"Successful Process Mining Projects" e-book

Driving blind and too fast (or not fast enough)

Most organizations have a hard time seeing where their performance lacks. They see the metrics from the past, like sales numbers, but don’t have visibility into how these numbers were produced. What went well, and what did not go so well (and why)?

So they have no idea what and how to improve their processes. Or they try to get visibility but have no consistent monitoring in place:

• Do organizations measure the performance of their processes? Well, often I see some metrics being measured (like “number of widgets created” in manufacturing), but those are mostly lagging metrics.
• There are almost no predictive measures being derived or defined, and processes are not adapted to changing situations, for example when it comes to delays of a supplier shipment.
• And then there are just a few organizations that actually have their processes formally designed, for example in the form of process models. And those who do might see them only as an exercise to “appease the gods”—may that be the management or the regulators.
• That leads to interesting results when it comes to audits—some poor souls trying to figure out what actually happens in the processes that they audit. And that might lead to regulatory notes that your organization has not made enough progress or even to hefty fines (I am looking at you, finance industry).

Overall, I am missing an awareness of the need to measure what you are doing. Some smart person once said, “You can only measure what you see,” and how boring would a football match be if we had not defined what “winning” means and how you can create the necessary points that will end up on the scoreboard? 

Process Intelligence - a new frontier for Processs Management

Over the last couple of years, I have seen more interest in addressing these issues. And for good reasons:

• Process mining (as the main technology for process intelligence) is the “speedometer for your business.”
• It will give you the visibility into how your processes are executed. Not only on the “happy path” that you might or might not have documented, but with all its variations and loops that you have stored in your runtime systems of record.
• Process mining gives you calculated performance metrics out-of-the-box, like frequencies and times, and also shows you where you have rework or other unwanted behaviors in your process data.
• Moreover, you can create your own dashboards for calculated metrics that allow you to investigate your hypotheses of your process performance. Data from process mining analysis can also be sent to traditional business intelligence tools and be embedded in already existing dashboards as well. And moreover, you can trigger actions from thresholds that you define in your process mining tool and interact with running process instances—think notifications, alerts, or transactions. This also allows “non-process nerds” to gain insights into what they are doing in their processes.
• Lastly, process mining is embedded into a larger process management practice and tool stack, and you can, for example, download discovered processes (in case you did not have any documentation) and use these for future-state process design and simulation. Or you can upload already existing reference processes into Process Mining from your repository, and the tool checks to what degree you have conformed to “how the work should be done.”

Mining technologies close the missing gap in the process/solution lifecycle and provide the data-driven analysis approach for your complete process performance management.

Who is this book for?

This book is for people who are serious about how they can improve how their organizations run and how they can make their next large transformation project a success. They should be able to measure the benefits of the large efforts that go into that.

It is for the process and architecture practitioners who run their programs for years and don’t get the visibility that they deserve.

It is for the analysts who want to switch their approach to a more data-driven approach (while not forgetting the human contributions to any analysis) and for whom the words “Digital Gemba” sound good.

And of course, it is for the curious folks who have heard about “process intelligence” and were wondering what this means and how this approach and new technologies fit into their organizations, without creating another hype and disappointment when the results of the first project do not meet the inflated expectations. As you know, the first time you do something, you most likely suck, and that is the same here.

But wait, this is not all.

This book also comes with a code for the accompanying website, which includes additional tools: all graphics as a downloadable PDF and a checklist that summarizes the key aspects of the six-step process for successful process mining projects.

And in addition to this, this book also comes with the necessary source files, data workflows, reference models in BPMN format, and, in case you use the same tool stack, an ARIS database and an ARIS Process Mining project.