From Process Mining to Process Intelligence

What is Process Mining?

While the volume of data available is estimated to increase by 40% per year, it is a source of strategic information for your business. Now, it is no longer a question of looking for where to find information, but rather of identifying those that will be relevant and useful in this ocean of available data. Each application (CRM, ERP, business applications, etc.) has a log of events or actions performed. Process Mining is the analysis of the data of the real course of a company’s processes, in order to optimize their performance. By collecting or extracting data mining (data mining) from these records, Process Mining creates a model that accurately reflects the execution of a process. The analysis of the models thus created makes it possible to understand the processes at work and to discover possible deviations from the optimal path and finally to search for the causes.

Once a process is equipped, the Process Mining approach makes sense to analyse and optimise it.

Jean-Pierre HOTTIN, Risk Management Partner, PwC France and Maghreb

The fields of application are multiple: in the banking industry, Process Mining allows for example the analysis of the credit granting process. The field of purchasing, particularly in the context of the regulation of payment terms, is particularly suitable for a Process Mining analysis to understand transactions, analyze orders, manage suppliers and ultimately reduce payment terms. In general, Process Mining can be implemented in all industries and is particularly requested in the context of transformation and standardization initiatives.

What does Process Mining bring?

Whereas processes were previously modelled through interviews with the various actors and relied on reading manuals, these analyses were time-consuming and error-prone. Moreover, based only on the statements of the actors concerned, and not on an exhaustive analysis of reality, the processes were analyzed subjectively and were therefore not optimized in their entirety. Thanks to Process Mining, the approach is automated and generalized, and gives a more precise vision of possible optimizations. This approach, which places data at the heart of the analysis, makes it possible to make more precise and faster decisions. In addition, you can see at a glance the differences between the model and the actual functioning of your processes. Your performance gains are then more easily identifiable and measurable. Finally, you increase the automation of processes, thus gaining in productivity.

Process Mining represents a very important step forward in process analysis and optimization compared to a manual approach.

In short, you accelerate the digital transformation of your function and increase productivity while reducing compliance risks. This means you save money.

However, to transform Process Mining into Process Intelligence, it is necessary to go even further.

 

How to transform Process Mining into Process Intelligence?

 

1- Identify root causes of process deviations

While traditional Process Mining processes identify relatively simple process-related problems, Process Intelligence goes further by highlighting deeper causes (root causes). By defining an ideal path in an environment of operational excellence, and by comparing the most used paths with the expected paths, Process Intelligence highlights frictions in the process (manual activities, round trips, bottlenecks, deviation from the nominal process), and allows faster resolution.

2- Analyze more complex processes

While traditional Process Mining analysis is effective when it comes to evaluating relatively simple processes, it finds its limits when processes become more complex, and presents a significant number of variations. Also, overly complex approval processes generate additional risks whether operational (processing times generating lost opportunities) or regulatory (non-compliance with regulatory deadlines or circumvention of critical controls). In this case, the Process Intelligence approach will help analyze and optimize them.

 

3- Produce real-time analysis

Thanks to the real-time analysis of processes and deviations, Process Intelligence goes further than traditional Process Mining which is limited to the analysis of past data. While the traditional approach allows a good understanding of what has worked and of past shortcomings, it has limitations when it comes to proposing optimization solutions for the future. Real-time analysis makes it possible to enter into a continuous, predictive improvement approach and aim for operational excellence.

With the adoption of a Process Intelligence tool, you equip your organization with a great asset to achieve operational excellence, you gain efficiency and compliance. You then open a field of possibilities and identify new optimization opportunities: for example, Robotic Process Automation (RPA) to automate the processes identified and corrected through Process Intelligence or conversion to SAP S4/Hana type systems.