Process Mining and Machine Learning
Capture, document and analyze processes: Manual process audits are time-consuming and error-prone. Process Mining allows the processes within a company to be analyzed with software support. Artificial Intelligence (AI) plays an essential role in this.
From manual audit to process analysis
For a long time, pen and paper were the most important tools in the systematic documentation of processes within a company. However, this approach is facing growing challenges:
The high complexity of modern business processes makes decisions that take all essential factors into account complicated. Decision-makers need data-based decision models that go beyond intuition and gut feeling.
Manual process documentation is prone to errors. Errors in process descriptions lead to poorer decisions for the further development of these processes.
In the digital world, time is of the essence for sustainable business success in all areas of the company. Manual process audits only map processes that lie in the past. A manual audit is of no help for processes that are currently running.
Against the background of growing competitive pressure in all industries, effective process management is more important than ever. But digitization also opens up an opportunity. To put process management on software-supported feet and to take advantage of the possibilities offered by machine learning technologies.
What is Process Mining?
In Process Mining, software reads event logs from IT systems and uses them to interpolate the underlying processes. Process mining presents the results clearly in a visualization of the analyzed processes. Decision-makers use these visualizations to decide which processes need to be optimized.
In principle, no Artificial Intelligence (AI) is required for this form of Process Mining. Process Mining software analyzes event logs based on rules and gives structure to the captured events. However, AI technologies significantly expand the range of Process Mining use cases.
What is Machine Learning?
Machine Learning describes the ability of IT systems to evolve autonomously based on data – without code deterministically dictating behavior. To do this, Machine Learning uses algorithms – mathematical rule sets that are applied to the data. The algorithms are built in such a way that they can be used to recognize patterns within the data set.
There are various methods for doing this. Neural networks are increasingly used in real-world business scenarios. Artificial neural networks are modeled on the human brain. Like the brain, a neural network consists of three neurons:
- Input neuron
- Output neuron
- Hidden neuron
Input neurons take in information, output neurons give it back. In between sit hidden neurons that map internal information patterns. Complex networks additionally have connections through edges: Neurons receive information on the one hand and pass it on to other neurons on the other. When this network is fed with historical process data, it gradually develops a better understanding of the structure based on the feedback it receives.
Process Mining and AI: How process management benefits from self-learning systems
Due to the triumph of AI, four application fields in process mining have emerged in recent years:
- Descriptive Mining
- Diagnostic mining
- Prognostic Mining
- Prescriptive mining
Understanding Business: Descriptive Process Mining
Process Mining a method to identify patterns within processes and thus gain a better understanding of the essential business processes. Machine Learning already plays an important role at this stage. The algorithm evaluates the collected log files and learns to
- Identify commonalities and form groups
- Detect outliers.
- Identify problems
Through these processes, the algorithm learns about the process. The Grouped and Classified data, in turn, allows KPI-critical processes to be interrogated for important success criteria. In the manufacturing industry, for example, this may involve lead time in the production sector: How long does a part take to be ready for packaging in the desired quality?
Knowledge of these key data is the prerequisite for initiating the necessary measures for optimization.
Identify root causes: Diagnostic Process Mining
The systematic evaluation of event logs in the context of Process Mining is basically not dependent on Machine Learning. But only diagnosis opens up insights into the reasons and causes for an event and thus creates the basis for remedying the problem.
- Identify causes of problems found
- Classify problems
- Capture trends
Diagnostic Process Mining is important where problems need to be located within a process. Modern supply chains, for example, are often so complex that the weak points are not obvious. A modern diagnostic solution can monitor these processes in real time and automatically alert the appropriate people in the company.
Making predictions: Predictive Process Mining
Machine Learning becomes particularly exciting where viable forecasts are to be derived from historical data. Predictive analytics deals with the data-based generation of such forecasts:
- Prediction of central KPI and your development
- Prediction of events
Predictive Process Mining creates a data basis for the strategic management of the company. While diagnostics produce insights in real time at best and mostly in hindsight, predictive methods can produce robust forecasts for the future. Historical data can be used to identify trends in sales patterns. This knowledge can in turn be used – for example, to conduct an intensive marketing campaign at a specific time.
The Future of Process Management: Prescriptive Process Mining
Process managers have a lot of information about the past, present and future of business processes in the company through descriptive, diagnostic and predictive process mining. However, new challenges arise before the volume of new data:
- Which processes should be optimized first?
- Which processes are particularly suitable for RPA?
Prescriptive Process Mining automates decision making and performs many tasks autonomously. Machine Learning-based systems learn on their own based on input. They have the same information available as the decision maker – but they have more experience than the decision maker.
- Send notifications to users
- Start workflows or automation routines on their own
- Interact with the ERP or CRM
In many business-critical processes, time plays an essential role. In customer support processes, it is decisive for customer satisfaction. The longer it takes for a customer to receive an initial response, the more dissatisfied they become. With prescriptive Process Mining, not only the diagnosis of a delivery delay can be automated, but also the corresponding notifications to customer support. Customer support is notified early and the customer’s problem can be addressed before it becomes current.
Conclusion: Process Mining and AI
Process Mining and AI technologies are working together to revolutionize process management in the enterprise.
- Machine Learning-supported Process Mining is a helpful tool both for classic cases and questions from the field of business intelligence and for the data-based forecasting of business-critical events and key figures.
- Through the software-supported and intelligent evaluation of processes, a more complete picture emerges than in a manual process audit. Diagnostic Process Mining helps to identify acute problems in a timely manner on the one hand, and to localize systemic problems within a process on the other hand.
- Predictive Process Mining can be used to develop robust models for predicting events or KPIs, which offer much higher accuracy compared to classical business intelligence models.
- Prescriptive Process Mining is the latest application field of AI-supported Process Mining: In the future, it offers the potential to automate essential process management tasks.
In the future, the importance of AI-supported Process Mining will continue to increase. Companies in all industries should therefore look into the possibilities that process mining offers for their own processes today.