Data-driven Business Intelligence Platform for Smart Retail Stores

Abstract

The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client delivery. This decision support system used Machine Learning (ML) technologies such as LSTM (Long short-term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule Mining Algorithms as key technologies. Data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system.