Robotic Process Automation (RPA) using QPR ProcessAnalyzer 2020.3

QPR ProcessAnalyzer 2020.3 provides predefined robotic process automation (RPA) charts, statistical calculations and advanced security hardening for process mining. Read more to learn about these new functionalities in order to improve your organization's process mining journey! 

Robotic Process Automation (RPA) Charts

RPA Charts speed up any process mining initiative by adding seven predefined charts that can be used as ad-hoc analysis charts or dashboard components in your own RPA solution. All charts are delivered as ChartView presets and they can be further customized by adding your own dimensions, selections and what-if scenario capabilities.


What is your end-to-end automation rate today and is it improving?

Automation Rate Trend shows the progress and results of your RPA and automation efforts as a monthly trend. It is easy to see how automation rate is increasing as a result of RPA activities. However, if the automation is not working fine, then the amount of other manual tasks within the process starts to increase. This is often caused by rework, manual work for fixing events performed by bots and manual work based on customer complaints.

With this report,  you get a quick overview of your automation progress within the end-to-end process. QPR ProcessAnalyzer provides visibility to your end-to-end automation rate over time.

Automation Opportunity Scout

Which tasks should be automated next?

Automation Opportunity Scout is an easy-to-use bubble chart correlating to three main characteristics of event types:

  1. Total amount of events
  2. Amount of manual events
  3. Ratio for manual work


You should look for:

  1. Big bubbles, which indicate a lot of manual events.
  2. Position in the vertical axis - event types in the 100% line are completely manual, event types in between are partially manual/automated
  3. Position in the horizontal axis - high volume event types are on the right. 


How well you know who does the work in your organization?

Let process mining reveal the details for manual users, bots and system automation for each event type.

Automation per Event Types shows the activities taking place during the end-to-end process execution by the automation property recorded in each event.

  • Automatic events shown with light blue color are events that ERP and other IT systems generate automatically based on execution rules without a manual user activity.
  • The green Bot events show those events that have been automated using Robotic Process Automation (RPA) tools.
  • Dark blue colored Manual events are performed by actual users and these are potential areas for further process automation efforts. In the automation type is not known, then red N/A value is shown. 



How many times you need to touch the case to score a goal? 

Manual Activities per Case is an easy-to-use way of showing the amount of manual activities needed to complete a case. Touchless cases are cases that complete automatically, without any manual activities. Other cases may need one, two, three, or even more manual user activities in order to complete.  


How much faster is the automated process?

Case Duration vs. Manual Activities shows the effect of manual activities on the average case duration. As you see from this picture, the process tends to be faster when the amount of manual activities is smaller.

The size of each bubble is defined by the amount of cases that have the corresponding amount of manual activities - the bigger the bubble, the more cases there are with that amount of activities.

This chart can be used to guide your RPA project by benchmarking successful and fast automated cases in left-low corner with problematic, long-lasting cases in the right-top corner.







Which manual events cause longest lead times?

Manual Activities Details provides a further drill-down to the manual activity combinations causing the longest lead times. The first row in the detailed report shows the amount of cases with zero manual activities: 65 cases, with an average case duration of 82 days.  Third row shows that there are 103 cases with single manual invoice receipt with average case duration of 113 days.




Cost Savings by Automation


How much time and money can be saved by your next automation project?

Cost Savings by Automation is a detailed report showing the estimated potential for cost savings. The report is generated for each individual flow in the discovered as-is process model. Each flow is then analyzed by comparing the lead time of automated cases against the manual cases. The difference is regarded as the potential savings for the lead time.

By calculating these savings with the total amount of occurrences for that particular flow, we get the estimate for the potential lead time savings in the total flow. Using the given Activity Cost per Hour, this lead-time improvement is further converted into potential savings in currency.

This report serves as a baseline tool for analyzing the automation cost savings potential. It can be fully customized for your organization's details concerning hourly costs for different flows and other what-if scenario options.



Statistical Calculations

Statistical calculations are used to easily add more depth and accuracy into your process mining analyses. More than 10 operations can be applied to any ChartView measure, dimension or column, in order to show cumulative values, moving averages and trend charts with change from the previous value. Operations can also be used for focusing the chart content into relevant finding - by removing outliers such as null values, insignificant values, too small/large values and incorrect historical timestamps and future events.

Available calculations are:

