Özet:

One of the most important topics in the manufacturing industry is the subject of material stock management and optimization. In many production enterprises, excess and old materials can be seen in their warehouses due to the use of the existing material planning algorithm that is not suitable for dynamic environments. This results negatively affect the business outcomes of companies. As the business capital is connected to stocks, it cannot be used when necessary, operating costs increase and production time extends. The static EOQ (Economic Order Volume) models operating in the MRP (Material Requirement Planning) software can be a suitable alternative in stock management in cases where demand is stable and predictable. However, in the face of demand variation, it is not always possible to produce very accurate results and stock levels are not reduced to the desired levels. In this situation, it is necessary for to constantly monitor the change of demand from MRP systems in order to determine the correct order amount at all times and to constantly update the order amounts of materials and the re-order levels at any time necessary. Having in mind that there are thousands of materials in the stock of the business, this process seems unlikely to be done with human labour. In this study, a smart and dynamic model algorithm was proposed to make the material needs planning process more sensitive to demand variations. The analytical formulas of the proposed model have been extracted, validity tested by making simulations in a computer environment and the model has been observed to produce correct results. The study concluded that stock levels could be lowered through dynamic and intelligent algorithms that will work behind MRP systems.

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