In a corporate context, many enterprise processes are partially and even fully 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 enough 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 points of view?

Is it possible 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 first definition

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

Thus, Process Mining is a high value-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?

Whatever 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 through application logs. This stored information often has comparableities and makes it attainable to trace the trail of an «object» through completely different levels at totally different times in time.

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

Required data

In an effort to be usable, these digital footprints must at the very least embrace:

Object: an occasion that will be adopted all through 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 selection of activities influences the granularity of the process

Date: determines the order of activities and timing

In addition, it may be fascinating to collect additional data depending on the process, for instance: supplier, type of product, location, individual/management, channel, worth…. These will enable further investigation.

Process visualization and analysis

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

Past the representation of the process, one may look on the execution occasions of every step, or look at a more limited scope in order to identify the place, when and why the process deviates from its excellent version.

Example with a purchasing process

For a simplified buying process ideally composed of four 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 main use cases of Process Mining:

Discovery: building a vision of an current 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 particular implementation of processes, based on goal and exhaustive data, that makes the added value of the Process Mining approach.

In addition, this approach represents an improvement in the subject of process administration:

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

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

Opportunity, once a new process is designed, to make sure 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 bring value wherever processes are carried out and studied. Within a company, a number of features may 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 enrich their knowledgeable vision

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

Audit or inside control: faster evaluation and the possibility of counting on the exhaustiveness of cases moderately than on a pattern

Key success factors

As a way to acquire 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 person/buyer expertise….

To define a well-defined examine 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 study is based. To do this, it is essential to collaborate with the IT specialists of the systems used as well as the enterprise 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 shouldn’t be an end in itself but should function a factual starting level for further process studies. Reintroducing a human side, for example by utilizing a Design Thinking approach, makes it attainable to deepen the outcomes obtained thanks to Process Mining by taking the tip users into account.

If you have any kind of inquiries regarding where and the best ways to utilize Order Management Process Mining, you can contact us at our site.