Process Mining Use Cases¶

Process Improvement Teams¶

There are many different terms used for process improvement teams in organizations: Process Excellence, Operational Excellence, Process Performance Management, etc. These teams often use Lean Six Sigma methods in their improvement initiatives and, as a central team, help different business units in the organization. Process mining fits very well into their toolbox and allows them to analyze the true processes based on data, rather than through manual inspections and interviews.

Process mining itself is agnostic to the improvement method that you use. This means that it does not matter whether your organization uses Business Process Management (BPM), Theory of Constraints, Lean, Six Sigma, or Lean Six Sigma. Process mining does not replace these methods (see also New Improvement Methodology). Instead, the business analysts will use their improvement framework to interpret the process mining results, drive the change, and verify whether the outcome was effective.

The benefit of using process mining in process improvement projects is that the actual processes can be analyzed much faster, and much deeper, than they could be in any manual way. This does not mean that the workshops with process managers and other stakeholders in the business unit go away: Instead, you will start the conversation with them on another level. You can show them the process and say “This is what we are seeing. Do you know why this is happening?” (instead of wasting hours of their time by letting them explain to you how the process works).

Further reading:

• Frank Geffen & Rudi Niks, Accelerate DMAIC using process mining
• Anne Rozinat, How to reduce waste with process mining

Data Science Teams¶

Many organizations have started to build data science teams, because they have recognized the value of increasing amounts of data and they want to be able to make use of it. Data scientists are typically well-versed in all kinds of technologies. They know how to work with SQL, NoSQL, ETL tools, statistics, scripting languages such as Python, data mining tools, and R. And they know that 80% of the work consists of the processing and cleaning of data.

Data scientists are starting to adopt process mining, because it fills a gap that is not covered by existing data-mining, statistics and visualization tools: It can discover the actual end-to-end processes. Process mining also allows data scientists to work much faster. Even if you could write an SQL query that answers your particular process question, the process mining tool shows you the full process right after importing and allows you to directly filter the data without any programming.

Furthermore, data science teams do not analyze data for themselves, but to solve problems and issues for the business. Process mining helps them to communicate their analysis results back to the business in a meaningful way. Charts and statistics are often too abstract when summarizing a process. So, being able to provide a visual representation of the process to the process manager makes your explanation much more accessible to them.

Further reading:

• Anne Rozinat & Christian W. Günther, Why process mining is ideal for data scientists
• Anne Rozinat, Change in perspective with process mining

Process Managers¶

Process managers are responsible for one particular process in the organization. The methods they use are often similar to the central process improvement teams (see above), but instead of working with different departments at different times they focus on their own processes and repeatedly analyze them for continuous improvement.

When a process manager adopts process mining, they have the advantage that they have all the domain knowledge available to interpret the data and the process correctly. This is a great advantage, because process mining does not only require expertise in how to do the actual process mining analysis, but the domain knowledge to interpret what you are seeing is absolutely crucial. At the same time, they typically need some training in a process improvement method (like Lean).

Process managers focus on operational questions and process mining brings them an eye-opening transparency about what is actually going on in their process. Once they have completed a process mining analysis, they can easily repeat it to see whether the improvements were as effective as they have hoped.

Further reading:

• Joris Keizers, Leveraging human process knowledge via process mining (the lead time of their core production process was cut in half)
• Donna Stewart talks about process mining with Marcello La Rosa in this video (the throughput time of handling simple insurance claims was brought from several weeks to hours)

Auditors¶

The role of internal audit departments is to help organizations ensure effectiveness and efficiency of operations, reliability of financial reporting, and compliance with laws and regulations in an independent and objective manner. External auditors provide assurance from outside the organization.

Both groups can benefit from process mining in many ways. Clearly, processes are not all an auditor looks at. For example, an IT auditor also looks at which system controls are in place to prevent fraud. However, when they do look at processes they typically do it in a very manual way (by looking at the process documentation, interviewing people, and inspecting samples). This is time-consuming and does not guarantee that the actual process problems will be detected.

