Wil van der Aalst, Chief Scientist at Celonis, and Cong Yu, VP Engineering at Celonis, discuss process mining, its benefits for organizations, and examples of its application in companies like BMW and Uber. They explore the use of AI in process mining, the importance of data engineering, and the potential of process mining to transform industries.
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Quick takeaways
Process mining helps organizations optimize workflows and make data-driven decisions for process improvement.
Process mining provides a holistic view of end-to-end processes, uncovering inefficiencies and enabling organizations to save time and reduce costs.
Object-centric process mining offers a more comprehensive understanding of interdependencies between processes and facilitates automation and streamlining of business processes.
Deep dives
Process mining: A powerful technique to optimize workflows
Process mining is a technique that can help organizations optimize their workflows by using data to identify inefficiencies and bottlenecks. It involves extracting event data from various systems and using it to uncover what is really happening in the organization's processes. By visualizing the actual flow of activities, process mining reveals discrepancies between expected and actual outcomes. This transparency allows organizations to pinpoint areas for improvement and make data-driven decisions to optimize their workflows. Examples of process mining benefits include saving millions of dollars by detecting and preventing duplicate invoices, improving manufacturing lead time, reducing shipment delays, improving customer satisfaction, and increasing working capital. Process mining tools, such as those provided by Solonis, offer enterprise-level capabilities for analyzing and monitoring processes at scale. The adoption of process mining requires support from top-level management, involvement from data scientists or analysts, stakeholder buy-in from different lines of business, and alignment with the IT department. The time scale for achieving benefits from process mining can vary, but improvements can be seen in a matter of weeks to months. Continuous monitoring and feedback loops are important for sustaining process improvements, and process mining becomes an indispensable tool in enhancing operational efficiency and driving business growth.
The power of process mining in optimizing processes
Process mining offers quantifiable benefits to organizations by optimizing workflows and improving operational efficiency. It enables organizations to identify and address inefficiencies, compliance issues, bottlenecks, and risks in their processes. Process mining provides a holistic view of the end-to-end processes by extracting event data from various systems and visualizing it in a transparent way. It helps organizations uncover the gap between expected and actual processes, enabling them to take data-driven actions for process improvement. Whether it is automating manual tasks, predicting and preventing problems, or enhancing customer satisfaction, process mining allows organizations to save time, reduce costs, improve quality, and increase productivity. The adoption of process mining may start with standard processes, such as financial or administrative processes, before expanding to more unique and complex processes within the organization. The benefits of process mining are not limited to large enterprises, as smaller organizations can also derive value from this technology. With continuous monitoring, organizations can sustain process improvements and ensure efficient operations in the long run.
Object-centric process mining and its transformative potential
Object-centric process mining is an emerging approach in process mining that focuses on the interactions between objects across multiple processes. Unlike traditional process mining, which focuses on individual processes, object-centric process mining provides a more holistic view of how objects flow through the organization's processes. It enables organizations to gain deeper insights into the interdependencies between processes and the potential impact of changes in one process on others. Object-centric process mining helps organizations understand the end-to-end customer journey, identify areas for optimization, and make informed decisions to enhance operational efficiency. With the combination of process mining and generative AI, such as large-language models, organizations can automate repetitive tasks, streamline processes, and improve overall business performance. The integration of process mining into the organization's software ecosystem, along with the involvement of diverse stakeholders, including top-level management, data scientists, and business users, is essential for successful implementation and continuous improvement of processes.
Real-world examples and successful implementation of process mining
Process mining has gained significant adoption across various industries, including Fortune 500 companies. Organizations such as BMW, Lufthansa, HP, and Dell have successfully implemented process mining to optimize their workflows and achieve substantial benefits. These benefits range from reducing costs and increasing efficiency in financial processes, such as purchase-to-pay and order-to-cash, to improving manufacturing lead time, minimizing shipment delays, and enhancing customer satisfaction. The adoption of process mining is more advanced in Europe compared to the US, with many organizations in countries like the Netherlands and Germany leveraging its capabilities. Process mining tools, including Solonis, have emerged as market leaders in providing enterprise-level process mining solutions. While the initial implementation of process mining may require time for data engineering and scoping, subsequent rollouts become faster and more efficient. The continuous monitoring of processes and the use of intelligent workflows and AI technologies ensure sustainable process improvement and ongoing operational excellence.
Process mining as a catalyst for change management
Change management plays a crucial role in the successful implementation of process mining. Top-level support from executive leadership, including the CFO, COO, and CIO, is essential to drive organizational change and prioritize process optimization initiatives. Building a center of excellence for process mining, involving individuals with technical skills and domain knowledge, helps ensure effective implementation, scaling, and continuous improvement. Process mining provides transparent insights into inefficiencies and compliance issues, creating an undeniable case for change. The ability to drill down into specific cases and highlight deviations from expected processes strengthens the change management process. Process mining acts as an observability tool, enabling organizations to monitor the impact of AI-driven transformations, track KPIs, and measure the effectiveness of process improvements. As process mining becomes a standard tool, organizations can lower the threshold for adopting machine learning and AI technologies, driving efficient operations, proactive decision-making, and long-term business success.
Regardless of profession, the work we do leaves behind a trace of actions that help us achieve our goals. This is especially true for those that work with data. For large enterprises where there are seemingly countless processes happening at any one time, keeping track of these processes is crucial. Given the scale of these processes, one small efficiency gain can leads to a staggering amount of time and money saved. Process mining is a data-driven approach to process analysis that uses event logs to extract process-related information. It can separate inferred facts, from exact truths, and uncover what really happens in a variety of operations.
Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University.
His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 275 journal papers, 35 books (as author or editor), 630 refereed conference/workshop publications, and 85 book chapters.
Cong Yu leads the CeloAI group at Celonis focusing on bringing advanced AI technologies to EMS products, building up capabilities for their knowledge platform, and ultimately helping enterprises in reducing process inefficiencies and achieving operational excellence.
Previously, Cong was Principal (Research) Scientist / Research Director at Google Research NYC from September 2010 to July 2022, leading the NYSD/Beacon Research Group, and also taught at NYU Courant Institute of Mathematical Sciences.
In the episode, Wil, Cong, and Richie explore process mining and its development over the past 25 years, the differences between process mining and ML, AI, and data mining, popular use cases of process mining, adoption from large enterprises like BMW, HP, and Dell, the requirements for an effective process mining system, the role of predictive analytics and data engineering in process mining, how to scale process mining systems, prospects within the field and much more.
