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 429

Special Issue Editors


E-Mail Website
Guest Editor
1. Faculty of Economics, University of Porto, 4200-464 Porto, Portugal
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

E-Mail Website
Guest Editor
1. ISCAP, Polytechnic University of Porto, 4465-004 S. Mamede de Infesta, Porto, Portugal
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • supply chain management
  • machine learning
  • demand forecasting
  • operations management
  • business hierarchical structure
  • forecast reconciliation
  • inventory management
  • artificial intelligence

Published Papers

There is no accepted submissions to this special issue at this moment.
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