Supply Chain Forecasting with Machine Learning Approaches
A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Computer Science".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 812
Special Issue Editors
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: time series forecasting; machine learning; deep learning; data science; big data
Special Issues, Collections and Topics in MDPI journals
2. INESC TEC – Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: time series forecasting; machine learning; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Supply chain forecasting is an important aspect of business operations that can be optimized with machine learning approaches. Machine learning can handle a large amount of data in real time, learn and adapt over time, and improve accuracy, efficiency, and profitability. Various machine learning approaches, such as neural networks, decision trees, random forests, support vector machines, and Bayesian networks, can be applied depending on the requirements of the supply chain and available data. Machine learning has the potential to help businesses make more informed decisions, respond more quickly to changes in demand, and achieve long-term success. Given this context, the Special Issue aims to disseminate insights and encourage a more critical discussion and perspective on practical applications of AI and machine learning in supply chain forecasting, as well as recent advancements in utilizing these emerging technologies. To this end, authors are invited to submit original research articles that address significant issues and contribute to the development of new concepts, methodologies, applications, trends, and knowledge in the field. Additionally, review articles that present the current state-of-the-art are also highly encouraged.
Dr. Jose Manuel Oliveira
Dr. Patrícia Ramos
Guest Editors
Manuscript Submission Information
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Keywords
- supply chain management
- machine learning
- demand forecasting
- operations management
- business hierarchical structure
- forecast reconciliation
- inventory management
- artificial intelligence
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