FORECASTING DEMAND LEVEL USING TIME SERIES AND MACHINE LEARNING UNDER UNCERTAINTY CONDITION
DOI:
https://doi.org/10.23055/ijietap.2023.30.5.9009Keywords:
Demand Forecast, Time Series, Chicken Meat, Artificial Neural Networks, Fuzzy SetsAbstract
Demand forecasting and studying customers’ (consumers) behaviors is of high importance. Without accurate planning for the future, organizations might risk their future. In other words, failure to have reliable planning for the future increases organizational expenses and in turn increases production costs, which is translated into a higher end-price of goods (services). Forecasting demand in the supply chain follows the same rule. In this paper, the level of customer demand in the chicken meat industry has been studied. Considering that the relationship between the decision variables is more linear or non-linear, artificial neural networks are used to analyze the data to overcome this challenge. The approach used in this study includes the combination of time series and artificial neural networks. After analyzing the data for a period of 120 days for 24 selected chicken meat stores located in the city of Arak (Iran), the future demand for each store has been determined. Forecasted demands are reported as triangular fuzzy numbers. Also, instead of point forecasting, interval forecasting has been provided and all data analysis has been done in MATLAB software.
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