When auditors use process mining they focus on compliance questions (like segregation of duties and process deviations). The advantage of using process mining is that they can be much faster. Furthermore, they can analyze the full process (not just samples) and, therefore, achieve a higher assurance. They can focus on the deviations (by quickly seeing what goes right) and better identify the true risks for the organization. Finally, the visual representation helps them as well, because in the end they will need to communicate their findings in an audit report.

Further reading:

• Mieke Jans et. al, A field study on the use of process mining as an analytical procedure in auditing
• Youri Soons, Experiences of CAS with process mining

IT Departments¶

If you look at process mining from the perspective of an IT department, you are mostly concerned about how well the IT systems (or apps, or websites) are working. [2] There can be many different reasons to try to understand how IT systems are actually used. For example, you might want to replace a legacy system. Or you might want to scale back unnecessary customizing to make upgrades easier and save maintenance costs.

More recently, organizations have started to analyze the so-called customer journeys by combining click-stream data from their apps and websites with data from other customer interaction channels. The goal to improve the customer experience is typically at the center of these customer journey process mining analyses.

Customer journey processes are often more complex than, for example, administrative processes. Therefore, it is really important to formulate concrete questions and filter down the data to the subset that relates to your question (see this article for 9 simplification strategies). However, if done right, customer journey analyses can contribute greatly to not just improving the usability of websites and apps, but also to shift the perspective from “How are we doing things?” to “How does the customer experience our service?” in any process improvement project.

Further reading:

• John Müller, Interview before Process Mining Camp 2014
• Anne Rozinat shows three customer journey examples in 8 min, starting from here (Video)

Consultants¶

Process mining fits into many types of consultancy projects. Whether you are helping your client to introduce a new IT system (transformation projects), build an operational dashboard, or help them to work more efficiency, in all of these projects you need to understand what the ‘As is’ process looks like.

The most common use case of process mining for consultants is in process improvement projects. As such, the use case is very similar to the one of Process Improvement Teams (see above). But instead of an internal team working with a business unit in the organization, you are coming in as an expert from the outside, bringing with you a fresh perspective and your experience of working with different clients.

Consultants can specialize in many different areas by, for example, focusing on particular industries or IT systems. Furthermore, if you build up your process mining skills, you can help clients to try out or adopt process mining, when they do not have these skills themselves yet.

Further reading:

• Anne Rozinat, 7 Reasons for consultants to do process mining
• Anne Rozinat, Process mining does not remove jobs — it creates new ones

Possible Outcomes¶

Another way to understand the different process mining use cases is to look at the possible outcomes of a process mining analysis.

Before we look at the different outcomes, let’s take a step back and clarify what process mining is. When we talk about process mining as an analysis tool, do we mean the software tool or the overall approach? The answer is that you need a process mining software like Disco for your process mining analysis but the process mining tool is just one ingredient. Process mining is not an IT project, where you simply buy a software and are done with it. Instead, process mining is a discipline and you need to build up skills and experience with applying this new tool in your organization.

As shown in Figure 2, process mining allows you to analyze very complex processes. This is important, because processes are typically much more complex than people realize (see also Why Do You Need Process Mining?). Furthermore, you don’t need to know what the process looks like (just identify the three parameters case ID, activity name, and timestamp as explained in the Data Requirements) and you can even look at the same process from different perspectives. This left side in Figure 2 is what the process mining tool enables you to do.

However, to deal with this complexity, to check for data quality problems, to create the different views, to analyze the process and interpret the results, and to successfully embed process mining in your company you need a human analyst with the skills and experience to do it well. This is the methodology part of process mining (see right side in Figure 2). This process mining handbook will help you to develop the process mining skills that you need and you will build up experience over time by starting to use process mining on your own data.

Figure 2: Process mining is an analysis tool that allows a skilled, human analyst to understand very complex and unknown processes.

