Abstract

Forecasting sporadic demand can be considered as one of the biggest challenges in supply chain management. It is very difficult to forecast the sporadic demand because of the irregularities present in this type of dataset. This ultimately reduces the overall supply chain performance. Traditional methods used to forecast sporadic demand include Simple Exponential Smoothing (SES), Croston’s method, Multiple Linear Regression (MLR), and other parametric and non-parametric methods. However, none of these methods considers the factors behind the irregularities present in sporadic datasets. Principal component analysis (PCA), an artificial intelligence algorithm, can analyze a dataset of two or more variables and observations are explained by distinct inter-correspond variables. In this paper, a framework for forecasting sporadic demand considering multiple factors of the irregularities is presented using Principal component analysis (PCA). Furthermore, a numerical illustration is provided using automotive spare parts data to demonstrate the effectiveness of the proposed model. The main ambition of the proposed PCA model is to extract relevant information from the provided dataset and provide predictive models. The proposed forecasting model with PCA is able to reduce forecasting error and forecast the sporadic demand with a higher degree of accuracy compared to other traditional methods.