What is Process Mining?
By: Thomas Zarazik
Tags: Process, Data Science, Data Visualization, Process Mining, Business Process

Photo by Franki Chamaki on Unsplash

What is Process Mining?

An analytical approach that aims to build an exhaustive and objective vision of processes based on factual data

Context and issues

In a corporate context, many business processes are partially or even completely supported by IT systems: the digitalization of processes represents more and more activities, supported by a growing number of systems that generate ever more data.

That being said, it is legitimate to ask whether traditional ways of studying processes are still sufficient:

Is documenting a vision of the target process sufficient for the process to be implemented in practice?

for the process to be implemented in practice? When a deviation from a model is perceived , is it optimal to seek consensus in a group from subjective points of view ?

, is it optimal to seek consensus in a group from ? Is it possible to measure the actual execution speed of the process from start to finish?

Process Mining provides a new approach to take these elements into account.

A first definition

Process Mining is an analytical approach that aims to build an exhaustive and objective vision of processes based on factual data.

Process Mining is at the crossroads of Data Mining and Business Process Management

Thus, Process Mining is a high value-added approach when it comes to building a viewpoint on the actual implementation of a process and identifying deviations from the ideal process, bottlenecks and potential process optimizations.

How does it work?

Whatever the nature of the process , as soon as it is supported by digital tools, information is created and stored by the corresponding IT systems (ERP, business applications, etc.), in particular via application logs. This stored information often has similarities and makes it possible to trace the path of an “object” through different stages at different times in time.

Process Mining is based on tools that use these digital footprints to reconstruct, visualize and analyze processes, thus providing transparency and objectivity towards the real process.

The process analysis is based on the application logs

Required data

In order to be usable, these digital footprints must at least include:

Object : an instance that will be followed throughout the process, with a unique identifier. The choice of this object influences the scope of the studied process

: an instance that will be followed throughout the process, with a unique identifier. The choice of this object influences the scope of the studied process Activity : a step in the studied process. The choice of activities influences the granularity of the process

: a step in the studied process. The choice of activities influences the granularity of the process Date: determines the order of activities and timing

In addition, it may be interesting to collect additional data depending on the process, for example: supplier, type of product, location, person/management, channel, value…. These will allow further investigation.

Process visualization and analysis

From these data, it is possible to visualize a representation of the ideal process and all deviations from it. This allows for early detection of potential inefficiencies in the process.

Beyond the representation of the process, one can also look at the execution times of each step, or look at a more limited scope in order to identify where, when and why the process deviates from its ideal version.

Example with a purchasing process

For a simplified purchasing process ideally composed of 4 steps (“Record the order”, “Receive the goods”, “Record the invoice” and “Pay the invoice”), the process followed by orders is traced from the digital footprints left in an ERP.

Exploitation of digital footprints:

Process visualization:

You will quickly notice that, compared to Order 1 (black) which follows the ideal process, the other two orders are in deviation:

Order 2 (purple) follows an additional step: “Change price”.

(purple) follows an additional step: “Change price”. Order 3 (blue) follows the right steps, but not in the ideal order

Use cases and benefits

There are three major use cases of Process Mining:

Discovery: building a vision of an existing process when no model exists a priori Verification of the correct implementation and analysis of deviations from a previous model Process improvement

In all three cases, it is the understanding of the actual implementation of processes, based on objective and exhaustive data, that makes the added value of the Process Mining approach.

In addition, this approach represents an improvement in the field of process management:

Acceleration of studies (limitation of time spent and number of interviews) to build a representation of existing processes

(limitation of time spent and number of interviews) to build a representation of existing processes Taking into account more data , or even the exhaustiveness of data, in the measurements

, or even the exhaustiveness of data, in the measurements Opportunity, once a new process is designed, to ensure effective management of its use and to see improvements

Process Mining is not dedicated to a particular sector of activity: the approach will be able to bring value wherever processes are implemented and studied. Within a company, several functions may be interested in the approach:

Operational excellence teams: complementing the methods already used (Lean, Six Sigma, etc.)

Data Scientists: visual representations of data to generate new insights

Process managers: factual analyses to complement their expert vision

CIO: vision of the use of the systems and the corresponding user paths

Audit or internal control: faster analysis and the possibility of relying on the exhaustiveness of cases rather than on a sample

Key success factors

In order to obtain good results, the launch of a Process Mining initiative requires some precautions. It can be noted that it is important:

To identify from the outset the added value objective : cost reduction, improvement of the user/customer experience….

: cost reduction, improvement of the user/customer experience…. To define a well-defined study scope in terms of process

in terms of process To operate iteratively with short cycle analyses, within a fixed total time limit

with short cycle analyses, within a fixed total time limit To ensure the quality of the data on which the study is based. To do this, it is essential to collaborate with the IT experts of the systems used as well as the business experts of the processes studied

on which the study is based. To do this, it is essential to collaborate with the IT experts of the systems used as well as the business experts of the processes studied To accompany the change in case of redefinition of a target process

Moreover, the analyses carried out by Process Mining should not be an end in itself but should serve as a factual starting point for further process studies. Reintroducing a human aspect, for example by using a Design Thinking approach, makes it possible to deepen the results obtained thanks to Process Mining by taking the end users into account.

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