature, and electrode diameter. A 2k factorial design was applied to evaluate their effects on weld depth, which was maximized, and weld width, which was minimized. Multi-response optimization was conducted using response surface methodology and desirability functions, with different weighting schemes assigned to the response variables. Results: The findings revealed that current intensity was the most influential parameter affecting weld quality. Moreover, a significant interaction effect was identified between current intensity and welding position, indicating that the joint effect of these parameters plays an important role in determining weld profile characteristics. Compared with the historical data, the optimized parameter settings improved weld depth by an average of 13.77% and enhanced weld width by an average of 3.56%. Following the implementation of the optimal parameter values in the production process, the average scrap rate decreased from 19.8% to 13.1% over a six-month period. Conclusion: The proposed framework effectively improved weld quality and production performance. The reduction in scrap rate and enhancement of welding responses resulted in an estimated 3–4% decrease in production costs, demonstrating the practical value of the approach for industrial welding optimization. http://www.ijsom.com/article_11088.html Objective: High wheat flour imports pose a high risk to the sustainability of the food industry in the event of trade wars and product boycotts by exporting countries, creating an urgent need to develop local flour as an alternative to imported wheat flour consumption. This study aims to identify and analyze sustainability related performance challenges within the tapioca and Mocaf flour supply chains in West Java. Methods: The research applies an integrated of the SCOR DS and TOPSIS to determine priority solutions for improving the KPI performance matrix based on key actors in the supply chain. A stratified sampling method was used in West Java, specifically in Bogor, Sukabumi, Bandung, Garut, and Sumedang. Results: The findings show that tapioca SMEs represent the ideal solution node for improving supply chain performance, with a preference value of 0.50. Based on this ideal solution, performance improvement in the tapioca and Mocaf agro‑industry supply chain in West Java should focus on coarse tapioca SMEs, prioritizing Source Availability in the KPI AM.1.1 Cash to Cash Cycle. Conclusion: Enhancing the performance of tapioca and Mocaf flour supply chains particularly by strengthening Source Availability and shortening the AM.1.1 Cash to Cash Cycle at coarse tapioca SMEs will reinforce the economic resilience of local flour‑based agro‑industrial actors. These improvements foster inclusive and sustainable industrial development and support the achievement of Sustainable Development Goal 9 on Industry, Innovation and Infrastructure. http://www.ijsom.com/article_11108.html Objective: In the fast-fashion industry, rapidly changing customer preferences create significant challenges for demand forecasting and inventory management. Social media platforms have emerged as real-time sources of consumer sentiment and trend information. This study analyzes social network search patterns to build a customer-centric framework that enhances supply chain responsiveness and operational efficiency. Methods: A real-time case study of a mid-sized Indian apparel brand demonstrates the integration of social media search analytics with supply chain systems. Using natural language processing (NLP), sentiment analysis, keyword clustering, search trend mapping, and real-time data pipelines, the framework analyzes trending queries and hashtags to identify emerging customer preferences. This enables production and inventory decisions to be aligned more effectively with market demand. Results: The social media-driven framework improved demand forecasting accuracy and enhanced supply chain responsiveness. As a result, the company achieved a reduction in stockouts, improved product availability, and increased customer satisfaction. Integrating optimized search suggestion engines with inventory and production systems enabled effective alignment of consumer demand signals with supply chain operations. Conclusion: The findings indicate that social network search behavior provides valuable real-time market intelligence for supply chain optimization. The proposed approach enables agile and responsive supply chains that adapt to changing customer preferences. Furthermore, AI-driven social media analytics can enhance supply chain resilience, competitiveness, and customer-centric decision-making, offering a strong foundation for future intelligent supply chain research. http://www.ijsom.com/article_11109.html Objective: Indonesia is the world’s second-largest avocado producer; however, it is not among the leading avocado-exporting countries because its supply chain has limited capacity to meet increasing consumer demands for product authenticity, organic certification, and traceability. This study aims to develop a blockchain-based traceability model that improves transparency, record reliability, and quality assurance throughout Indonesia’s avocado supply chain. Methods: The proposed model maps the participation and data-sharing roles of key supply-chain stakeholders, including farmers, a certification body, intermediaries, and retailers. Blockchain technology is used to record and connect product information across supply-chain stages, creating an immutable and transparent history of the avocado journey from production to retail. Results: The developed model provides an integrated traceability framework in which authorized stakeholders can record and verify information related to product origin, certification, handling, distribution, freshness, and compliance with relevant standards. The model produces a reliable product history that can be accessed to support verification and respond to consumer traceability requirements. Conclusion: The proposed blockchain-based model can strengthen assurance of avocado authenticity, freshness, organic status, and standards compliance by improving information transparency and preventing unauthorized alteration of supply-chain records. Its implementation may increase stakeholder and consumer trust, support the competitiveness of Indonesian avocados in export markets, and contribute to long-term value creation in Indonesia’s horticultural sector. http://www.ijsom.com/article_11110.html Objective: The objective of this study is to examine the optimal design of blockchain-enabled supply chain networks using reinforcement learning (RL). The goal is to develop an integrated network framework considering simultaneously supply chain connectivity, blockchain node activation and cross-layer integration decisions, to minimize the total network cost under operational constraints. Methods: The problem is formulated as a combinatorial optimization problem and modeled as a two-layer network of supply chain entities and blockchain nodes. A Q-learning framework is used to explore feasible network configurations in the presence of stochastic costs. The model includes various cost factors, connectivity requirements, constraints for blockchain activation, and penalty mechanisms to ensure feasible solutions. Results: The results of experiments show that the proposed RL-based method is able to find out the cost-effective network designs in accordance with network connectivity constraints. The convergence behavior is shown to be stable throughout different runs, and the method converges within an earlier stage of training. The sensitivity analysis indicates that the increment of minimum number of active blockchain nodes will increase the cost of the designed network, highlighting the trade-off between blockchain deployment requirements and economic efficiency. The resulting network structure turns out to be feasible, connected, and interpretable. Conclusion: Based on the findings, the research proves the feasibility of applying reinforcement learning to blockchain-enabled supply chain network design problems. The proposed framework provides an approach that can be utilized evaluating blockchain integration strategies and balancing implementation costs with operational requirements. The research further creates a base for further research involving larger-scale networks, dynamic environments, and multi-objective optimization settings.