How can Artificial Intelligence & Machine Learning improve processes? Robotic Process Automation automates and improves individual tasks but it requires a hybrid intelligence to combine data, algorithms, and people.
Process mining is not a magic wand. It is a discipline that requires a smart human being who can make the connection between the data and the underlying business process — with the help of the process mining tool. You have to apply your domain knowledge to interpret the results and develop improvement ideas.
Pundits love to tell us which professions will be replaced by robots any day now. All the while, the smart devices in our pockets and at home are not as smart as we would like them to be. Where exactly lies the boundary between what machines can do, and what will humans continue to do well into the future? In his closing keynote, Wil explored the questions of where technology outperforms humans, where human intelligence is needed, and where both blend together to combine the best of both worlds.
Fluxicon organizes Process Mining Camp since 2012. It's our favorite part of the year. We look back at the talks from the past nine years and categorize them into industries and use cases.
During the first quarter of 2020, the mortgage provisioning lead time was above the stated limit. Daan shows how only the process analyst and the domain expert together can solve the problem.
We discuss process improvement methodologies and best practices for process improvement with Sudhendu Rai and Daan Jabroer.
When you perform an audit with process mining, you need to be methodical in your approach and tame the complexity by focusing on the right questions.
We talk about audit standards, new definitions of materiality, and research on how auditors assign risk to deviations with Mieke Jans, Jasmine Handler, and Andreas Preslmayer.
Process mining complements productivity management in the digitized industry. Domain knowledge is necessary to generate improvement ideas and multi-disciplinary teams are needed to implement the change.
We dive into Lean manufacturing and discuss whether data analysis is too far away from the physical processes with Joris Keizers, Minh Chau Nguyen, and Klaus Kühnel.
Gary shows how he got from disparate data sources to an end-to-end process visibility. As a process owner, process mining allows him to analyze his process completely by himself, independently from the IT department.
We talk about where to find data and how to make it available for fresh analyses with Carmen Lasa Gómez, Javier García Algarra, and Gary Bonneau.
We discuss explainability research, multi-agent systems, and responsible data science with Manuela Veloso and Wil van der Aalst.
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