Developing a Python-Based Business Process Automation Model for Workflow Optimization and Decision Accuracy
Abstract
Business Process Automation (BPA) has become a critical enabler of organizational efficiency, reducing operational delays, minimizing human errors, and enhancing decision accuracy across diverse industry sectors. Python, with its extensive ecosystem of automation, data-processing, and machine-learning libraries, offers a flexible and scalable foundation for developing workflow optimization solutions. This review paper examines the current state of Python-based BPA models, exploring how frameworks such as Airflow, FastAPI, Celery, Robocorp, and workflow-oriented machine learning pipelines can streamline repetitive tasks, orchestrate complex processes, and support data-driven decision-making. The study synthesizes existing research on rule-based, robotic, and AI-assisted automation approaches, emphasizing their applicability in finance, healthcare, supply chain, and administrative process optimization. Key architectural components?including event-driven automation, API-based integrations, intelligent exception handling, and human-in-the-loop decision support?are critically analyzed. The review also highlights emerging trends such as predictive workflow optimization, explainable AI-enabled decision models, and the integration of digital twins for operational forecasting. The paper concludes by identifying practical implementation considerations, challenges, and future research directions for designing robust, scalable, and interpretable Python-based BPA systems capable of driving enterprise-wide digital transformation.
Keywords
Business Process Automation (BPA)? Python Workflow Modeling? Robotic Process Automation (RPA)? Decision Support Systems? Workflow Optimization, Intelligent Automation.
Citations
IRE Journals:
Odunayo Mercy Babatope, Winner Mayo, Precious Osobhalenewie Okoruwa, David Adedayo Akokodaripon "Developing a Python-Based Business Process Automation Model for Workflow Optimization and Decision Accuracy" Iconic Research And Engineering Journals Volume 3 Issue 3 2019 Page 326-348 https://doi.org/10.64388/IREV3I3-1713061
IEEE:
Odunayo Mercy Babatope, Winner Mayo, Precious Osobhalenewie Okoruwa, David Adedayo Akokodaripon
"Developing a Python-Based Business Process Automation Model for Workflow Optimization and Decision Accuracy" Iconic Research And Engineering Journals, 3(3) https://doi.org/10.64388/IREV3I3-1713061
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