Types and Applications of Process Mining

An audit trail of processes is provided together with the corresponding log data by information systems like customer relationship management (CRM) or enterprise resource planning (ERP) applications. This data from IT systems are used in process mining to build a process model or process graph. The entire process is then analyzed, with its specifics and any changes being described. 

 

Additionally, specialized algorithms can provide light on the main reasons why deviations from the average occur. Management may use these algorithms and visualizations to determine whether their processes are operating as intended, and if not, they can use the knowledge to support and allocate the extra resources needed to improve them.

 

They can also find ways to incorporate robotic process automation into existing workflows, accelerating any automation projects a firm is working on. The Process Mining Manifesto was issued in 2011 by the Institute of Electrical and Electronics Engineers (IEEE) in an effort to promote the use of process mining to improve corporate operations. Although the IEEE and other supporters of process mining encourage its adoption, market conditions will also contribute to its acceleration. 

 

Process-related research will increase as a result of digital transformation initiatives, which will also increase the rate at which new technologies like artificial intelligence, task automation, and hyper-automation are used. Businesses will need to use operational resilience to react given how quickly these organizational changes are occurring. In order to fulfill their business goals, companies will rely more and more on process mining tools.

 

Also read | Intelligent Process Automation (IPA)

 

What is Process Mining?

 

Process mining uses data science to find, verify, and enhance workflows. Organizations may use log data from their information systems to assess the performance of their processes, exposing bottlenecks and other opportunities for improvement, by combining data mining with process analytics. 

 

Process mining uses a data-driven strategy for process optimization, enabling managers to make decisions about the resource allocation for current processes with objectivity. It places a strong emphasis on several viewpoints, including control flow, organization, case, and time. 

 

While much of the work on process mining concentrates on the order of events or control flow, the other viewpoints also offer management teams useful information. Time perspectives may show bottlenecks by monitoring the processing times of various events within a process, while organizational perspectives can highlight the various resources inside a process, such as specific job positions or departments.

 

Business process management (BPM), data mining, and process mining are all related concepts. Despite the fact that both process mining and data mining utilize data, each dataset's size varies. To create process models that may be used to find, contrast, or improve a certain process, process mining especially leverages event log data. 

 

Data mining encompasses a range of different data sources and has a much larger application. Applications include customer churn analysis, fraud detection, and market basket analysis, to mention a few. It is used to monitor and forecast behaviors.

 

In contrast to BPM, which has traditionally been managed more manually, process mining is more data-driven. BPM often employs workshops and interviews to gather data in a more informal manner before using software to map out the workflow. Process mining applies a more quantitative approach to a process problem by describing the actual process through event data since the data that supports these process maps is more qualitative.

 

Also Read | How Business Analytics impacts Decision Making in Businesses

 

Types of Process Mining:

 

The different types of process mining are listed below:

 

1. Discovery: 

 

Process discovery employs event log data to autonomously build a process model. According to this categorization, no existing process models would be available to guide the creation of a brand-new process model. The most common form of process mining is this one.

 

2. Conformity: 

 

Conformity testing determines if the planned process model is represented in actual behavior. This kind of process mining analyses event log data to match a process description to an existing process model and find any differences from the intended model.

 

3. Enhancement:

 

Extension, organizational mining, and performance mining are some names for this kind of process mining. A process model that already exists is enhanced in this genre of process mining through the introduction of new data. 

 

For instance, the results of compliance testing might help managers discover bottlenecks in a process model and improve an existing process by pointing out these bottlenecks.

 

Also Read | How is RPA used in Big Data?

 

What are process mining algorithms?

 

In essence, how the process model is deduced from unprocessed event data is determined by the process mining algorithm. In order to find trends, patterns, and aggregate metrics in the event logs that an information system has captured, an algorithm is utilized to correlate the data. 

 

After that, the original enterprise process models and the mined models are compared to see whether there is compliance or to find more streamlined and effective business process models.

 

Process mining algorithms might be difficult to select for a particular organization or application area since there isn't a single, widely-accepted benchmark standard from which to assess and compare them. For instance, a good algorithm is able to quickly correlate events from several heterogeneous data sources, such as events from various separate systems.

 

Enterprises will benefit if they can choose a process mining algorithm that generates mined models that are comparable to or superior to the original models, albeit different process mining algorithms have varying features.

 

Difference between business intelligence and process mining:

 

The quality and depth of the analysis are different in process mining from typical business intelligence. As a result, traditional BI concentrates on the recurring computation of aggregated metrics for reporting and presupposes knowledge of underlying operations. 

 

However, while these business intelligence dashboards can highlight any possible issues in areas that are already recognized, they do nothing to offer visibility into areas that are currently unknown.

