In a corporate context, many enterprise processes are partially or 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 or not traditional ways of finding out processes are still sufficient:
Is documenting a vision of the goal process ample for the process to be applied in observe?
When a deviation from a model is perceived, is it optimal to seek consensus in a bunch 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 parts into account.
A first definition
Process Mining is an analytical approach that goals to build an exhaustive and objective vision of processes based on factual data.
Thus, Process Mining is a high worth-added approach when it comes to building a viewpoint on the actual implementation of a process and figuring out deviations from the best process, bottlenecks and potential process optimizations.
How does it work?
Whatever the nature of the process , as quickly 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 comparableities and makes it potential to trace the trail of an “object” through completely different stages at different occasions in time.
Process Mining relies on instruments that use these digital footprints to reconstruct, visualize and analyze processes, thus providing transparency and objectivity towards the real process.
To be able to be usable, these digital footprints must no less than embrace:
Object: an occasion that will be adopted all through the process, with a novel 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 may be fascinating to collect additional data relying on the process, for example: provider, type of product, location, person/administration, channel, value…. These will enable additional investigation.
Process visualization and evaluation
From these data, it is feasible to visualize a illustration of the best process and all deviations from it. This allows for early detection of potential inefficiencies within the process.
Past the illustration of the process, one may also look at the execution times of each step, or look at a more limited scope as a way to establish the place, when and why the process deviates from its supreme version.
Instance with a buying process
For a simplified buying process ideally composed of four steps (“Record the order”, “Receive the goods”, “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 main 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 analysis of deviations from a previous model
In all three cases, it is the understanding of the particular implementation of processes, primarily 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 representation of present processes
Taking into consideration more data, and 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 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 an organization, 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 complement their professional vision
CIO: vision of the usage of the systems and the corresponding person paths
Audit or inner control: faster analysis and the possibility of counting on the exhaustiveness of cases relatively than on a sample
Key success factors
With a view to obtain good results, the launch of a Process Mining initiative requires some precautions. It may be noted that it is important:
To establish from the outset the added value goal: price reduction, improvement of the person/buyer experience….
To define a well-defined research scope in terms of 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 study is based. To do this, it is essential to collaborate with the IT specialists of the systems used as well as the enterprise consultants 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 finish in itself but ought to function a factual starting level for additional process studies. Reintroducing a human side, for example through the use of a Design Thinking approach, makes it attainable to deepen the results obtained thanks to Process Mining by taking the tip users into account.
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