Demand Forecasting in the Food Equipment Industry Using Predictive Data Analytics Techniques

Although food and restaurants are two of the most trending entertainment sectors in Saudi Arabia, where the equipment industry is a massive contributor to the enrichment of this trending environment, a major appliance manufacturer is facing difficulties forecasting the demand in the market. This study aims to study the sales department of one of the biggest appliance traders in the MENA region and forecast their sales for 2021. This study predicted sales using time series methods such as linear progression, exponential smoothing, Seasonal, and Trend decomposition using Loess (STL) and Auto-Regressive Integrated Moving Average (ARIMA). The program used in forecasting and using all these methods was Power BI, including R script commands. The results of the ARIMA method showed higher accuracy and less error than the other methods. The ARIMA method demonstrated an accuracy of 92.16% and a 9.24% error. For more accurate demand forecasting, businesses should consider the market changes for at least two years to understand consumer behavior that might change faster than expected, directly affecting the restaurant industry and the demand for equipment.