FROM PROCESS ANALYSIS TO CONTINUOUS INNOVATION: PROCESS MINING AND ITS APPLICATIONS
Process Mining is a process analysis technique applied to event logs meaning information derived from operations performed by an IT system sequentially and chronologically to identify and optimize those processes.
Using specific algorithms, it is possible to extract knowledge and extrapolate habits, patterns and other information regarding the use and behavior of an IT system and processes in general.
In practical terms, Process Mining provides a realistic view of what is actually happening in business processes and can be used to review three main types of KPIs:
the time taken to complete a particular process, the cost associated to complete that process, and quality, understood as the set of predetermined criteria met by those processes.
In particular, Process Mining has a significant advantage over more traditional methods. It does not present a static snapshot of what the data show, but indicates what is occurring within the systems
to detect the actual behavior of people, organisations, and machines and compare it with reference models, correlate millions of events to show how reality is different from perceptions and opinions, and provide a basis for continuous process improvement.
Therefore, Process Mininghelps to understand the current state of systems and processes, identify their deviations and the interventions needed to bring them in line with business rules.
Mastering solutions in Process Mining requires great adaptability and commitment to innovation.
Simplicity, sustainability, and scalability are the characteristics that processes and tools useful for deploying improvement and continuous innovation actions must have.
Companies that intend to turn their business around and experience profound change cannot fail to integrate and embrace new decision-making and management approaches.
A dedicatedHyperautomation team, for example, such as that of SCAI Finance, supports the transformation of business processes through Process Mining platforms by specifically contributing to their monitoring, transformation and streamlining using application robots and process mining platforms that leverage deep learning solutions.
In Test Governance, Process Mining tools allow for real-time process deviation analysis; in Risk Governance, they allow for calculating the cost caused to the customer by a service disruption and predicting its occurrence over time.
Process Mining then becomes the subject of complex intervention strategies, and in each application its goal is to improve the system to achieve a better understanding of business processes to pursue continuous innovation.
