Kharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Lean Six Sigma Tools for Optimizing Manufacturing Small and Medium Enterprises: A Systematic Literature Review1742031182110.22034/ijsom.2026.111035.3572ENThomasMunyaiDepartment of Operations Management, Faculty of Management Sciences, Tshwane University of Technology, Pretoria, South AfricaRefentse LydiaSelepeDepartment of Operations Management, Faculty of Management Sciences, Tshwane University of Technology, Pretoria, South Africa0000-0002-3174-7444OlasumboMakindeDepartment of Operations Management, Faculty of Management Sciences, Tshwane University of Technology, Pretoria, South AfricaJournal Article20260429Objective: Manufacturing small and medium-sized enterprises (SMEs) face growing competitive pressures due to operational inefficiencies, high defect rates, equipment unreliability and resource constraints. Lean Six Sigma (LSS), which combines Lean waste-reduction with Six Sigma’s data-driven quality-improvement approach, has emerged as an effective methodology for enhancing operational performance. This study synthesizes recent empirical evidence on the application and outcomes of LSS in manufacturing SMEs.Methods: A Systematic Literature Review (SLR) was conducted using peer-reviewed journal articles and conference papers indexed in the Scopus database between 2021 and 2025. Studies were identified through a structured Boolean search strategy and screened using predefined inclusion and exclusion criteria. Bibliometric analysis, keyword co-occurrence mapping, and comparative evaluation were used to identify research trends, key LSS tools, and associated performance outcomes.Results: Findings reveal increasing scholarly interest in LSS implementation within manufacturing SMEs. Strong relationships were identified between LSS tools and performance indicators such as productivity, efficiency, lead-time reduction and quality improvement. Commonly applied tools include DMAIC, Value Stream Mapping, 5S, Kaizen, TPM, SMED and Statistical Process Control. Reported outcomes include defect reductions of up to 70%, productivity gains exceeding 20%, reduced cycle times, and improvements in Overall Equipment Effectiveness of 15-20%.Conclusion: LSS significantly improves operational and quality performance in manufacturing SMEs. Emerging evidence also highlights the integration of LSS with Industry 4.0 technologies, supporting data-driven continuous improvement and sustainable competitivenesshttp://www.ijsom.com/article_11821_b99f4242922cd10313630b0ecccda1dc.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Key Actor Selection for Sustainable Supply Chain Based on Integration of SCOR DS-TOPSIS in Tapioca and Mocaf Agro-Industry2042281108810.22034/ijsom.2026.110954.3518ENSesar HusenSantosaDepartment of Agro-Industrial Engineering, Faculty of Agricultural Technology, IPB University, Bogor, IndonesiaMariminMariminDepartment of Agro-Industrial Engineering, Faculty of Agricultural Technology, IPB University, Bogor, IndonesiaTiti CandraSunartiDepartment of Agro-Industrial Engineering, Faculty of Agricultural Technology, IPB University, Bogor, IndonesiaJono MintartoMunandarDepartment of Management, Faculty of Economics and Management, IPB University, Bogor, IndonesiaJournal Article20251123Objective: 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_11088_f2b6806d6ed60d2d87b0dd5ae62e6f20.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Leveraging Social Network Search Patterns for Customer-Centric Supply Chain Optimization: A Real-Time Case Study2292431110810.22034/ijsom.2026.110842.3427ENJoe PrathapPathrose MaryDepartment of Computer Science and Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, India0000-0002-4708-8308MiniPrinceDepartment of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R D Institute of Science and Technology, Avadi, IndiaVinil DaniWencheslasDepartment of Electrical and Electronics Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, IndiaJaithunbiAbdul KareemDepartment of Artificial intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IndiaSherin BeeviLucasDepartment of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, Chennai, IndiaVijaylakshmiNagarajanDepartment of Computer Science and Engineering, SNS College of Technology, Coimbatore, IndiaJournal Article20250704Objective: 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_11108_2bd9cae0be467d9523a8489230163366.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Optimization of Key Parameters in a Welding Process Using Multivariate Statistical Analysis and Design of Experiments2442721108710.22034/ijsom.2026.110844.3429ENNargesDelavar PasikhaniDepartment of Industrial Engineering, Sharif university of technology, Tehran, IranAbbasForoozanfarDepartment of Industrial Engineering, Sharif university of technology, Tehran, IranSeyed Taghi AkhavanNiakiDepartment of Industrial Engineering, Sharif university of technology, Tehran, IranJournal Article20250708Objective: 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_11087_a82aac2169c9a76e374b2f4c96bfa0a0.