Unlock Instant Process Insights: QPR ProcessAnalyzer Now on Snowflake Marketplace
We’re excited to announce that QPR ProcessAnalyzer is now available on the Snowflake Marketplace. As the only process mining software worldwide with native...
With 2020.1, we are proud to present our enhanced real-time process mining functionality, which is compatible with all ERP systems and other data sources, and ensures the analyses are always based on most recent data. QPR real-time process mining also supports a single-data concept with modern ERP systems like SAP S/4HANA, making it possible to create the process mining model on-the-fly as a report directly from the then-current data in SAP.
In addition to Real-Time Process Mining, the 2020.1 also includes a nice selection of new pre-configured process mining charts.
Background: Emma has built an impressive number of process mining models to cover all the main processes of her organization. She has also carried out a process mining training program, so that more than 100 employees in her organization have turned into eager process mining enthusiasts using process mining models for RPA automation, process excellence, internal audit, supply chain management and driving digital transformation.
Challenge: After making the initial findings about processes, Emma's colleagues start to continuously ask for more up-to-date process mining models:
Managers and team leaders are asking for Business Review Process Models to view the processes and KPIs for the previous review period as it completes, whether it is a quarter, month, week, day or a work shift. They want to identify the most recent process bottlenecks, rework, and long lead times. They are also eager to see the root causes, so that they find corrective actions within their teams right away.
Internal Audit is requesting company code -specific models for audited processes. These Ad-Hoc Process Models should have the latest information concerning the audited business.
Customer service and logistics workers need access to most Customer Specific Process Models, containing all ERP events related to customer sales orders, ongoing projects, and metadata changes. This information needs to be available when the phone rings or inquiry email needs to be solved.
Real-time Dashboard Screens are requested, showing the current KPIs compared to targets, as well as the current bottlenecks in the operations at this very moment.
Operative managers from service desk and customer delivery teams are asking for Artificial Intelligence based Case-Level Prediction Models to show, based on previous process flow behavior, the probability of a customer ticket not meeting the Service-Level Agreement (SLA) or the probability of a customer delivery to be delivered late. These models are updated on an agreed period, for example once per day, as a tool for operative managers and teams to focus their daily efforts and improve customer satisfaction.
Solution: Real-Time Process Mining powered by QPR ProcessAnalyzer 2020.1 is capable of delivering the process mining analysis on-the-fly for each of these use cases! In this blog article, I will discuss the full process mining model life-cycle and present the needed functionalities. In the picture below, we see the main three stages of process mining model creation: Extraction, Transformation and Loading:
Now it is time to dive into the technical details. Feel free to jump over the details now and come back later when you are implementing a real-time process mining solution....or better yet, browse through this full blog to get an idea of needed technology component details:
Data Systems are the physical ERP systems, web applications, files, and databases containing the event data for process mining. Data from multiple sources is typically used when creating end-to-end processes. An organization may be running for example several on-premise copies of a SAP ERP system, one copy of Salesforce as a cloud service, a few custom applications using SQL Server database, a selection of excel, csv and XES files containing data from legacy systems and 3rd party business partners. Finally, there can be IoT (Internet of Things) devices and applications sending event data without storing it into any database. All of these data sources are supported by QPR ProcessAnalyzer and can be freely mixed to create process mining models that provide valuable insights!
Connectors are the tools and interfaces for connecting to the Data Sources. QPR supports a wide selection of Connectors that make it easy to connect to all your Data Sources. The first set of Connectors allow QPR ProcessAnalyzer to pull data from other systems, databases and files. The underlying connectivity is build using standard interfaces and technologies like ODBC, OleDB, ADO.NET (SQL), SAP (R/3 SAP .NET Connector, SAP S/4HANA ODBC), Salesforce and generic Web Service. The source systems can also push the new data into QPR ProcessAnalyzer. This is supported by the QPR ProcessAnalyzer WebService API. Naturally, it is also possible for a user to manually import data using web, excel, and file uploader interfaces.
More information about the QPR Connectors:
https://www.qpr.com/products/qpr-connectors
https://devnet.onqpr.com/pawiki/index.php/QPR_ProcessAnalyzer_Scripting_Commands
Extraction can be performed by multiple software components:
Extraction can be performed as complete or incremental.
The complete extraction mode is used to extract the full data from an ERP system for the analysis. Since all the data is reloaded, there is no need to worry about changes. When delivering real-time process models, the performance may become an issue, specifically if it takes hours to extract complete data from the Data System.
For real-time process model extractions, it is beneficial to store data into datatables during the ETL process. Benefits/use cases for using datatables include:
QPR ProcessAnalyzer datatable concept is further documented in https://devnet.onqpr.com/pawiki/index.php/QPR_ProcessAnalyzer_Objects_in_Expression_Language#Datatable.
Datatables are stored into the underlying SQL Server database supporting Terabytes of data (https://docs.microsoft.com/en-us/sql/sql-server/maximum-capacity-specifications-for-sql-server?view=sql-server-ver15)
https://devnet.onqpr.com/pawiki/index.php/DataFrame_in_Expression_Language#Merging_DataFrames
Transformation converts the extracted data into process mining models. When connecting to multiple source systems, it is beneficial to store some of the extracted data for later use. Also, the transformation itself creates intermediate data that is used during the transformation operation, for example for temporary tables containing temporary results.
More information about the options:
https://devnet.onqpr.com/pawiki/index.php/QPR_ProcessAnalyzer_Model_Datasources
There are many situations when a new process mining model is created or an existing model is updated with new data.
Unloading process mining models from memory and deleting the models completed are necessary steps for covering the full life-cycle of process mining models. Typical situations include:
Thank you for reading this far! My personal tip-of-the-day for building real-time process mining in your organization is the following:
QPR ProcessAnalyzer 2020.1 is shipped with pre-configured process mining charts for typical process mining tasks including:
Case Trend by Selected Event showing the case counts of how many cases go through any of the selected events. By default the report uses weekly trends:
Root Causes showing the possible root causes for any discovered process mining finding. By default, the chart shows the biggest problem areas. There is a nice tool tip that explains any selected root cause in more detail, for example: "Customer Group Kids has the analyzed feature in 22 % of the cases which is 5.1 % more (197 cases) than on average. This root cause explains 11 % (197 of 1,820 cases) of the occurrence of the analyzed feature, provided that there is a causal relationship." It is also possible to limit the search of Root Causes to only include the selected case attribute and to show the best practice examples.
QPR ProcessAnalyzer includes extensive usage statistics reporting, which helps to understand model usage and ensures the results are delivered to right people. 2020.1 introduces five new usage reports as Chart View pre-configured charts:
This is the new QPR ProcessAnalyzer 2020.1 😀👍
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:
Thank you for participating, here are the release note presentation pdf and release webinar recording:
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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