In a corporate context, many enterprise processes are partially or even completely 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 adequate:
Is documenting a vision of the goal process sufficient for the process to be carried out in apply?
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 parts into account.
A primary definition
Process Mining is an analytical approach that goals to build an exhaustive and goal vision of processes primarily based on factual data.
Thus, Process Mining is a high worth-added approach when it involves 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 quickly 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 relatedities and makes it potential to trace the trail of an “object” via completely different stages at completely different instances 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 not less than include:
Object: an instance that will be adopted all through the process, with a unique identifier. The selection of this object influences the scope of the studied process
Activity: a step within the studied process. The choice of activities influences the granularity of the process
Date: determines the order of activities and timing
In addition, it could also be fascinating to gather additional data depending on the process, for example: provider, type of product, location, individual/management, channel, worth…. These will allow additional investigation.
Process visualization and analysis
From these data, it is feasible to visualize a representation of the perfect process and all deviations from it. This permits for early detection of potential inefficiencies in the process.
Beyond the representation of the process, one can even look on the execution occasions of every step, or look at a more limited scope with a purpose to identify where, when and why the process deviates from its superb version.
Instance with a purchasing process
For a simplified purchasing process ideally composed of 4 steps (“Record the order”, “Obtain the goods”, “Record the bill” and “Pay the invoice”), 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 current process when no model exists a priori
Verification of the right 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 mostly on goal 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 illustration of current processes
Taking into consideration more data, or even the exhaustiveness of data, in the measurements
Opportunity, once a new process is designed, to ensure efficient administration of its use and to see improvements
Process Mining is not dedicated to a particular sector of activity: the approach will be able to carry worth wherever processes are carried out and studied. Within an organization, a number of features could also 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 enrich their expert vision
CIO: vision of the use of the systems and the corresponding user paths
Audit or inner management: faster evaluation and the possibility of counting on the exhaustiveness of cases fairly than on a pattern
Key success factors
With a purpose to receive good outcomes, the launch of a Process Mining initiative requires some precautions. It may be noted that it is important:
To determine from the outset the added value objective: price reduction, improvement of the consumer/buyer experience….
To define a well-defined study scope in terms of process
To operate iteratively with brief cycle analyses, within a fixed total time limit
To make sure the quality of the data on which the examine is based. To do this, it is essential to collaborate with the IT experts of the systems used as well because 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 shouldn’t be an end in itself but should function a factual starting point for further process studies. Reintroducing a human aspect, for example by using a Design Thinking approach, makes it potential to deepen the results obtained thanks to Process Mining by taking the end users into account.
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