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020			_aTHE0010015 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .T36 2023 r Bc.
100	1		_aTan, Wei Qing, _eauthor.
245	1	0	_aDevelopment of demand forecasting model for inventory management using deep learning approach via transfer learning / _cTan Wei Qing
264		1	_aKuantan, Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_avi, 84 pages : _billustrations ; _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCentre for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aInventory 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.
610	2	0	_aCentre for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Project _xDissertations
942			_2lcc _cPSM