Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2108
Title: Data-driven Business Intelligence Platform for Smart Retail Stores
Authors: Eheliyagoda, D. R. M. R. R. D. R. S
Liyanage, T. K. G
Jayasooriya, D. C
Nilmini, D. P. Y. C. A
Nawinna, D. P
Attanayaka, B
Keywords: Data-driven
Business Intelligence
Intelligence Platform
Smart Retail Stores
Issue Date: 9-Dec-2021
Publisher: IEEE
Citation: D. R. M. R. R. D. R. S. Eheliyagoda, T. K. G. Liyanage, D. C. Jayasooriya, D. P. Y. C. A. Nilmini, D. Nawinna and B. Attanayaka, "Data-driven Business Intelligence Platform for Smart Retail Stores," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 97-102, doi: 10.1109/ICAC54203.2021.9671146.
Series/Report no.: 2021 3rd International Conference on Advancements in Computing (ICAC);Pages 97-102
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.
URI: http://rda.sliit.lk/handle/123456789/2108
ISSN: 978-1-6654-0862-2
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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