Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion

TL;DR

This paper proposes a hybrid demand forecasting model that decomposes demand into baseline and promotional components, demonstrating improved accuracy across various demand volatilities influenced by promotions.

Contribution

The study introduces a hybrid demand forecasting approach that effectively handles demand volatility caused by promotions, outperforming traditional models in accuracy and robustness.

Findings

Hybrid model outperforms traditional models in volatile demand scenarios.

Decomposition of demand improves forecasting accuracy.

Support vector regression and dynamic linear regression are robust across demand variations.

Abstract

The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Demand unpredictability is one of the managers' concerns in the supply chain that can cause large forecasting errors, issues in the upstream supply chain and impose unnecessary costs. We investigate 843 real demand time series with different values of coefficient of variations (CoV) where promotion causes volatility over the entire demand series. In such a case, forecasting demand for different CoV require different models to capture the underlying behavior of demand series and pose significant challenges due to very different and diverse demand behavior. We decompose demand into baseline and promotional demand and propose a hybrid model to forecast demand. Our results indicate that our proposed hybrid model generates robust and accurate forecast across series with different levels of volatilities. We stress the necessity of decomposition for volatile demand series. We also model demand series with a number of well known statistical and machine learning (ML) models to investigate their performance empirically. We found that ARIMA with covariate (ARIMAX) works well to forecast volatile demand series, but exponential smoothing with covariate (ETSX) has a poor performance. Support vector regression (SVR) and dynamic linear regression (DLR) models generate robust forecasts across different categories of demands with different CoV values.