Process mining applies data science to discover, validate and improve workflows. By combining data mining and process analytics, organizations can mine log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas of improvement. Process mining leverages a data-driven approach to process optimization, allowing managers to remain objective in their decision-making around resource allocation for existing processes.
Information systems, such as Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) tools, provide an audit trail of processes with their respective log data. Process mining utilizes this data from IT systems to create a process model, or process graph. From here, the end-to-end process is examined, and the details of it and any variations are outlined. Specialized algorithms can also provide insight into the root causes of deviations from the norm. These algorithms and visualizations enable management to see if their processes are functioning as intended, and if they aren’t, they arm them with the information to justify and allocate the necessary resources to optimize them. They can also uncover opportunities to incorporate robotic process automation into processes, expediting any automation initiatives for a company.
Process mining focuses on different perspectives, such as control-flow, organizational, case, and time. While much of the work around process mining focuses on the sequence of activities—i.e. control-flow—the other perspectives also provide valuable information for management teams. Organizational perspectives can surface the various resources within a process, such as individual job roles or departments, and the time perspective can demonstrate bottlenecks by measuring the processing time of different events within a process.
In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the Process Mining Manifesto (PDF, 9.6 MB) (link resides outside IBM) in an effort to advance the adoption of process mining to redesign business operations. While proponents of process mining, like the IEEE, promote its adoption, Gartner notes that market factors will also play a role in its acceleration. Digital transformation efforts will prompt more investigation around processes, subsequently increasing the adoption rate of new technologies, such as artificial intelligence, task automation, and hyperautomation. The pace of these organizational changes will also require businesses to apply operational resilience to adapt as well. As a result, enterprises will increasingly lean on process mining tools to achieve their business outcomes.
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Wil van der Aalst, a Dutch computer scientist and professor, is credited with much of the academic research around process mining. Both his research and the above-mentioned manifesto describe three types of process mining, which are discovery, conformance, and enhancement.
Discovery: Process discovery uses event log data to create a process model without outside influence. Under this classification, no previous process models would exist to inform the development of a new process model. This type of process mining is the most widely adopted.
Conformance: Conformance checking confirms if the intended process model is reflected in practice. This type of process mining compares a process description to an existing process model based on its event log data, identifying any deviations from the intended model.
Enhancement: This type of process mining has also been referred to as extension, organizational mining, or performance mining. In this class of process mining, additional information is used to improve an existing process model. For example, the output of conformance checking can assist in identifying bottlenecks within a process model, allowing managers to optimize an existing process.
Process mining sits at the intersection of business process management (BPM) and data mining. While process mining and data mining both work with data, the scope of each dataset differs. Process mining specifically uses event log data to generate process models which can be used to discover, compare, or enhance a given process. The scope of data mining is much broader, and it extends to a variety of data sets. It is used to observe and predict behaviors, having applications within customer churn, fraud detection, and market basket analysis to name a few.
Process mining takes a more data-driven approach to BPM, which has historically been managed more manually. BPM generally collects data more informally through workshops and interviews, and then uses software to document that workflow as a process map. Since the data that informs these process maps is more qualitative, process mining brings a more quantitative approach to a process problem, detailing the actual process through event data.
Increasing sales isn’t the only way to generate revenue. Six sigma and lean methodologies also demonstrate how the reduction of operational costs can also increase your return-on-investment (ROI). Process mining helps businesses reduce these costs by quantifying the inefficiencies in their operational models, allowing leaders to make objective decisions about resource allocation. The discovery of these bottlenecks can not only reduce costs and expedite process improvement, but it can also drive more innovation, quality, and better customer retention. However, since process mining is still a relatively new discipline, it still has some hurdles to overcome. Some of those challenges include:
- Data Quality: Finding, merging and cleaning data is usually required to enable process mining. Data might be distributed over various data sources. It can also be incomplete or contain different labels or levels of granularity. Accounting for these differences will be important to the information that a process model yields.
- Concept drift: Sometimes processes change as they are being analyzed, resulting in concept drift.
Process mining techniques have been used to improve process flows across a wide variety of industries. Since process maps highlight the key performance indicators (KPIs) which impact performance, they have spurred businesses to reexamine their operational inefficiencies. Some use cases include:
- Education: Process mining can help identify effective course curriculums by monitoring and evaluating student performance and behaviors, such as how much time a student spends viewing class materials.
- Finance: Financial institutions have used process mining software to improve inter-organizational processes, audit accounts, increase income, and broaden its customer base.
- Public works: Process mining has been used to streamline the invoice process for public works projects, which involve various stakeholders, such as construction companies, cleaning businesses, and environmental bureaus.
- Software Development: Since engineering processes are typically disorganized, process mining can help to identify a clearly documented process. It can also help IT administrators monitor the process, allowing them to verify that the system is running as expected.
- Healthcare: Process mining provides recommendations for reducing the treatment processing time of patients.
- E-commerce: It can provide insight into buyer behaviors and provide accurate recommendations to increase sales.
- Manufacturing: Process mining can help to assign the appropriate resources depending on case—i.e. product—attributes, allowing managers to transform their business operations. They can gain insight into production times and reallocate resources, such as storage space, machines, or workers, accordingly.
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