Prediction Of Product Stock Procurement Using Single Moving Average And Single Exponential Smoothing Algorithms
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Abstract
Information systems and technology have a crucial role in people's lives because they help make effective decisions. Reliability in predicting Vape sales of liquid products is a priority to improve Vape shop services and profits. However, Vape Industrial does not yet have a method for forecasting the future sales of liquid goods, causing some liquids to experience an excess or shortage. In this study, an application was developed to predict the amount of stock needed in the next period. Consumer transaction data in the previous period were analyzed using the Single Moving Average and Single Exponential Smoothing algorithms. The two algorithms are compared based on the results of error calculations in choosing the right algorithm for product stock prediction. The test results show that this application is successful in predicting Besti Matcha's liquid stock well. For the daily period, the Single Exponential Smoothing algorithm with the alpha 0.2 method gives the right prediction results, with predictions of selling 1 product and an error value of 0%. As for the weekly period, the Single Moving Average algorithm with 5 periods gives predictive results of 5 products sold and an error value of 25%. For the monthly period, Single Exponential Smoothing with an alpha of 0.2 gives prediction results of 21 products sold and an error value of 10.5%. With this application, it is hoped that Vape Industrial can be effective in predicting stock requirements for the next period and avoiding excess product losses.
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