Comparative analysis of ARIMA and double exponential smoothing for forecasting rice sales in fair price shop

Abstract

Abstract One of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.