In a corporate context, many business processes are partially or even utterly 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 adequate:

Is documenting a vision of the goal process ample for the process to be carried out in follow?

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

Is it potential to measure the actual execution speed of the process from start to complete?

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

A primary definition

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

Thus, Process Mining is a high value-added approach when it comes to building a viewpoint on the actual implementation of a process and figuring out 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 by way of application logs. This stored information often has similarities and makes it doable to hint the trail of an «object» by means of completely different levels at totally 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.

Required data

So as to be usable, these digital footprints should at the least include:

Object: an occasion that will be followed throughout the process, with a singular identifier. The selection of this object influences the scope of the studied process

Activity: a step in the studied process. The selection of activities influences the granularity of the process

Date: determines the order of activities and timing

In addition, it could also be interesting to collect additional data relying on the process, for example: supplier, type of product, location, particular person/administration, channel, value…. These will permit further investigation.

Process visualization and analysis

From these data, it is feasible to visualize a representation of the best process and all deviations from it. This permits for early detection of potential inefficiencies within the process.

Past the illustration of the process, one also can look at the execution instances of each step, or look at a more limited scope in order to establish where, when and why the process deviates from its ideally suited version.

Example with a purchasing process

For a simplified purchasing process ideally composed of 4 steps («Record the order», «Obtain the products», «Record the bill» and «Pay the invoice»), 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 present process when no model exists a priori

Verification of the proper implementation and evaluation of deviations from a earlier model

Process improvement

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

In addition, this approach represents an improvement within the area of process administration:

Acceleration of research (limitation of time spent and number of interviews) to build a illustration of current processes

Taking into consideration more data, and even the exhaustiveness of data, in the measurements

Opportunity, once a new process is designed, to ensure efficient management of its use and to see improvements

Process Mining shouldn’t be dedicated to a particular sector of activity: the approach will be able to deliver value wherever processes are applied and studied. Within a company, a number of capabilities could also 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 enrich their expert vision

CIO: vision of using the systems and the corresponding person paths

Audit or inside management: faster analysis and the possibility of relying on the exhaustiveness of cases quite than on a sample

Key success factors

With a view to acquire good outcomes, the launch of a Process Mining initiative requires some precautions. It may be noted that it is vital:

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

To define a well-defined study scope when it comes to process

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

To make sure the quality of the data on which the research is based. To do this, it is essential to collaborate with the IT experts of the systems used as well because 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 ought to function a factual starting level for further process studies. Reintroducing a human aspect, for instance through the use of a Design Thinking approach, makes it doable to deepen the results obtained thanks to Process Mining by taking the end customers into account.

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