| dc.contributor.author | Katwigye, Caleb Jeromy | |
| dc.contributor.author | Agaba, Blaise | |
| dc.contributor.author | Kakande, Abdulatiff Musa | |
| dc.contributor.author | Gazeghen, Samuel | |
| dc.date.accessioned | 2024-12-09T08:58:38Z | |
| dc.date.available | 2024-12-09T08:58:38Z | |
| dc.date.issued | 2024-06 | |
| dc.identifier.citation | Agabam B. ... et al. (2024). Stockwise: a predictive inventory management system for retail and wholesale traders; Unpublished dissertation, Makerere University, Kampala | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/19975 | |
| dc.description | A project report submitted to the School of Computing and Informatics Technology in partial fulfillment of the requirements for the award of the Degree of Bachelor of Science in Computer Science of Makerere University | en_US |
| dc.description.abstract | Predicting product sales accurately is a crucial part of trade in today’s market. StockWise is an innovative inventory management app created to help retailers and wholesalers accurately forecast product demand and manage inventory levels. Using historical sales data and machine learning, StockWise predicts future product demand with high precision. It also excels at managing data efficiently, allowing users to store sales information securely. This forms the basis for precise demand forecasting, enabling users to make smart decisions about their stock. As development continues, StockWise aims to improve inventory management by optimizing stock levels, reducing stock-outs, minimizing excess inventory, and boosting overall efficiency thus this app represents a major step forward in inventory management in the trade industry. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Stockwise | en_US |
| dc.title | Stockwise: a predictive inventory management system for retail and wholesale traders | en_US |
| dc.type | Thesis | en_US |
