Comparative Analysis of the Double Moving Average and Double Exponential Smoothing Methods in Forecasting Inventory at Mami’s Care Store

Abstract

Mami's Care is a shop that sells necessities such as baby food, water bottles and clothes. One of the obstacles faced by Toko Peduli Mami is that decision support system technology has not been used in inventory forecasting and inventory data collection is still done manually, resulting in low accuracy of the data obtained. By considering these preferences, decision support system methods can produce predictions that are more accurate and relevant to the company's strategic goals. To forecast inventory, researchers provide a solution in the form of a system that helps managers determine product estimates that need to be prepared so that overstocking or understocking does not occur. Inventory reporting plays a very important role in any business as accurate information enables informed decision making. Therefore, a calculation method is needed to predict future inventory levels. This predictive system design uses a UML approach consisting of use case diagrams, activity diagrams, class diagrams and sequence diagrams. The UML approach is suitable if the system you are developing uses object-oriented techniques. This research completes a system that can predict future sales using two methods: double moving average and double exponential smoothing. The results of the two calculations are compared using the lowest MAPE percentage so that Mommy's Care can estimate the number of clothing items available for the following month.