03019ntm a2200337 i 4500 0 0 lcc 0 0 10000 10000 2025-04-29 0 CD13570 T000003168 2025-04-29 00:00:00 1 2025-04-29 PSM TIADA HARDCOPY 102480 102486 MY-KuUP 20251125111037.0 t||||fs|||| 000 0 ta 250429t20232023my a|||fr|||| 000 0 eng d THE0010015 (Local) Hardback UMPSA eng UMPSA rda PSM .T36 2023 r Bc. Tan, Wei Qing, author. Development of demand forecasting model for inventory management using deep learning approach via transfer learning / Tan Wei Qing Kuantan, Pahang : UMPSA, 2023 © 2023 vi, 84 pages : illustrations ; 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 Includes bibliographical references Inventory Management is crucial for small and medium-sized enterprises since it requires significant financial and human resources. However, businesses now must contend with seasonal client demand in addition to a competitive and unstable economic environment. Stock predicting management inventory would be required in this situation in order to reduce losses and boost profitability. Predicting or projecting a future occurrence or trend is known as demand forecasting. Demand forecasting of inventory management is able to assist businesses from preventing overstock or stock-outs. Due to the capital commitment made by excess inventory, high inventory levels might result in revenue losses. Losses in sales and a loss in consumer satisfaction and brand loyalty may result from shortages or out-of-stock situations. To estimate future seasonal product demand, inventory management has thus integrated the traditional time series approach, machine learning, and deep learning techniques. This research discusses how to construct a demand forecasting model for inventory management via transfer learning and determine whether classical time series analysis or machine learning methods are more suitable in forecasting demand of inventory management or deep learning method is better. The models are evaluated by using confusion matrix, root mean square error (error terms) and accuracy of each model. In this research, a demand forecasting model for inventory management using transfer learning is constructed and achieved accuracy of 90.97% and error term (MAE) is 0.318. However, after the comparison between deep learning model and machine learning model as well as time series methods, LSTM model seems achieved a better result which accuracy is 91.62 and error term (MAE) is 0.316. Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project Dissertations lcc PSM