Forecasting Model of Corn Commodity Productivity in Indonesia: Production and Operations Management, Quantitative Method (POM-QM) Software

Abstract

Food is an essential ingredient needed by humans. In addition to being consumed, it can also be a valuable commodity for economic purposes through productivity of food crops. Therefore, this study aims to model the forecasting of maize productivity in Indonesia using Production and Operations Management-Quantitative Method (POM-QM) software. The data collected on productivity of corn commodities in Indonesia between 1980-2019 shows fluctuations, with both deficit and surplus periods. This study uses a time series data-based forecasting model consisting of three methods, namely Double Moving Average (DMA), Weighted Moving Average (WMA), and Single Exponential Smoothing (SES). The selection of the best model was conducted based on the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). SES emerged as the most preferred, with a lower MAPE value of 4.913%. The predicted productivity of corn in Indonesia is estimated at 5.28 tons/ha/year, sufficient to meet consumers' demand. Therefore, governments are recommended to use this information in predicting corn productivity to meet the national demand in the future.