AI in Retail: Transforming Customer Experience and Supply Chain Management
Abstract
This paper investigates the transformative role of artificial intelligence (AI) in the retail industry, focusing on how AI technologies are revolutionizing customer experience and supply chain management. Through an analysis of case studies and industry reports, the study explores how AI-driven solutions such as personalized recommendations, demand forecasting, and inventory optimization are reshaping various aspects of retail operations, including marketing, sales, and logistics. It discusses the potential benefits of AI in improving customer engagement, increasing sales, and reducing operational costs, while also addressing challenges related to data privacy, ethical considerations, and workforce adaptation. Additionally, the paper examines the role of AI in enabling more personalized and seamless shopping experiences, by analyzing customer behavior data and providing real-time assistance and recommendations. Furthermore, it discusses the importance of data integration, collaboration between retailers and technology providers, and investment in AI talent and infrastructure in harnessing the full potential of AI in retail. The findings underscore the transformative power of AI in creating more dynamic, efficient, and customer-centric retail ecosystems for the future of commerce.
Share and Cite
Article Metrics
References
- Berman, B. (2018). The role of artificial intelligence in creating a personalized shopping experience. Journal of Retailing and Consumer Services, 41, 154-163.
- Chen, Y., Cheng, P., Cheng, J., & Lu, Y. (2020). Demand forecasting for perishable goods with deep learning approaches: A review. Omega, 102, 102315.
- Hachey, B., Grover, P., & Vidyarthi, N. (2019). Artificial intelligence in supply chain management: Trends, applications, and challenges. Computers & Industrial Engineering, 139, 105558.
- Hong, I. B., & Cho, H. (2017). The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management, 37(3), 627-637.
- Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3-8.
- Lu, B., Gürsoy, D., & Lu, C. (2016). Shopping motivation as a moderator in the retail service evaluation. Journal of Retailing and Consumer Services, 31, 80-88.
- Wang, X., Zheng, X., Chen, Y., Wang, H., Cui, X., & Liu, J. (2019). Inventory optimization for perishable goods with expiration dates under conditions of dynamic demand: A review. Omega, 88, 107-122.
- World Economic Forum. (2017). Shaping the Future of Retail for Consumer Industries.