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

Abstract

Food is essential for humans. In addition to being consumed, it can also be a valuable commodity for economic purposes through the production of food crops. Therefore, this study aims to model the forecasting of maize production in Indonesia using Production and Operations Management-Quantitative Method (POM-QM) software. The uniqueness of this research lies in its innovative approach to predicting corn production, which contributes a valuable addition to the existing body of knowledge in the field of production and operations management. This model not only enhances forecasting accuracy but also offers a novel perspective on optimizing corn production processes in the Indonesian context. The data collected on the production of corn commodities in Indonesia between 1980 and 2019 shows fluctuations, with both deficit and surplus periods. Secondary data in the form of time series sourced from the data and information center (Pusdatin) under the Ministry of Agriculture and the Central Bureau of Statistics (BPS) were used in this study. Our research employs advanced quantitative methods to analyze this historical data, aiming to develop a robust forecasting model that enhances the accuracy of predicting corn commodity production in Indonesia. This study uses a time series data-based forecasting model consisting of three methods: Double Moving Average (DMA) of 29.5 million tons, Weighted Moving Average (WMA) of 28.9 million tons, and Single Exponential Smoothing (SES) of 27.7 million tons. 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). DMA emerged as the most preferred, with a lower MAPE value of 10.703%. The predicted production of corn in Indonesia is estimated at 29,5 million tons, sufficient to meet consumers' demand. The fusion of advanced quantitative methods and real-world data positions our forecasting model as a valuable asset in the pursuit of a more sustainable and resilient corn production sector in Indonesia.