Comparison of Sales Forecasting Models for Construction Retail Business Case Study: Hayeak Group (2559) Co.,Ltd.
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Abstract
The case study company has an inventory management of placing an order when out of stock. There is no record of what is ordered, and sometimes the product received is not exactly as what is ordered. Therefore, the received product becomes unsold inventory. This research aims to study the sales forecast of large volume products: Portland cement, and the sales forecast of low volume products: toilet bowls. The data of both products were collected for 25 months from July 2020 to July 2022. Four forecasting methods: 1) the Moving Average Forecast Method, 2) Brown’s One Parameter Linear Exponential Smoothing, 3) Holt’s Two Parameter Linear Exponential Smoothing, and 4) Winter's exponential smoothing method, are used. The criteria are Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) with the lowest value. The results revealed that Brown’s One Parameter Linear Exponential Smoothing was the most suitable model for forecasting Portland cement sales 3 months and 6 months in advance. The MAPE and MAD were 7.78% and 1,818, respectively. The most suitable model for forecasting toilet bowls sales 3 months and 6 months in advance was Winters’s Linear and Seasonal Exponential Smoothing. The MAPE and MAD were 42.83% and 2.833, respectively. The company could use these forecasting model techniques to plan its inventory in advance according to the actual sales at that time.
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