Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2108
Full metadata record
DC FieldValueLanguage
dc.contributor.authorEheliyagoda, D. R. M. R. R. D. R. S-
dc.contributor.authorLiyanage, T. K. G-
dc.contributor.authorJayasooriya, D. C-
dc.contributor.authorNilmini, D. P. Y. C. A-
dc.contributor.authorNawinna, D. P-
dc.contributor.authorAttanayaka, B-
dc.date.accessioned2022-04-29T07:05:33Z-
dc.date.available2022-04-29T07:05:33Z-
dc.date.issued2021-12-09-
dc.identifier.citationD. 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.en_US
dc.identifier.issn978-1-6654-0862-2-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2108-
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 3rd International Conference on Advancements in Computing (ICAC);Pages 97-102-
dc.subjectData-drivenen_US
dc.subjectBusiness Intelligenceen_US
dc.subjectIntelligence Platformen_US
dc.subjectSmart Retail Storesen_US
dc.titleData-driven Business Intelligence Platform for Smart Retail Storesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC54203.2021.9671146en_US
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

Files in This Item:
File Description SizeFormat 
Data-driven_Business_Intelligence_Platform_for_Smart_Retail_Stores.pdf
  Until 2050-12-31
1.5 MBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.