Outlier-Aware Demand Prediction Using Recurrent Neural Network-Based Models and Statistical Approach

Abstract

The paint industry comprises an elaborate supply chain involving various activities such as raw material procurement, manufacturing, and distribution. In addition, the accuracy of demand prediction significantly impacts supply chain management. A recurrent neural network (RNN) is a powerful method that learns intricate patterns through vast amounts of historical data and provides a prediction, demonstrating excellent performance in demand prediction. However, standard RNN-based demand predictions are limited by many outliers that occur due to the characteristics of the paint industry. Unexpected events such as a factory fire cause rapid fluctuations in paint demand and are difficult to pre-observe. To overcome these limitations, we propose a novel approach that employs clustering to identify demand time series data with similar characteristics and applies statistical outlier adjustment to allow the prediction model to learn the complex patterns of actual demand. The prediction target is the sum of sales for 15 days in the future, and sales data for four paint products are used to evaluate the proposed approach. Experimental results demonstrate a satisfactory prediction accuracy improvement ranging from 100.9% to 152.4%. In addition, unlike the RNN-based models used for comparison, the proposed method is more robust against actual demand fluctuations.