Forecasting as demand in the culinary industry is very necessary for decision making in the supply of culinary ingredients and reducing stock outs that often occur. This study uses a single moving average (SMA) and single exponential smoothing (SES) time series to make a forecast or demand forecast with the culinary industry and raw materials. Forecasting is an attempt to predict what will happen in the future based on previous data or past data based on scientific and qualitative methods that are carried out systematically. (MAPE and Exponential Moving Average (EMA) show that a single moving average has more accurate results when compared to the Single exponential smoothing method. This research method uses systematic interpretation to study and identify the effectiveness of simple time series forecasting, especially the culinary industry. By comparing the accuracy of each SMA and SES methods, SMA has the smallest error rate in forecasting. Therefore, the time series method in predicting time series is simple in the culinary industry. room in the culinary industry, the company uses a good forecasting system for sales, purchases, supply and demand with a simple time series forecasting method, where the industry has succeeded in reducing preparation costs and production inventory costs. with time series forecasting, p this conducted for give integration to be more optimal in development industry culinary that.

Jumiatin Maesaroh

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The Effectiveness of Simple Time Series Implementation in The Culinary Industry: A Systematic Literature Review