Process mining tools analyze and optimize end-to-end processes, particularly for supply chain management, by collecting event logs from various IT systems to identify inefficiencies and bottlenecks, providing quantitative insights for process improvement. Task mining, on the other hand, focuses on the granular level of individual tasks within a process.
Data origin
Process mining leverages system data extracted from event logs in various enterprise information systems such as Salesforce, Oracle, and HubSpot. These logs provide crucial information about the activities performed, including their identifiers and timestamps.
In contrast, process discovery and task mining collect data by monitoring and recording user interactions with computers. This approach captures all processes with the help of software agents, which provides a comprehensive view of user behavior.
How is data collected?
Data comes from different sources, requiring different collection methods.
For process mining, IT support and robust back-end development are essential to facilitate integrations with controlled information systems, including enterprise resource planning (ERP) systems.
In the case of process discovery and task mining, software agents must be installed on users' computers. These agents run continuously in the background, capturing everything that happens in enterprise software and applications.
Completeness of data collected
It is important to note that some software and applications do not generate event logs, which significantly limits the potential for process mining. For example, if the goal is to map the current e-invoicing process and identify areas for improvement, process mining analyzes the different steps within the selected e-invoicing platform.
However, if the billing expert has to use this platform indonesia phone number example and other applications, for example Excel, to execute this process, the steps in Excel are neglected, which leaves more room for bad results. The system can therefore only capture the discrete data of the particular steps in the process and leaves the blank spaces between the logs which are out of the scope of its discovery.
Process discovery and task mining, on the other hand, can collect data from log-producing information systems, as well as other productivity applications and software that employees use frequently, such as email and the Microsoft suite. This capability makes them excellent complementary tools for process mining and robotic process automation (RPA), as they can provide valuable insights into parallel activities that might otherwise go unnoticed.
Data analysis
Process mining begins with collecting, cleaning, and structuring data to reconstruct the current state of a process and compare it to the ideal version of that process. Focusing on key performance indicators (KPIs), this compliance check uses various data analysis and mining techniques, as well as data science methods, to identify potential bottlenecks and recommend improvements based on observed gaps.
Process discovery captures everything that happens on users’ desktops, enabling an accurate representation of processes as they are executed, with all their random deviations and defects. It visualizes the process as it happens in real life, creating a metamodel using computer vision, machine learning algorithms, and artificial intelligence tools. This approach makes it easier to identify root causes and various bottlenecks, which helps improve overall efficiency.
