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 traditional ways of studying processes are still ample:
Is documenting a vision of the target process ample for the process to be applied in apply?
When a deviation from a model is perceived, is it optimum to seek consensus in a bunch from subjective factors of view?
Is it potential to measure the actual execution speed of the process from start to finish?
Process Mining provides a new approach to take these parts into account.
A first definition
Process Mining is an analytical approach that aims to build an exhaustive and objective vision of processes primarily 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 identifying deviations from the best 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 by way of application logs. This stored information typically has similarities and makes it doable to hint the path of an “object” via totally different stages at completely 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.
Required data
In order to be usable, these digital footprints should at the least embody:
Object: an occasion that will be followed all through the process, with a unique identifier. The selection of this object influences the scope of the studied process
Activity: a step in 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 collect additional data relying on the process, for instance: provider, type of product, location, particular person/administration, channel, value…. These will allow further investigation.
Process visualization and analysis
From these data, it is possible to visualize a representation of the perfect process and all deviations from it. This allows for early detection of potential inefficiencies within the process.
Beyond the representation of the process, one can even look on the execution instances of each step, or look at a more limited scope so as to identify where, when and why the process deviates from its superb version.
Example with a buying process
For a simplified buying process ideally composed of 4 steps (“Record the order”, “Obtain the products”, “Record the invoice” and “Pay the bill”), the process adopted 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 proper implementation and analysis 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 goal and exhaustive data, that makes the added worth of the Process Mining approach.
In addition, this approach represents an improvement within the field of process administration:
Acceleration of studies (limitation of time spent and number of interviews) to build a representation of existing processes
Taking under 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 administration 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 carry value wherever processes are applied and studied. Within a company, several capabilities may be interested in the approach:
Operational excellence teams: complementing the strategies already used (Lean, Six Sigma, etc.)
Data Scientists: visual representations of data to generate new insights
Process managers: factual analyses to enrich their professional vision
CIO: vision of using the systems and the corresponding user paths
Audit or inner management: faster analysis and the possibility of relying on the exhaustiveness of cases somewhat than on a sample
Key success factors
With a view to obtain good outcomes, the launch of a Process Mining initiative requires some precautions. It may be noted that it is necessary:
To establish from the outset the added worth goal: price reduction, improvement of the consumer/customer expertise….
To define a well-defined research scope when it comes to process
To operate iteratively with short cycle analyses, within a fixed total time limit
To ensure the quality of the data on which the examine is based. To do this, it is essential to collaborate with the IT consultants of the systems used as well as the business 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 finish in itself but should function a factual starting point for further process studies. Reintroducing a human side, for instance through the use of a Design Thinking approach, makes it doable to deepen the outcomes obtained thanks to Process Mining by taking the end customers into account.
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