What’s Process Mining?

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 rising number of systems that generate ever more data.

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

Is documenting a vision of the target process sufficient for the process to be applied in apply?

When a deviation from a model is perceived, is it optimum to seek consensus in a group from subjective factors of view?

Is it possible to measure the precise execution speed of the process from start to complete?

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

A first definition

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

Thus, Process Mining is a high worth-added approach when it involves building a viewpoint on the precise implementation of a process and identifying deviations from the perfect 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, enterprise applications, etc.), in particular through application logs. This stored information often has similarities and makes it attainable to hint the path of an “object” by completely different levels at different occasions in time.

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

Required data

To be able to be usable, these digital footprints should a minimum of embody:

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

Activity: a step within 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 gather additional data relying on the process, for instance: provider, type of product, location, person/management, channel, value…. These will enable additional investigation.

Process visualization and evaluation

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

Beyond the illustration of the process, one may also look on the execution instances of every step, or look at a more limited scope with the intention to determine the place, when and why the process deviates from its ideal version.

Instance with a purchasing process

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

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 proper implementation and evaluation of deviations from a previous model

Process improvement

In all three cases, it is the understanding of the actual implementation of processes, based mostly on goal 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 illustration of present processes

Taking under consideration more data, or even the exhaustiveness of data, in the measurements

Opportunity, as soon as a new process is designed, to make sure efficient management of its use and to see improvements

Process Mining just isn’t dedicated to a particular sector of activity: the approach will be able to carry value wherever processes are applied and studied. Within a company, several capabilities may be interested in the approach:

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

Data Scientists: visual representations of data to generate new insights

Process managers: factual analyses to enrich their knowledgeable vision

CIO: vision of the usage of the systems and the corresponding consumer paths

Audit or internal management: faster evaluation and the possibility of counting on the exhaustiveness of cases rather than on a sample

Key success factors

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

To determine from the outset the added value objective: cost reduction, improvement of the consumer/buyer expertise….

To define a well-defined research scope by way of process

To operate iteratively with short cycle analyses, within a fixed total time limit

To make sure the quality of the data on which the examine is based. To do this, it is essential to collaborate with the IT experts of the systems used as well as the enterprise experts of the processes studied

To accompany the change in case of redefinition of a goal process

Moreover, the analyses carried out by Process Mining should not be an end in itself but should serve as a factual starting level for additional process studies. Reintroducing a human side, for example by utilizing a Design Thinking approach, makes it potential to deepen the outcomes obtained thanks to Process Mining by taking the tip customers into account.

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