The digital detective - Unravel the mysteries of your process

A non technical introduction to process mining

In this article, I will attempt to give a jargon-free primer of process mining and its importance. We will being with understanding what process mining is and how task mining complements it. Next, we will try to understand why this becomes so important. Finally, we will conclude the article with an overview of the broad process mining use-cases.

Imagine a detective trying to reconstruct the homicide crime sequence using the clues that she's got. She speaks to bunch of witnesses and understand the major events that unfolded during the crime day. Next, she further probes into the closed circuit camera feeds, call data records, access logs etc., Now her understanding of the day gets more granular. As the next step, if she get overlay the mobile phone's location data, she can get even more precise. All of these will enhance and provide a comprehensive understanding of events that happened during the crime day. Excuse me if the analogy sounded morbid, but this is exactly what the process mining attempts to do from the event logs.

When you use your IT systems to process something, you keep creating traces of actions. It can be something like moving the application to the next stage or approving a request or anything that changes the state of the entity that is being processed. They have the basic information of who did what and when it was done. These are called event logs. In the case of a purchase order, you will have information of when it was created, approved, fulfilled and dispatched. Process mining software reconstruct the process flow with these event logs. This initial step is also known as process discovery where, all the event logs are synthesized into one single visualization, with chronological sequence of events from start to finish. This is also known as the 'digital twin'.

Does the 'digital twin' help you answer the process questions like speed, efficiency, cycle times etc.? If it does, then good enough. But there are times when you feel that event logs are not very granular. You might realize you need more visibility between two points in an event log because they quite long and a lot happens in-between. This is when 'task mining' comes to your rescue. These software collect the actions happening in the user desktop. You will be get insights on,

* How much time is spent on every application?

* How much time the user toggles between the spreadsheet and the email agent?

* How many items are copy - pasted?

There is wealth of information that gets captured, in an anonymized fashion, into the how work gets done. Now these insights can be consumed independently of process mining or can be converted into an event log and ingested into the digital twin that has already been constructed to get a more granular view of the process.

To appreciate the importance of this technology, you should contrast it to the way process discovery happened in the 'not so distant' past. Big conference rooms with wide walls were booked. The wall was segmented vertically to different sections, with one for each department. Each task was assigned a particular color and each sub-task had one post it note. Understandably, with multiple stakeholders, it gets noisy, chaotic and, at times, contentious. Initially, all the stakeholders have an 'assumption' of what the process is. But during the interaction, all of them agree on a 'perceived' process. This might a politically acceptable version of the process and still be a different version from the actual 'as-is' process. The process mining technology bypasses all these gaps and gives you an objective view of the process based on the data. That is a big cost and time lift to the way processes discovery and transformation was done. It also opens up a lot of other possibilities.

First it super charges business-intelligence. Which helps us get actionable insights on the past, present and the future. On the past - it helps us answer the basic questions like,

* How long did something take ?

* What was our processing capacity?

* How much was the utilization of the resources?

* What the extent of rework ?

* Where are the bottlenecks in the process?

* Did we meet the agreed delivery timelines?

and so on.

It also helps in answering more nuanced questions. For example, in an expense approval process, you might want to understand how many of them took the standard way and how many of them had to get an exception approval because of various reasons. Each of them is a variant of the process. Variants are different ways to reaching the same destination but through different routes. Not all variants are inefficient. But some of them definitely area. Process mining software helps you understand the different variants and compare the process measures across them. This will you focus on the ones need attention and also focus on standardization of the process, wherever possible.

Now lets discuss the present. Once you have this digital twin constructed and the various measures defined, you can use them to monitor your present. Once you plug in real time data into this system, at a very basic level, they can alert you if something is getting stuck in process. It can get more sophisticated than that. When a process sequence or state is encountered, they can even run some automations or take some preset action. It doesn't stop there. Plug this into a sophisticated AI system, they can mimic some of the human decision making as well. This suite of capabilities to manage the operational processes based on intelligence synthesized from the event logs is called 'execution management system'

The next logical thing is to extrapolate the past, use the present data and predict what could happen. Then prescribe certain actions based on the prediction. For example, based on the current work volume in progress and historical cycle times, can you predict the wait time for the new customer order that just landed up? Once you're able to answer that question, you can configure the system to do send a communication or reprioritize resources or any form prescriptive action, based on the prediction that was made. The possibilities are endless.

Now that you have a crystal ball, that answers the past, present and future - what prevents you from answering - 'what if? ' questions. Using simulation capabilities, the process mining software help you simulate scenarios like,

* What if a step was eliminated or automated?

* What if you work on reducing the time taken for a particular step?

* What if you deploy more resources for an activity?

* What if you change some policies?

Various such scenarios can be tested out and you can study how key process metrics change for each of them. This will help you make educated guesses about the process design and the corresponding tradeoff between various measures.

By now, you might have realized how each of these capabilities are going help in different phases of process management. Think of the impact it would have in digital transformation - where you first study the way work is getting done, then evaluate the kind of changes you're going to make, simulate multiple versions of it and compare them. Finally, make the changes and ensure that the processes are getting executed the way you wanted them to. Process mining software can add a lot of value in all these stages.

Another adjacent use case is in evaluating if things have happened according to the rules that have been laid out. Traditionally, during the process audits that happen on a periodic basis, random samples are selected. They are they evaluated if the necessary process steps were following in executing that case. Now with this capability, there is no need for a sample or periodic audits. All the entities are evaluated real time and any exception or deviation is flagged instantaneously. This will free up the auditing department to move up the audit value chains.

I hope this article helped you understand the essence of process discovery algorithms and big edifice that has been constructed on top of it. There are many vendors out there and they are approaching the market differently. You might want to read this article where I explore the evolution of process mining software over the last two decades. Rest assured, irrespective of the domain, this technology is going to have a widespread adoption across the board.

What are your thoughts?