Spare parts inventory management is very important to ensure smooth operation of maintenance department. The main objectives of inventory management of spare parts are to ensure the availability of spares and materials for the maintenance tasks and increase the productivity of the maintenance department. This research centred on the development of the Computerised Inventory Management System (CIMS) for the maintenance team at Weida Integrated Industries Sdn. Bhd. The inventory management technique used to control the spare parts inventory in this research was the basic Economic Order Quantity models (EOQ). However, the CIMS developed is unique as it has the ability in handling inventories in multiple-storage locations. The CIMS was written using the Visual Basic 2010 software. This CIMS has the abilities to keep records and process the spare parts information effectively and faster besides helping the user to perform spare parts ordering tasks compared to the current manual recording. In addition, the ordering quantity and frequency for the CIMS is determined through the EOQ technique. However, observation indicates that the overall average inventory level currently at the factory is lower than the expected overall average inventory level produced by the CIMS. This is due to the fact that the CIMS was unable to consider the opening stock in ordering the inventories. Therefore, further improvements are needed to optimize the performance of the system such as using the EOQ with the reorder point technique, the periodic or continuous review system.
The quantity of palm oil fruits supplied from palm oil estates often affects the number of workers required and the area to be harvested. Thus, the ultimate objective of this research is to develop a system to forecast monthly delivery quantities such that the companys profit will increase through proper balance between supply and demand. This research is limited to 10 years of monthly deliveries from a palm oil estates deliver to only one palm oil mill as the case study. Two forecast techniques were chosen; the linear regression and additive forecast methods. Based on theories and formulations of the selected forecast techniques, forecast software was developed. For this software, user only needs to specify the year to be forecasted and choose one forecast technique to be used. Then, the forecasted values and errors were calculated and the results were displayed on the GUI. The performance of each technique was compared based on the mean absolute percentage error (MAPE). The generated results showed that the additive method produced lower MAPE compared to the linear regression method. This proved that the additive method is a better technique to predict the monthly delivery quantities of the palm fruits by the estate.
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