International Journal of Supply and Operations Management

International Journal of Supply and Operations Management

Leveraging Social Network Search Patterns for Customer-Centric Supply Chain Optimization: A Real-Time Case Study

Articles in Press

Document Type : Case Study

Authors
Joe Prathap Pathrose Mary 1
Mini Prince 2
Vinil Dani Wencheslas 3
Jaithunbi Abdul Kareem 4
Sherin Beevi Lucas 5
Vijaylakshmi Nagarajan 6
1 Department of Computer Science and Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, India
2 Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R D Institute of Science and Technology, Avadi, India
3 Department of Electrical and Electronics Engineering, School of Engineering and Technology, Sapthagiri NPS University, Bengaluru, Karnataka, India
4 Department of Artificial intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India
5 Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, Chennai, India
6 Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India
10.22034/ijsom.2026.110842.3427
Abstract
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.
Keywords
customer-centric supply chain
social network analytics
optimized search suggestions
demand forecasting
inventory management
Subjects
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International Journal of Supply and Operations Management

Articles in Press, Accepted Manuscript
Available Online from 03 July 2026