Adopting Process Mining techniques and solutions in Lean Six Sigma initiatives


The positive benefits of quality management have convinced many companies to implement quality management systems. One of the quality management frameworks that has seen a significant increase in use in recent decades is Six Sigma. Six Sigma uses a number of quality principles and techniques to minimise the number of defects in a process and, consequently, the causes of variability in processes. However, the current challenges companies face, such as increasing process and supply chain complexity and high volumes of unstructured data, make adopting traditional Six Sigma tools problematic.

Process mining, thanks to the evolution and availability of AI-based process discovery capabilities, is an ideal candidate for systematically supporting Six Sigma-based process improvement initiatives, as it incorporates a range of techniques that enable an organisation to gain insight into its processes. 

Computer systems and devices record and store large amounts of data. In particular, the logs provide data on the operational steps performed. Process mining is a discipline that offers a range of techniques for gaining insights based on recorded events and for conducting further analyses to support process improvements. In practice, it operates between computational intelligence and data mining on the one hand, and process modelling and analysis on the other, as an enabling technology for process-oriented quality management methods and frameworks. The starting point is therefore the event log. An information system monitors real-world business processes and records events that are stored in event logs. An event refers to an activity (a defined step in a process) that took place at a particular time and is related to a particular instance (process instance). Process Mining, by analysing the data, makes it possible to systematically identify the real functioning of the process, highlighting for each individual instance the main flow, any deviations from a predefined standard flow, any recycling and the execution times for each individual activity.

Additional information, such as the resource (e.g., person or device) performing the activity, or the cost incurred to execute a single instance of the activity, can be used to enrich the event log and thus extend the Mining & Analysis capability.

The most popular process mining platforms. UiPath, Celonis, MyInvenio, to name but a few, tend to support the entire DMAIC (Define, Measure, Analyze, Improve, Control) cycle, making it possible to identify areas for intervention, simulate the execution of the optimised process to assess its real impact, so as to concentrate efforts and investments on the interventions that will produce the greatest benefits in practice.

In conclusion, Six Sigma focuses on improving business processes by statistically quantifying changes in process performance, but the data used for such analyses are usually collected manually, which makes Six Sigma an expensive and time-consuming endeavour. In recent years, process mining has proven to be a useful technique for conducting process analysis in a potentially time-efficient manner, so it can serve as an important support technology for process improvement initiatives based on Six Sigma.

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