Machine Learning and Data Analytics for Business Process Improvement
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 22
Special Issue Editors
Interests: business process modeling; redesign; business analytics; digital transformation
Special Issues, Collections and Topics in MDPI journals
Interests: business process modeling; redesign; business analytics; digital transformation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In an era of rapid digital transformation, businesses are increasingly leveraging machine learning (ML) and data analytics to optimize their processes, enhance decision making, and drive efficiency. This Special Issue aims to explore cutting-edge research and practical applications that showcase the integration of ML and data-driven techniques in business process management (BPM).
We welcome original research contributions, case studies, and review articles covering, but not limited to, the following topics:
- Machine learning models for predictive business analytics.
- Process mining and anomaly detection in business operations.
- Optimization techniques for workflow automation.
- Decision support systems leveraging data analytics.
- AI-driven insights for process efficiency and cost reduction.
- Applications of deep learning and reinforcement learning in BPM.
- Data-driven approaches to risk management and compliance.
- Ethical and interpretability challenges in ML-based business processes.
Researchers, practitioners, and industry experts are encouraged to contribute their latest findings and innovations to this Special Issue. Submissions should demonstrate the impact of ML and data analytics in improving business processes, highlighting real-world implementations, novel methodologies, or theoretical advancements.
For submission guidelines and important dates, please visit the journal's website or contact the Guest Editorial Team.
We look forward to your contributions!
Prof. Dr. Kostas Vergidis
Dr. George Tsakalidis
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. Information is an international peer-reviewed open access monthly 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 1600 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
- machine learning (ML)
- data analytics
- business process management (BPM)
- predictive analytics
- process mining
- workflow automation
- decision support systems
- deep learning
- reinforcement learning
- risk management
- ethical AI
- explainable AI (XAI)
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