Forecasting demand profiles of new products

R. M. van Steenbergen*, M. R.K. Mes

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Nowadays, many companies face shorter product life cycles, increasing the need to properly forecast demand for newly introduced products. These forecasts allow them to support operational decisions, such as procurement and inventory control. However, forecasting the demand of new products is challenging compared to existing products, since historical sales data is not available as an indicator of future sales. Moreover, little attention has been paid in literature to quantitative methods for new product forecasting, especially with respect to quantifying the uncertainty in demand. In this paper, we present a novel demand forecasting method denoted by DemandForest, which combines K-means, Random Forest, and Quantile Regression Forest. This machine learning-based approach combines the historical sales data of previously introduced products and product characteristics of existing and new products to make prelaunch forecasts and support inventory management decisions for new products. DemandForest clusters and predicts demand patterns, and predicts the quantiles of the total demand during an introduction period. We validate and illustrate our approach for forecasting and inventory management using real-world data sets of several companies. Compared to several benchmark methods, DemandForest provides the most accurate predictions, resulting in potential inventory savings of around 15% depending on lead times and service levels.

Original languageEnglish
Article number113401
JournalDecision support systems
Volume139
Early online date9 Sep 2020
DOIs
Publication statusE-pub ahead of print/First online - 9 Sep 2020

Keywords

  • UT-Hybrid-D
  • New product forecasting
  • Pre-launch forecasting
  • Quantile regression Forest
  • Random Forest
  • Inventory management

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