Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. (April 2020)

• Record Type: Journal Article
• Authors: Abolghasemi, Mahdi; Beh, Eric; Tarr, Garth; Gerlach, Richard
• Is Part Of: Computers & industrial engineering. Volume 142(2020)
• Publication Date: 2020-04
• Subjects: Demand volatility -- Promotions -- Forecasting models -- Robust forecasts -- Inventory performance; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106380
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The association between time series features and demand volatility is objectively examined. Demand volatility is shown to significantly impacts the accuracy of forecasts. A hybrid model is proposed to forecast highly volatile demands. The robustness of statistical and machine learning models is empirically evaluated. An empirical analysis is provided with insights for researchers and practitioners. 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 and robust inventory performance 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 forecast accuracy and inventory 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. In terms of inventory performance, ARIMAX and combination models have superior performance to the other presented models. The hybrid algorithm also depicts robust performance across different series with different CoVs and has low inventory costs.