Data-Driven Business Processes and Decision Optimization
A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 204
Special Issue Editors
Interests: bank operation management; operation management; operation decision analysis; e-enterprise application
Interests: optimization; decision-making; numerical analysis; operation management; neural network
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Organizations today operate in increasingly complex, interconnected, and data-rich environments, where business processes, human decisions, digital infrastructures, and operational outcomes are strongly interdependent. In such settings, improving performance requires not only local process enhancement but also a broader systems perspective that captures feedback loops, uncertainty, cross-functional interactions, and system-wide consequences. Recent advances in big data analytics, artificial intelligence, process mining, optimization, digital twins, and intelligent decision-support technologies have created new opportunities to analyze, design, and optimize business and organizational systems in a more integrated and adaptive manner.
This Special Issue aims to advance research at the intersection of data-driven business processes, decision making, and systems analysis. It particularly welcomes contributions that examine how data-driven methods can support the modeling, control, coordination, and optimization of complex business, industrial, service, and public-sector systems. In line with the scope of Systems, this Special Issue encourages studies that adopt a system-level view of organizational processes and decisions, including interactions among technical, managerial, and human components. We also welcome contributions addressing uncertainty-aware and knowledge-based decision frameworks, such as multi-criteria decision-making (MCDM), fuzzy decision systems, and their advanced extensions, for improving resilience, efficiency, adaptability, and sustainability in complex systems.
Potential topics include, but are not limited to, the following:
- Business process mining, discovery, and system-level process analysis;
- Process performance evaluation and continuous improvement in organizational systems;
- Data-driven business process management in complex socio-technical systems;
- Decision support systems and intelligent analytics for system design and control;
- Predictive modeling and forecasting for business and service systems;
- Optimization models for operational, tactical, and strategic decision making;
- Multi-criteria decision-making (MCDM) in business, industrial, and management systems;
- Fuzzy decision-making methods for complex and uncertain systems;
- Intuitionistic fuzzy, Pythagorean fuzzy, and q-rung orthopair fuzzy decision models;
- Neutrosophic sets and neutrosophic decision-making approaches in system environments;
- Hybrid MCDM frameworks integrated with machine learning, AI, or optimization;
- Digital twins for process monitoring, simulation, and decision support;
- AI and machine learning for process automation and decision intelligence;
- Risk analysis, uncertainty modeling, resilience, and robustness in organizational systems;
- Supply chain, logistics, and service systems optimization;
- Sustainable, adaptive, and human-centered systems for business and management.
Prof. Dr. Rongho Lin
Dr. Amirhossein Nafei
Guest Editors
Manuscript Submission Information
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Keywords
- business process management
- decision making
- multi-criteria decision making
- fuzzy sets
- artificial intelligence
- process mining
- decision optimization
- intelligent decision support
- data-driven systems
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