What’s Process Mining?

In a corporate context, many business processes are partially and even utterly 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 studying processes are still enough:

Is documenting a vision of the target process adequate for the process to be applied in follow?

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

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

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

A primary 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 figuring out deviations from the perfect process, bottlenecks and potential process optimizations.

How does it work?

Regardless of the nature of the process , as soon as it is supported by digital instruments, information is created and stored by the corresponding IT systems (ERP, business applications, etc.), in particular via application logs. This stored information typically has similarities and makes it possible to hint the trail of an “object” via different levels at totally different instances in time.

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

Required data

With a purpose to be usable, these digital footprints should at the very least embrace:

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

Activity: a step within 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 fascinating to collect additional data depending on the process, for instance: supplier, type of product, location, particular person/administration, channel, value…. These will allow additional investigation.

Process visualization and evaluation

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

Beyond the illustration of the process, one can also look on the execution times of each step, or look at a more limited scope so as to determine where, when and why the process deviates from its very best version.

Example with a buying process

For a simplified buying process ideally composed of 4 steps (“Record the order”, “Receive the goods”, “Record the bill” 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 main use cases of Process Mining:

Discovery: building a vision of an present process when no model exists a priori

Verification of the right implementation and evaluation of deviations from a previous model

Process improvement

In all three cases, it is the understanding of the particular implementation of processes, based mostly 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 management:

Acceleration of research (limitation of time spent and number of interviews) to build a representation of present processes

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

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

Process Mining will not be dedicated to a particular sector of activity: the approach will be able to bring value wherever processes are applied and studied. Within a company, several features could also be interested within 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 professional vision

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

Audit or inner control: faster analysis and the possibility of counting on the exhaustiveness of cases fairly than on a sample

Key success factors

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

To determine from the outset the added value objective: price reduction, improvement of the consumer/customer experience….

To define a well-defined research scope in terms of process

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

To ensure the quality of the data on which the research is based. To do this, it is essential to collaborate with the IT consultants of the systems used as well as the enterprise specialists 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 finish in itself however ought to serve as a factual starting level for additional process studies. Reintroducing a human side, for example by using a Design Thinking approach, makes it potential to deepen the results obtained thanks to Process Mining by taking the tip users into account.

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