More and more companies are relying on Robotic Process Automation – and with great success, as the ISG study reports: RPA leads to significant increases in productivity, more efficient use of resources and in fact shows no correlation to job losses. Employees are given the opportunity to perform higher-quality tasks while being more productive at the same time. A win-win situation. So RPA is an effective way to automate standard processes. Standard processes, however, only cover part of the value chain. The use of AI-based RPA systems can considerably expand this area.
When implementing RPA systems, an important aspect is often left out: Transparency and Controlling. Companies use a large number of process-supporting IT systems, making cross-system process transparency increasingly demanding. The use of RPA systems further complicates this situation. Process Mining is therefore crucial at this point. LANA Process Mining monitors and analyzes all process activities, including those of software robots.
Learning skills make systems more intelligent
Simple RPA systems imitate the human operation of programs by executing preset click and enter commands. This allows companies to automate simple standard processes. Intelligent RPA systems go one step further: software robots not only simulate human input during task processing but even human thought processes. This enables them to autonomously execute complex tasks and make decisions independently even in new situations. The decisive factor here is the AI-based learning capabilities of the system. Using both historical data and live data, software robots learn to understand and replicate the behavior patterns of employees.
Implementing processes more intelligently and efficiently – Process Mining as a success factor
For the implementation of RPA systems, as well as for the monitoring and optimization of the systems involved in the process, Process Mining is crucial. LANA Process Mining aggregates the process data of the systems and visualizes them in clear process models. This gives companies cross-system process transparency and enables them to perform automated performance measurements of the IT and RPA systems involved. Before implementation, LANA is the basis for the standardization and optimization of the processes, so that the RPA systems carry out the process activities efficiently and according to plan. After implementation, LANA can be used as a central controlling tool that analyzes and monitors the entire process performance. Especially after RPA implementation, it is important to check the automation performance in order to avoid repeated errors, for example, or to uncover further optimization potential. LANA’s unique machine learning algorithm is essential: it automatically uncovers the causes of any weak points or inefficiencies. For users, this is the fact-based decision-making basis for improvement measures. LANA also identifies the relevant performance indicators such as efficiency, quality or conformity indicators and can be continuously monitored via dashboards. The entire analysis and reporting process is automated with LANA and thus forms the basis for continuous process improvement. In the future, processes will be monitored in real time, i.e. on the basis of live data, so that controlling cycles can be optimized and risks identified at an early stage. This enables companies to be agile and proactive, identify new trends and implement their processes efficiently and customer-oriented.
Automate smart – digital process management
RPA is an effective technology for the automation of processes. At the same time, however, with each additional system the IT landscape of the company becomes more complex. This makes it increasingly difficult to monitor and analyze business processes. Process Mining is therefore indispensable for the process management of digital processes. Process Mining is not only a success-critical enabler for process automation, but also a controlling tool for the entire cross-system process. LANA combines automated controlling and data-based process analysis. In the future, LANA will use further AI technologies, for example to determine specific solution proposals for optimization potentials and to implement process simulations and interactions between the systems involved in the process – including RPA systems – and LANA.