Moving Average Smoothing for Gregory-Newton Interpolation: A Novel Approach for Short-Term Demand Forecasting

Abstract

Demand forecasting is a fundamental task when it comes to industrial stability. A high deficiency in demand forecasting results in high variability, and hence the presence of undesirable bullwhip effect in the supply chain. This causes unnecessary inventory or low service levels and consequently less profit and lower efficiency of the entire supply chain. In this paper, a new forecasting method is developed for relatively short-term demand forecasting. The new approach combines the simple moving average method with Gregory-Newton interpolation curve fitting. The developed method has two phases; a) Data Preparation (Smoothing) phase, where the data get cleaned of possible existing noise and, b) Forecasting phase where the forecast for a new period is obtained. The simple moving average is utilized twice for the 1st phase, then Gregory-Newton forward interpolation is used in the 2nd phase. The proposed method considers datasets with trend and seasonality separately. Numerical examples are conducted for testing the approach efficiency. Comparisons with other acknowledged methods of the literature are present. Results showed high potential of utilizing the new approach for real-life situations.