Order Pattern Prediction Using Artificial Intelligence In An Inventory System Design
Achieving smmooth production is one of the major concern by the manufacturing industry. In order to have smooth production, waste must be avoided. Furthermore, the cost of investment in production can be high with contribution of Wasted activities especially high inventory management cost. Economic...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/20764/1/Order%20Pattern%20Prediction%20Using%20Artificial%20Intelligence%20In%20An%20Inventory%20System%20Design%20-%20Nor%20Amirah%20Mohd%20Arshad%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20764/2/Order%20Pattern%20Prediction%20Using%20Artificial%20Intelligence%20In%20An%20Inventory%20System%20Design%20-%20Nor%20Amirah%20Mohd%20Arshad.pdf |
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| Summary: | Achieving smmooth production is one of the major concern by the manufacturing industry. In order to have smooth production, waste must be avoided. Furthermore, the cost of
investment in production can be high with contribution of Wasted activities especially high inventory management cost. Economic Order Quantity (EOQ) has been applied in inventory management in order to determine economic lot size. However, EOQ has limitation due to uncertain situation. Thus, the aim of this study to reduce cost investment in inventory. This study has three objectives, (1) to investigate ordering pattern ordering pattern which is affected the inventory, (2) to propose order pattern in inventory using ANFIS and (3) to evaluate proposed order pattern with cost investment. The study was conducted based on case study at the furniture company. The historical data of demand and supply was provided for 52 weeks. Firstly, the inventory level was investigated with the historical data based on stochastic EOQ model. From the investigation, shortage occurred because order does not make for a long time. Hence, the total cost of inventory was high. Then, investigated order pattern using Fuzzy Inference System and shortage still occurred. Thus, manual prediction order pattern was developed which to ensure the inventory just below reorder point. This purposed to ensure that every week order was took placed and shortage was avoided. Adaptive Neuro Fuzzy Inference System was used in order to find the parameters in forecasting the order quantity. The result showed that the proposed order pattern can avoid
shortage and every week the inventory is below reorder point. Every week order is take place. Also, the total cost is reduced since no shortage occurs. |
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