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Improving Avocado Distribution Ecosystem using Blockchain Technology2732921110910.22034/ijsom.2026.110873.3457ENSriWidiyanestiCenter of Excellence for Strategic Advancement for Key Digital Transformation Indonesia, Research Institure for Intelligent Business & Sustainable Economy, Telkom University, Main Campus, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, Indonesia0000-0003-0388-6698WaritsaFirdausiSchool of Economics and Business, Telkom University, Bandung, IndonesiaAndryAlamsyahCenter of Excellence for Strategic Advancement for Key Digital Transformation Indonesia, Research Institure for Intelligent Business & Sustainable Economy, Telkom University, Main Campus, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, Indonesia0000-0001-5106-7561Muhammad NaufalHakimCenter of Excellence for Strategic Advancement for Key Digital Transformation Indonesia, Research Institure for Intelligent Business & Sustainable Economy, Telkom University, Main Campus, Jl. Telekomunikasi No. 1, Bandung 40257, West Java, Indonesia0009-0006-3681-3427Journal Article20250827Objective: 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_11109_6a52db09e45a58b3e50bcc6213785282.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501A Scenario-Based Stochastic MILP Model for Sustainable Reconfiguration of Smart Grids in the Electricity Supply Chain under Uncertainty2933151185210.22034/ijsom.2026.111015.3556ENSaraMohammadiDepartment of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, CanadaMariamAmeliDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, TehranHamedDavari-ArdakaniDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, TehranJournal Article20260214Objective: This study develops a sustainable reconfiguration framework for smart grids in electricity supply chain networks under uncertainty. The research aims to integrate strategic redesign and operational decisions while simultaneously addressing economic and environmental objectives.Methods: A two-stage scenario-based stochastic mixed-integer linear programming (MILP) model is developed to minimize total network cost and greenhouse gas emissions under uncertain electricity demand and selling prices. The bi-objective model is solved using the augmented epsilon-constraint (AUGMECON) method to generate Pareto-efficient solutions. Stochastic performance measures, including the Value of the Stochastic Solution (VSS) and Expected Value of Perfect Information (EVPI), are also employed to evaluate the value of considering uncertainty. Results: The results show that explicitly incorporating uncertainty changes the trade-off between economic and environmental objectives and leads to redesign strategies that outperform deterministic solutions under different operating conditions. Electricity demand has a greater influence on network cost than electricity selling price, while the transmission and distribution loss coefficient is identified as the most influential parameter affecting economic performance. The VSS confirms the economic benefit of explicitly considering uncertainty, while the EVPI demonstrates the value of improved information for redesign decisions. Conclusion: The study integrates strategic reconfiguration decisions, including the installation and closure of generation and distributed generation facilities, with operational decisions on electricity generation, pricing, load allocation, and capacity planning within a unified stochastic framework. The proposed approach provides decision support for coordinated and sustainable smart grid reconfiguration under uncertain operating conditions.http://www.ijsom.com/article_11852_ba67e777107290283dcc9adaed9459e1.pdfKharazmi UniversityInternational Journal of Supply and Operations Management2383-135913220260501Reinforcement Learning for Blockchain-Enabled Supply Chain Network Design3163321111010.22034/ijsom.2026.111012.3553ENSaraHasheminezhadFaculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, IranMahsaMoayedFaculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, IranArdavanBabaeiFaculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, IranErfanBabaee TirkolaeeDepartment of Industrial Engineering, Istinye University, Istanbul, Turkey
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Department of Mechanics and Mathematics, Western Caspian University, Baku, Azerbaijan0000-0003-1664-9210Journal Article20260212Objective: 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.http://www.ijsom.com/article_11110_805d94d14046509f82c7bdb1819c64af.pdf