  • Remove empties (nulls): Removes rows where the measure/dimension/column has a null value.
  • Remove outliers using normal distribution: Removes rows where the measure/dimension/column has a value that is outside the [average] +/- X * [standard deviation]. The X is defined as an additional parameter.
  • Remove insignificant values from start and end: Removes rows from the beginning and end of the data set the measure/dimension/column value is below the defined percentage of the maximum value in the data set. This
    setting can be used to remove e.g. indiscernible items in a column chart.
  • Remove values lower than: Removes rows where the measure/dimension/column value is lower than the defined limit. Requires numerical column.
  • Remove values greater than: Removes rows where the measure/dimension/column value is greater than the defined limit. Requires numerical column.
  • Remove dates older than (days): Removes rows where the measure/dimension/column value is earlier than the defined number of days. Requires a date type of column.
  • Remove dates that are in future: Removes rows where the measure/dimension/column value is in future when comparing to the current time.
  • Show cumulative values: Calculates sum of values for the measure/dimension/column from the beginning until that data point.
  • Show change from previous: Calculates difference to the previous value. The first value shows zero.
  • Show percentual change from previous: Calculates a percentual change to the previous value. The shown unit changes to percentage. The first value shows zero.
  • Smooth using moving average: Calculates all measure/dimension/column values as an average of the nearest values. The additional parameter is the number of steps to go back and forth to take into account.
  • Calculate moving sum: Calculates sum for each measure/dimension/column values. Goes back number of steps defined by the additional parameter.
  • Add by: Adds the specified number to all the measure/dimension/column values.
  • Multiply by: Multiplies all measure/dimension/column values by the specified number.
  • Divide by: Divides all measure/dimension/column values by the specified number.

Several statistical calculation operations can be used at the same time. Some calculations also support an additional parameter, that is specified in the Reference value for statistical calculations field.

Sounds great - let's now move to examples!


This enhanced duration chart is showing the actual duration histogram, with green column chart bars representing the amounts of cases completed within a given led time.

The second measure - shown as the blue line chart - is the cumulative percentage of cases that are completed within the given duration. The cumulative value curve reaches 100%. 

Here, you see the statistical calculations for change in the case count. The duration histogram is same as in the first picture. Now, the spline chart is used to show difference between column bars. The chart on the left shows all values and the chart on the right uses the statistical calculation option "Remove insignificant values from start and end" to filter entries with less than 100 cases - making the chart more readable.




Below is a summary line chart showing five versions of the Case count measure in a trend chart by the dimension "case starting month". The plain case count value is shown as a blue line. The "change in case count" is shown with green column bars. The "moving sum of 6 periods" is the highest reaching black line. The red line shows the case count values multiplied by three. The yellow line chart is the 2-period moving average of the case count.  




I encourage you to use the statistical calculations in your process mining analysis to make your results even more relevant and meaningful for business users. Together with the powerful expression language, you can calculate and present any relevant process KPI measure within your charts and dashboards. 

Advanced Security Hardening

QPR ProcessAnalyzer meets the strict security requirements of large enterprise customers both in the QPR Cloud environment as well as for on-premise deployments.

As part of the 2020.3 release, we provide a list of advanced security hardening options, which should be considered when using QPR ProcessAnalyzer on-premise. QPR Cloud environments are configured to use the adequate security options. Read more about system architecture and components in QPR ProcessAnalyzer Wiki.


Advanced security hardening options for QPR ProcessAnalyzer include:

  • QPR Secure Strings - Secure strings provide a way to store passwords and other confidential information in QPR ProcessAnalyzer in a way that the information can be used without users seeing the original plain text. For example, ETL scripts, SAP, Salesforce and ODBC passwords can be stored as secure strings, which can then be referred to by their names in the ETL script commands.
  • Improved Single-Sign-On authentication - QPR products have already supported single-sign-on (SSO) authentication for more than 10 years, both in cloud and on-premise environments. If your organization is currently using the built-in user accounts and passwords, consider taking the Single-Sign-On option into use. QPR 2020.3 has enhanced support and streamlined user experience for SSO and SSO/Built-in hybrid authentication. 
  • Database user least priviledges - no need to grant db_owner permission.
  • Enable only the latest security protocols - by disabling SSL, TLS 1.0 and TLS 1.1, and enabling only the TLS 1.2, you ensure secure client connections. You should do this change when your client devices have been updated to the latest protocol, ie. support TLS 1.2.
  • Disable weak ciphers - weak encryption methods should be disabled
  • Add Security Related HTTP Headers in IIS - allow only secure https traffic, disable unsecure http, and further limit the user agent rights to load only needed content types
  • Disable 8.3 File Name Creation - short file name creation is designed for old legacy applications and is not needed for QPR deployments

 This is the new QPR ProcessAnalyzer 2020.3 😀👍

If you’re already using QPR ProcessAnalyzer, go ahead and try these new features when you get a chance. If not, and if you’re new to Process Mining, read more on this page. If you want to know more about QPR ProcessAnalyzer, go here. Also, don’t hesitate to book a live QPR ProcessAnalyzer demo:

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, though 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 send our Process Mining team a direct message:

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Webinar Recording

QPR ProcessAnalyzer 2020.3 Release Webinar Recording (30 mins + Q&A)

Webinar Presentation Slides

Download QPR ProcessAnalyzer 2020.3 Release Webinar Presentation Slides here.

QPR ProcessAnalyzer Release Notes

Access the See QPR ProcessAnalyzer Release Notes here.



Written by
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Teemu Lehto

Dr. Teemu Lehto, holding a Ph.D. in process mining, has spent more than two decades advancing the field of Digital Twin of an Organization (DTO). Teemu has helped hundreds of companies achieve unprecedented visibility into their business operations throughout his career. With a passion for this field, Teemu’s mission is to empower organizations to make data-driven decisions, optimize processes, and discover untapped potential within their businesses.

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