Processes are virtually everywhere, i.e., almost everything we do has some notion of activities, which we execute (repetitively) to achieve some goal. The vast majority of companies active in virtually any domain executes a process. Whether the core business of a company is to deliver a product, e.g., manufacture a car, cooking a delicious pizza, etc., or, providing a service, e.g., providing you with a mortgage to buy your dream house, paying back your insurance claim, etc., for efficient delivery of your product/service, processes are executed. Clearly, from a business perspective, understanding how processes are executed within a company is a vital first step in order to be able to get grip of the process, and, eventually, improve the process. In general, the field of Business Process Management studies methods, tools and techniques in order to achieve such aforementioned understanding of processes running at a company.
Process mining represents a collection of tools, methods, techniques, algorithms, etc., that allows us to achieve a better understanding of the execution of a process, by means of analyzing the operational execution data that is generated during the execution of the process.
Aim of this analysis is to show how powerful process mining techniques are in identifying business process issues and providing the right insights to address them properly. In greater detail, I applied various techniques and algorithms available in Python PM4PY library to a dataset of process logs.
PM4PY is a great open source process mining platform written in Python and designed to be used in both accademia and industry. It allows to easily analyze business processes using event logs data and let users to discover process patterns and process issues that should be addressed.
Find more here: 👉 https://pm4py.fit.fraunhofer.de/
Data were provided by Kaggle and free for use: 👉 https://www.kaggle.com/datasets/asjad99/it-incident-management-process.
Using PM4PY I was able to identify business process issues (such as bottlenecks, people working less than other department colleagues,...) providing useful insights for making the process far more efficient. I truly believe we'll see more and more process mining in the near future.