<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=390134628368750&amp;ev=PageView&amp;noscript=1">

Process Mining for Process KPI Reporting

Apr 24, 2020 / by Teemu Lehto    |    8 min read

Key performance indicators (KPIs) are arguably the most important tools for managers to understand how their organization is operating. KPIs help measure the level of success or failure of your business, and they can be assigned for different levels of business operations: on the task-based, process-based, or organizational level.

In order to have a clear vision for your organization and make decisions based on facts and data, your management team needs to have a system of smart, effective, and realistic KPIs. However, we've seen many businesses struggle with their KPI-related tasks: perhaps it's taking too long to establish such a KPI measurement and reporting system, or people fail to understand the root causes of their problems. 

In this blog post, I will explain how you can use process mining to establish your process KPI system in a more efficient way, with much less effort compared to traditional ways of building process KPIs.


1. Background: KPI-driven PDCA cycle and process mining KPIs

2. Build KPIs faster with less effort

3. Understand the root causes of your problems

4. Prediction for open cases that are likely to fail the KPIs

5. QPR ProcessAnalyzer for Process KPI Reporting


1. KPI-driven PDCA cycle and process mining KPIs

In my last blog post (Process Mining for Process Improvement), I emphasized the benefits of using the PDCA Deming cycle in change implementation. This model can also be used to enforce a KPI-driven improvement cycle:




In the first step, you analyze your processes, identify problems and their root causes, and then define relevant KPIs based on your findings.

Next, you implement a small-scale change in your process. After this change, you review its effects on your initial problems and their root causes, so that you can evaluate the relevancy and effectiveness of your pre-defined KPIs.

Ultimately, you're able to standardize your solution in a bigger scale, and set up a solid KPI system. It's important monitor your processes continuously so that you can react to future problems in a timely manner.

From the process mining perspective, there are three sets of KPIs that play the most significant role in the success of your operational process: Happy Customer, Happy Flow and Happy Automation. Altogether, they bring about the Process Excellence in your organization.


3 steps to process excellence

Check out the earlier blog post (Best Practices for Deploying Process Mining in Large Organization) for more details about these three sets of KPIs.


2. Build KPIs faster with less effort

With traditional Business Intelligence, KPIs are built manually by taking into consideration various factors, such as the related data, the intended outcome, the alternative measures, and even the opinions of stakeholders. After you finish building this first KPI with the traditional methods, you then take the original data, and move on to building the second KPI, then the third one, and so on. Building each of these KPIs will be a project of its own.

The amount of work needed to finalize a single KPI this way can take something between one day and 100 days, depending on the scale and complexity of that process.



From a process mining perspective, our experts first build the process mining model together with you. This is the starting point of any process mining project - it can't happen without a process mining model. Once this step is finished, you right away have access to more than 1000 standard KPIs based on the data from your own system.

Additionally, with our process mining software, QPR ProcessAnalyzer, you can define the custom attributes that you want your KPIs to have, then include them in QPR ProcessAnalyzer to build a custom KPI. Consequently, you spend much less time and effort compared to the traditional business intelligence approach.



3. Understand the root causes of your problems

After you successfully build a KPI with the traditional business intelligence approach, you can, in theory, set up a manual root cause analysis (RCA) for this KPI. However, this will require not only a huge amount of effort, but also access to many different types of extra data that correlates with this particular KPI.

In the process mining approach, after you set up your process mining model, you already have access to more than 1000 standard KPIs. This means while you create the process mining model, the software calculates and gives you a considerable amount of extra information from your raw data. For example, when you look at one KPI from these auto-generated KPIs, you now have 999 other KPIs as your potential root causes: you can see which other KPIs correlate with it.

Similarly, if you review your custom KPIs with process mining, you already have more than 1000 other standard KPIs as a source of information for your root cause analysis. From this point, you can go through these potential root causes one by one, or start by understanding this KPI's cause and effect. Process mining allows you to drill down these potential root causes to understand what is actually happening in your processes.


4. Prediction for open cases that are likely to fail the KPIs



Again, if you build your KPIs manually in a traditional business intelligence approach, you will need much more time to build the predictive capabilities for one KPI. The reason is you need even more information from your operation and the help of a data scientist to do this manually.

Process mining, on the other hand, gives you access to data from all the cases in your business process, each of which involves certain activities and has different case attributes. All of these data can be used for machine learning based predictions on a case level. You won't need a data scientist to help you build these prediction reports if you use QPR ProcessAnalyzer. By running this prediction feature, you are commanding the software to take into account all the old cases in your system. You only need to configure the tool to check what attributes and activities are correlating in a meaningful way with the result to increase the accuracy of the predictions. 


5. QPR ProcessAnalyzer for Process KPI Reporting

Here are some KPI Reporting features highlighted with screenshots. Clicking the pictures gives an easy access to the corresponding parts of the webinar recording.


Process KPIs included in the flowchart 



Process KPI: Long Duration shown with Root Causes

Blog - Process KPI - Long Duration shown with Root Causes


Process KPI: OTIF - On Time In Full, Delivery fails ie. Confirmed delivery date is before shipment sent. Shown with Root Causes

Blog - Process KPI - OTIF fails


Process KPI: Happy Customer, shown with Root Causes 

Blog - Process KPI - Happy Customer


Process KPI: Operations Dashboard

Blog - Process KPI - Operations Dashboard-1

It’s a good time to take a look at Process Mining if your company hasn’t already. The capabilities and usability of Process Mining software are improving rapidly, and the market is quickly becoming mature, although there’s still much work to be done. If you think your company is ready to step it up with the future of as-is process modeling and process efficiency maximization, the fastest way to get things moving is to follow these steps:

Topics: Process Mining, QPR ProcessAnalyzer, Best Practices

Teemu Lehto
Written by Teemu Lehto

Process Mining evangelist active in marketing, sales, consulting, product development and research. Teemu has been involved in 200+ end customer process mining project from order-to-cash, purchase-to-pay, plant maintenance, auditing and service. Teemu is also an active speaker delivering the process mining message as well as writer for several process mining and machine learning scientific articles. Book a meeting with Teemu using the link: https://outlook.office365.com/owa/calendar/TeemuLehtoQPR@QPR.onmicrosoft.com/bookings/

Join an upcoming webinar: 

27.10.2020  Process Mining for Service Management

3.11.2020 Process Mining for Process Improvement


Guides about popular Process Mining use cases:

4 Steps of RPA with Process Mining

Process KPI Reporting

Featured Success Story from EY UK:

Internal Audit and Risk Management