Published Paper Details:

Edge-enabled store analytics has emerged as a transformative approach for real-time performance monitoring in modern retail environments. This review explores the convergence of edge computing and artificial intelligence (AI) in enabling low-latency Key Performance Indicator (KPI) dashboards across large retail chains. By processing data locally at the edge, these systems drastically reduce latency, minimize cloud dependency, and enhance privacy, making them well-suited for high-frequency retail operations. The paper provides a structured overview of recent AI methods deployed at the edge, examines experimental results from real-world deployments, and highlights key performance benefits such as latency reduction, increased dashboard accuracy, and cost efficiency. It concludes with future directions emphasizing personalized AI at the edge, federated learning innovations, standardization efforts, and ethical considerations. This review aims to offer valuable insights for researchers and practitioners seeking to develop scalable, intelligent, and efficient store analytics frameworks in the digital retail age.