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A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks

Kayyali El-Alem, Yara; Maier, Sebastian; Seifert, Ralf W; (2022) A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks. International Journal of Forecasting 10.1016/j.ijforecast.2022.09.005. (In press). Green open access

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Abstract

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX's performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods.

Type: Article
Title: A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijforecast.2022.09.005
Publisher version: https://doi.org/10.1016/j.ijforecast.2022.09.005
Language: English
Additional information: © 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Forecasting, Machine learning, Product life cycle, Analytics, Deep learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10161893
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