SEEBURGER Goes Process Mining – An Adventure in Optimization

Process mining is awesome. It lets us identify both regular and non-regular processes from a lot of chaotic data, and we barely have to do anything! Like many other companies, we at SEEBURGER use several IT systems to do our daily work. Our idea was to use our own integration platform to read and compile the accruing data from all these systems and tools and analyze this using a process mining tool. In this article we look at what process mining is, how it works and how our integration platform can support this.

What is Process Mining?

According to Wikipedia, process mining is “…a process management technique that enables business processes to be reconstructed and evaluated on the basis of digital traces in IT systems.”[1]

I had difficulty understanding that sentence the first time I read it. Once I looked more closely at it, it became clearer. Let us deconstruct this sentence.

• business processes
  Essentially, a business process is a structured sequence of activities with fixed start and end points. In theory, this sounds very simple. In many companies, however, the reality is different. There are often a large number of individual activities that are assigned to different processes and linked to each other. A company’s business processes therefore tend to resemble a tangle of dependent activities.

Figure 1: Processes can look quite different in reality

• process management
  We define process management as analysing, designing, controlling and improving business processes. The aim is to increase their quality, efficiency or flexibility.
  When you manage a process, you always know who is responsible for what task, when it will be performed, and what resources are required for it.
• digital traces in IT systems
  This explanation is quite simple. IT systems process data. Information about previous events and activities are gathered in (event) logs. These are the digital traces. Every IT system in a company creates an event log in a more or less detailed and structured way.
  We usually only use this data to analyze errors or, in individual cases, to track activities. That’s quite a lot of data that is rarely used! Most companies have mountains of unused data they could mine for insights.

Figure 2: Event log data piles up and usually remains unused

How does process mining work?

You may already have an idea of what this means:

Process mining is a method of analyzing such data mountains in a structured way. It lets you determine the actual processes behind these event logs.

This process analysis is known as discovery. Process mining requires no human intervention, rather works silently and independently behind the scenes to extract how processes actually occur in reality based on the mountains of data. That’s really awesome, isn’t it?

Both real-time and archive data can be used for discovery. The purpose is to analyze individual business processes or overall business processes across departments/companies.

This gives you a better understanding on the time and resources you need to allocate to each process. You can also use these detailed insights into how processes really run to identify where you can – or should – make improvements.

The results of this analysis is often quite different from what the process designers/managers originally intended.

Once you have defined a process, you can verify and document how well a processes complies with this in practice. This analysis is known as conformance. This is a key task in an audit, where auditors examine whether legal requirements or internal rules are being upheld.

And it is precisely these findings that are the focus of the third process mining use case, enhancement. This involves taking historical data and running simulations to optimise your processes.

Figure 3: The three main use cases in process mining are discovery, conformance and enhancement.

In summary, using process mining to optimise a process involves the following:

• Collecting data from event logs
• Running process discovery to identify what is really happening
• Doing a conformance check to identify areas for improvement
• Performing model enhancement to define a desired target model
• Implementing the enhanced model
• Continuous monitoring of the optimized processes and identifying further areas for improvement

Now we have looked at the theory behind process mining, let us look at how SEEBURGER can contribute to it. Alongside analyzing and evaluating our customers’ processes with the help of our integration platform, we have had extensive experience of process mining in-house.

Our key findings on process mining

One of the most significant surprises that we experienced in our analysis is that the actual processes that we analyzed on the basis of EDI data or event logs very rarely conformed to the theory. Even in our own business area, where everything has already been digitalized, the variants are unexpectedly high. Process mining lets you quickly and easily discover throughput times and how long individual steps actually take. It’s also quite simple to identify bottlenecks. Since process mining is industry-independent, we were also able to apply it to non-EDI processes, such as the question “Is our customer service working?”. This was based on ticket data. Fortunately, our integration platform can handle any interface that an IT system provides.

We also realized quite quickly that our original expectations weren’t realistic. You can’t just run an application’s event log through a process mining tool and hope for a result. All the process mining tools we considered expect prepared data. This always means a combination of data, which we refer to as a data triple. To be more specific, this means the following three types of information:

• A unique case ID.
  The case ID identifies activities related to a specific process. For example, these could be an order number, a ticket number or some other identifier.
• One or more related activities
  There must always be at least one activity for each case ID. Of course, there could be more than one. For example, if the case ID is a ticket number, an associated activity would be “open ticket” or “forward ticket”. If the case ID is an order number, the activity could be an order, an order response or an invoice.
• Timestamp for each activity
  For sorting purposes, there must be a timestamp for each activity in the process. This allows you to see how long each step took in reality.

Figure 4: SEEBURGER’s integration platform prepares event log data for process mining

Let us go back to our key findings:

• Providing data for process mining and process tracking is easy if there is a case ID.
• Only structured data can be used for process mining.
• The quality of the data is critical. The results will not be accurate if there isn’t enough data or this is incomplete.
• You need to understand the process before you can optimize it.

Now, let’s look at how SEEBURGER can help you with your process mining.

How can we help your process mining?

Before you can run discovery on your data, it needs to be exported from your systems and prepared. At the heart of this is the data triple, which is a number of activities and timestamps that are assigned to a unique case ID.

As integration specialists, we have several decades of experience with interfaces and data preparation. We can get the data you need for process mining flowing to where you need it. We can also support in a consulting capacity and help answer the questions which arise. Is a particular process efficient? Are responsibilities clear? Would a certain change get to the root of the issue, or only treat the symptoms?

If you are planning to do process mining, we can support you in making the necessary data available.

Hopefully this article could give you a little insight into process mining. We are convinced that every company can benefit from it. As we said at the beginning, process mining is just awesome.

[1] Source: https://en.wikipedia.org/wiki/Process_mining . Accessed 09.03.2022

Source: https://blog.seeburger.com/seeburger-goes-process-mining-an-adventure-in-optimization/