Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 2 2026 05 01 Leveraging Social Network Search Patterns for Customer-Centric Supply Chain Optimization: A Real-Time Case Study 229 243 11108 10.22034/ijsom.2026.110842.3427 EN Joe Prathap Pathrose Mary Department of Computer Science and Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, India 0000-0002-4708-8308 Mini Prince Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R D Institute of Science and Technology, Avadi, India Vinil Dani Wencheslas Department of Electrical and Electronics Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, India Jaithunbi Abdul Kareem Department of Artificial intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India Sherin Beevi Lucas Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, Chennai, India Vijaylakshmi Nagarajan Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India Journal Article 2025 07 04 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_11108_2bd9cae0be467d9523a8489230163366.pdf