How does Process Mining support process management?
Decision-makers agree: Big data and Artificial Intelligence (AI) are the most important trends of the future – this is the result of a study by PwC. Against the background of major changes in all markets, companies in all industries have to keep an eye on their own processes. Process Mining plays an essential role here.
New market conditions demand more efficient processes
The digital transformation is changing the conditions for companies in all industries. On the one hand, the triumph of digital platforms is leading to a splitting up of value chains. At the same time, the barriers to market entry are falling. Many digital business models can now be realized without large amounts of capital or production resources. Companies that used to have complete control of their value chain now have to assert it against new competitors.
As a result, existing processes are under scrutiny. Companies must identify inefficiencies and take action to optimize them. Classic business intelligence and data-based methods are reaching their limits here.
Where Process Modeling fails
Classic business intelligence deals with the processes within a company. However, business intelligence maps real processes by putting a certain model into the processes. Thus, modeled processes are always only a more or less accurate approximation of reality. In practice, however, deviations occur time and again:
- Employees bypass the target process because it is impractical or unfeasible in everyday life
- Process run times are too high because the process takes longer than the model assumes
- Processes are efficient because individual process steps could be automated
For a long time, process modeling was sufficient. However, digitalization is bringing new competitors into all industries who are more efficient and more agile. Model-oriented process observation is therefore no longer sufficient for future-proof process management.
How does Process Mining work?
As with classic process management, Process Mining is about the visualization of processes. However, Process Mining takes a different approach. Instead of inserting a model into the real processes, the real process is its own model.
To do this, Process Mining takes advantage of a characteristic of the digitized world. All processes are IT-supported. IT-supported processes always leave traces in the relevant systems. Process Mining is about identifying these traces, visualizing them, and assembling them into a coherent overall picture that completely depicts the process. Each individual process step and all deviations can then be visualized in a single diagram.
3 Techniques of Process Mining
Process Mining consists of 3 phases:
1. Event Collection
Every action in a digital system leaves traces on that system. Depending on the environment, for example, users, usage time and activity are documented. Software stores such information in log files. For a long time, these log files were uninteresting. Only in the event of a bug or crash did they become relevant to the IT department responsible. But for modern enterprise control, these logs are essential to get a complete, data-based picture of real-world processes. They map the observable behavior of real users and thus provide a true-to-life view into process reality.
Data in itself has no meaning. Only the interpretation turns the collected data into a useful tool. Through Process Discovery, the collected data is visualized in an interactive and dynamic environment.
Algorithms assemble the data based on predefined rules, creating exactly the picture of the processes they need.
3. Conformance Checking
Process Mining is not in contrast to classical, data-based analysis methods. Model-driven analyses still have their use in marketing, sales, customer service and accounting. Process Mining is the mediating link between both data and process analysis. Conformance checking is used to determine the extent to which event logs conform to this reference model. In other words: Conformance Checking finds out if existing models are more than a paper tiger. It makes skipped or additional paths visible.
With process mining to efficient processes throughout the company
After Process Mining, you know which processes are running in the company and what they look like exactly. This provides the basic framework for analyzing and evaluating these processes:
- Where are there inefficiencies?
- Where do redundancies occur?
- Are employees sufficiently trained?
- Are the defined processes adequate to the applicable rules in the company or do employees waste time by circumventing internal rules?
After Process Mining has been completed, a complete picture of the processes emerges. Those responsible now develop a strategy to systematically optimize the processes. Inefficient processes are often found in all departments, so the possible use cases are diverse.
The processes affected are often those that involve multiple departments and responsible parties and are not significantly pre-structured by existing systems.
- Invoices are paid late because the duration of the documents is too long and addresses are matched manually
- New orders take too long because the approval process is unnecessarily complicated
- Customer satisfaction suffers from an inefficient order-to-cash process that is not fully visible to any of the parties involved without Process Mining
However, Process Mining is particularly interesting because of its exploratory nature. The strength of the technique lies in the identification of inefficiencies and errors where no human employee would suspect errors.
Implement & monitor processes
After optimization, the new processes must be implemented. Whether the optimizations are effective can then be determined by comparing the event logs. Once integrated, Process Mining is essential for monitoring process performance. It maps whether processes are executed correctly and produce consistent results. This creates a viable basis for an iterative approach to process management, where processes can be continuously optimized.
Automate processes: Robotic Process Automation
Process automation has one central prerequisite: the automated process must be known down to the last detail and be capable of being standardized. IT-supported processes are often so complex that no one has a complete overview of the process. Based on process mining, it is also possible to identify those processes that are suitable for Robotic Process Automation. How Process Mining Empowers Process Automation
Process Mining helps prioritize process automation. Companies can use the analysis to identify the processes whose automation promises the greatest return on investment.- These are often the processes that occur particularly frequently. To identify such process types, the processes can be harmonized.
From Data to Process Analytics: Big Data, Predictive Analytics and the Future of Process Management
Process Mining is indispensable for modern and sustainable process management. It forms the basis for the iterative optimization of central business processes and for the automation of standard processes. Therefore, every company should consider the introduction of a Process Mining system.