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With the upcoming completion of KI.RPA, PAF is strengthening its competencies in the areas of Robotic Process Automation and Artificial Intelligence. With the developed AI technologies, complex recurring tasks in processes will become easy to automate for the first time in the future.
ANN ARBOR, Mich. and DARMSTADT, Germany (PRWEB)
May 25, 2021
The fact that PAF is one of the leading companies in the field of research and further development of Process Mining is confirmed once again by the results of the German Federal Ministry of Education and Research (BMBF) funded research project KI.RPA, which is about to be completed. The background to the project, which is being implemented by PAF together with its partners Servicetrace GmbH, AWSi, TU Darmstadt and Deutsche Telekom Service GmbH, is the fact that a large proportion of current Robotic Process Automation (RPA) projects fail because it is unclear which processes can really be automated well. Existing tools tend to focus only on the automation of well-structured tasks that are particularly easy to capture and automate.
- Executing complex processes via software robots
KI.RPA, on the other hand, was dedicated to automating more complex and less structured tasks through software robots. By using AI, KI.RPA succeeded in accelerating numerous steps. These include, for example, the identification of routine tasks and automation potential, automatic decision-making in the case of branching processes, and the assignment of tasks to suitable employees if automation of a process instance is not possible.
dr Alexander Seeliger, Chief Scientist of PAF, expects the integration of the project results into future PAFnow versions to provide high added value for RPA users: “With the upcoming completion of KI.RPA, PAF is strengthening its competencies in the areas of Robotic Process Automation and Artificial Intelligence. With the developed AI technologies, complex recurring tasks in processes will become easy to automate for the first time in the future. In addition, it can be identified whether certain process flows are suitable for automation at all. In short, we know even better where automation via software robots pays off and can use them much more often.”
- Quick Start with the Upgraded Content Packs for SAP and Infor
The Summer Update also includes enhanced SAP Content Packs. This makes it even easier, for example, to track a document-centric approach at item level in SAP systems. There are also new content packs for Infor LN (purchase-to-pay and opportunity-to-order processes) and Infor M3 (purchase-to-pay processes).
PAFnow Content Packs provide pre-designed data models and business dashboards for a successful quick start to analyze and directly optimize business processes. Since “performance” is an abstract concept, measurable criteria are defined in the content packs, making process performance objectively assessable and business processes reviewable.
“The main advantages of the PAFnow Content Packs are the integration into existing infrastructure, the open data model and the flexible extensibility. In this way, we counteract monolithic systems that seal themselves off from the outside world and thus slow down projects in the long term and drive-up integration costs,” says Dr. Timo Nolle, CTO and Executive Vice President at PAF.
- Flexible database connection
PAFnow offers users a high degree of flexibility for deployment, which is possible in the customer’s own infrastructure both on-premises, hybrid or in the cloud. On the back-end side, the current version of the tool supports connections to a large number of databases. In addition to Microsoft SQL Server, these now include Exasol, Snowflake, Oracle, IBM DB2, PostgreSQL and SAP HANA.
- New Microsoft Azure-based PAFnow Data Transformation Service
With the Summer Update, a certified software application for automated data transformation is also available on the Microsoft Azure Marketplace. This enables users to get started with their Process Mining projects in the shortest possible time. Using the Azure Data Factory, the transformation of data into the event log required for Process Mining can be automated. At all times, user data remains within the user’s own infrastructure. For PAFnow Enterprise users, on-premise automation is still possible via Microsoft SQL SSIS.
- Comprehensive Online Training with the PAF Academy
There is also a fundamental innovation regarding the PAF Academy: From now on, the use of PAFnow can additionally be learned in online-only training courses. The courses consist of four PAFnow training modules, each of which is tailored to the specific requirement levels of end users in the business units. They teach users to independently analyze business processes with PAFnow, to create their own evaluations and to sustainably improve processes. After successful completion of the PAFnow training, users receive the “Certified PAFnow User” certificate. Anyone who books a PAFnow Training course in the future will automatically receive access to the online training courses.
- Automated Conformance Check in the Power BI Data Model
With the Summer Update, PAFnow offers an automated Conformance Check within the data model of Power BI in addition to the visual Conformance Check in the Process Explorer. This means that the results of the Conformance Check are now available for any further processing and analysis in Power BI. Typical questions that can be answered include: How often do I violate approval regulations? What is the most frequent violation? Where exactly does this violation occur? The new Conformance Check is particularly beneficial for companies for which these processes play an important role in day-to-day operations.
- Optimized visualization variants for the Process Explorer and the Case Viewer
The new PAFnow Process Explorer of the Summer Update 2021 contains an extension of the Variant Indicator. Instead of the entire process variant, only upstream and/or downstream su-bsteps of the selected process step can be highlighted. This makes it easier for users to maintain an overview even with very complex process images.
There are also new display modes in the Case Viewer. For example, the Highlight feature can be used to highlight defined outliers – ie particularly high or particularly low values - in color. How the outliers are calculated can be defined via “Highlight Scale” with individual color scaling.
- New Data-Driven Lead Time Calculator and Dark Mode for PAFnow Companion
The Summer Update also includes the previously announced new Lead Time Calculator with drag-and-drop functionality. With this, the lead times for any process sections can be calculated and visualized intuitively, flexibly and completely data-driven in just a few steps. Not only the average duration of the process, but also any outliers or bottlenecks can be identified.
Another practical innovation is the eye-friendly “Dark Mode” for the PAFnow Companion. The Companion transforms event log data from various data sources and end-to-end processes into a PAFnow event log.
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