 

On the other hand, process mining is predicated on the idea that things don't always go as expected and that issues appear where they can't be foreseen in advance. So, ensuring operational excellence requires a greater understanding of how the processes are actually working.

 

Operational teams may uncover bottlenecks, aberrations, and redundancies that slow down operations more rapidly by utilizing process mining techniques in novel and creative ways. Once these components have been located, it is possible to remove them, which speeds up the procedure and frequently reduces it in half.

 

Also Read | Introduction to Business Intelligence

 

Importance of Process Mining:

 

There are other ways to make money without increasing sales. Lean and Six Sigma approaches also show how lowering operational expenses may boost your return on investment (ROI). Process mining enables executives to make unbiased judgments regarding resource allocation by quantifying the inefficiencies in their operational models. This helps firms lower these expenses. 

 

The identification of these bottlenecks has the potential to increase innovation, enhance quality, and improve customer retention in addition to lowering costs and hastening process improvement. Process mining has certain challenges to overcome, though, as it is still a young subject. These difficulties include:

 

• Data Quality: In order to allow process mining, data must typically be located, merged, and cleaned. Data may be spread among a number of sources. Additionally, it could be lacking or have various labels or granularity levels. The information that a process model produces will benefit from accounting for these discrepancies.

 

• Concept drift: When processes are being evaluated, they might occasionally shift, leading to concept drift.

 

Process mining features advanced algorithms to make present business processes transparent, enabling firms to simplify and enhance them. In the end, it highlights the possibilities in your key business processes that will have the most impact on your consumers and your bottom line. It swiftly unearths useful insights that can boost productivity.

 

Process mining has access to real-time event data, which gives it a major edge over more conventional "as-is" analysis. Process mining also considers historical data, with the capacity to carefully examine a number of event logs in order to gain a thorough understanding of what's happening. This is a significant improvement over the previous heavy-duty data infrastructure, which was slow and labor-intensive and used to perform the same calculations.

 

Process mining can expose what is occurring right now instead of depending on conventional data infrastructure to evaluate transactions. It does this by utilizing enormous volumes of event data from all your systems to:

 

• Find out how people, organizations, and machines actually behave, then compare it to models that already exist.

• To demonstrate how reality differs from perceptions, opinions, and beliefs, correlate millions of occurrences.

• Lay the groundwork for the ongoing development of improved processes.

 

Above all, process mining enables you to comprehend the condition of your systems and processes at the moment while providing a quicker, more precise method to spot any deviations and aberrations, then remedy the situation.

 

Applications of Process Mining:

 

Financial services, telecommunications, healthcare, and retail are just a few examples of industries where process mining can be used for business process management and process improvement. These sectors have a wealth of data that can be used as a starting point, and process deviations from their intended behavior can have expensive repercussions.

 

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Applications of Process Mining

 

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1. Financial Services: 

 

Because of the rise in transaction volume and the digitization of more industries, aberrant activity is harder to detect using manual methods. Companies in the financial services sector have the chance to continually and thoroughly identify issues within high-volume processes thanks to process mining, which is a solution to the increased regulatory and audit requirements.

 

2. Telecommunications: 

 

As subscriber quantities increase and activations become more and more automated, there is a greater danger of unsuccessful activations. When telecom companies get more orders, process mining gives them the chance to identify pricey issues and client blowback in their Order-to-Activation processes.

 

3. Healthcare: 

 

The risks associated with preserving population health and achieving individual patient journey objectives rise as data about patient experiences and results keep growing. Process mining supports the delivery of effective and high-quality end-to-end patient journeys for healthcare organizations dealing with the exponential growth of data, from before a first doctor appointment through treatment regimens to closed treatment cases.

 

4. Retail:

 

Due to technology or process problems, retail businesses have seen expensive consumer fallout from complicated e-commerce operations. Process mining assists merchants in ensuring that consumers can complete transactions efficiently and without issues despite rising transaction volumes.

 

5. Digital Transformation:

 

Process mining is frequently used in larger-scale digital transformation initiatives because it can give you the precise insights needed for process improvement, allowing systems to run more quickly, smoothly, and efficiently, as well as objective data-driven insights into the causes of delays and inefficiencies within business processes. 

 

As a result, process mining may assist in identifying the digital transformation opportunities with the greatest potential for value addition and determining whether or not transformation activities have really produced the desired results. To optimize returns on investments in projects for digital transformation, process mining becomes a crucial instrument.

 

Conclusion:

 

Process mining is a tool that businesses may rely on to conduct audits that are more accurate and efficient while avoiding conjecture and subjective conclusions. This results in less time being lost on doubt and subsequent retesting. 

 

You should be careful when choosing a process mining solution that best satisfies all of your company objectives since an organization's capacity to analyze, monitor, and improve business processes has a direct impact on its revenue and customer happiness.