So, what exactly can be the outcome if you as a smart, skilled process analyst are getting to work with a process mining tool?

On a high level, there are four main outcomes of a process mining analysis (see also Figure 3). For any process mining project, a combination of these outcomes can apply.

1. Answer

Sometimes, the outcome is just an answer. For example, imagine you are the manager of a process and have received complaints that this process is taking too long. There is an internal Service Level Agreement (SLA) and you want to know whether the complaints are justified (and if so, how often it happens that the SLA is not met). Getting an answer to this question is the primary goal of the process mining analysis.

Another example would be a data science team that supports a customer journey project, where the customer experience is completely re-designed. To make sure that the new system supports the customers in the best way, the data scientists have been asked to analyze what the most common interaction scenarios are.

Finally, think of an auditor who assesses the compliance of a process. The audit report with the summary of their findings will be the main outcome of the process mining analysis.

2. Process change

In most situations, the outcome will be a process change. For example, a particular process step may be automated. There might be organizational changes to address the high workload and shortage of resources in a certain group. An update to the FAQ or website of the company could be made to prevent unnecessary customer calls. Based on the assessment of the audit team, a new control could be implemented in the IT system to reduce the risk of fraud. Or based on the analysis of an outsourced service process at an electronics manufacturer, the contracts with the outsourcing partners may be renegotiated in the next year.

Typically, the analysis will be repeated after some time to see whether the change was as effective as one had hoped. It is easy to repeat a process mining analysis with fresh data to support continuous improvement initiatives. The outcome of the follow-up analysis can then again be just an answer or result into more process changes.

3. Monitoring

Sometimes, you may also discover a new KPI that was not known before. For example, imagine that you are analyzing a payment process where the company can get 2% discount from their suppliers if they pay within 10 days. You realize that the late posting of the invoices is the main problem that these discounts are missed. If they are not posted within 3 days, there is almost no chance to get the payment through on time. And you want to monitor this new KPI in an automated way.

Like the process change, the KPI monitoring will happen outside of the process mining tool (see also why process mining is not a BI or Reporting Tool). In contrast to process mining, a monitoring tool has a fixed view on the data. It is typically easy to add a new KPI to your existing dashboard or Business Intelligence (BI) system once you know exactly what you want to measure. Process mining will help you to get there by helping you to understand the process and the data. For example, it will help you to know where exactly the measure points for the new KPI need to be placed.

4. Optimization

Finally, sometimes further, advanced model-based analyses may be needed after the process mining analysis has been completed. For example, let’s say you analyze the fall-out from a sales process, which means that you are looking at those customers who were interested in your products but for whichever reason never completed the ordering process (their revenue has been lost). You want to follow up with them and be pro-active in offering help before it is too late. But you only want to follow-up with the customers who are most likely to buy.

This would be a scenario, where your data science team sets up and trains a prediction algorithm. It will be a custom application built upon one of the state-of-the-art data mining or machine learning frameworks. In contrast to process mining, which is a generic process analysis tool, such a prediction analysis is targeted at one very specific problem (predicting which customers you should call). The prediction algorithm needs to be set up by a data scientist who knows the domain problem and who knows what they are doing. However, process mining can help them to understand the process and the possible process parameters for their model.

Figure 3: Each process mining project can lead to (a mix of) different outcomes.

There are many other scenarios where process miners will perform further analyses in other, complementary tools. For example, a Lean Six Sigma practitioner will want to perform additional statistical analyses in Minitab, data scientists might use data mining tools to discover correlations between the process variants and other attributes in the data, process improvement experts might want to run alternative what-if scenarios in a simulation software, and auditors might take some of the findings from their explorative analysis in Disco to their regular audit tools to include them in the standard check procedures.

All of these tools are specializing in different areas and can be used together. Process mining provides important input for these follow-up analyses by providing a process perspective on the data.

To further clarify how process mining is different from these other, complementary tools refer to the next section What Process Mining Is Not.

Footnotes