Combining Predictive Data Analytics with Process Mining for Proactive Business Process Improvement
Published 20-11-2022
Keywords
- predictive analytics,
- process mining
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
This paper investigates the integration of predictive data analytics with process mining techniques to facilitate proactive business process improvement. By leveraging predictive models, businesses can anticipate potential disruptions, inefficiencies, or bottlenecks within their processes before they occur. Process mining, in turn, enables the extraction of valuable insights from event logs to visualize and analyze actual process flows. Combining these two methodologies empowers organizations to identify areas that require optimization, perform early-stage risk management, and implement corrective actions before problems escalate. The synergy between predictive analytics and process mining thus allows for a data-driven approach to continuous process improvement, offering a pathway to more agile and resilient business operations. Furthermore, this research explores the role of advanced analytics tools in enhancing decision-making capabilities, providing a more robust framework for process optimization. By focusing on predictive insights, organizations can reduce downtime, improve operational efficiency, and achieve better resource allocation. The paper also discusses the challenges and limitations of integrating these technologies, including data quality concerns and the complexities of interpreting predictive outcomes in real-time. The findings of this research contribute to the ongoing discourse on the use of advanced analytics for proactive process enhancement in modern enterprises.
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