The application of machine learning and deep learning on demand forecasting across time-critical industries: A systematic review
Document Type
Article
Source of Publication
Advanced Engineering Informatics
Publication Date
9-1-2026
Abstract
The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular statistical metrics for evaluating demand forecasting are reviewed and summarized. This study reveals that while machine learning and deep learning are effective for demand forecasting, model selection highly depends on the target industry, data availability, and computational resources. Therefore, this study proposes a conceptual, generic framework that maps data characteristics to appropriate model architecture classes for demand forecasting and recommends adopting scale-independent evaluation metrics. The proposed framework offers a structured pipeline and practical guidance for practitioners and researchers to design forecasting systems across diverse industries, enabling consistent comparative analysis.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
74
Disciplines
Business | Computer Sciences
Keywords
Deep learning, Demand forecasting, Evaluation metrics, Industries, Machine learning
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Seyam, Asmaa; Mathew, Sujith Samuel; Barachi, May El; Zhang, Cheng; and Shen, Jun, "The application of machine learning and deep learning on demand forecasting across time-critical industries: A systematic review" (2026). All Works. 7956.
https://zuscholars.zu.ac.ae/works/7956
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series