Process mining helps businesses immensely in discovering their processes, enhancing them, and taking data driven decisions. Its value is not limited to specific industries or processes, any process could lend itself to process mining. Since the early days of this field, businesses have centered the use of process mining typically around proven use cases. With developments in AI and advancement in process mining the last couple of years, more use cases for the technology emerge. In this article, BearingPoint and Laurens Dols – CEO of technology and (hyper)automation leader Ofi Consulting – will cover how process mining has developed over the years and illustrate the shift in how this technology is now utilized across industries by businesses.

About process mining

Process mining has evolved into a widely adopted objective approach to business process improvement. Process mining allows organizations to make an x-ray of their business processes and analyze them by leveraging readily available data in their IT systems. There are three core functionalities:  

  1. Enable businesses to map their processes (process discovery)
  2. Ensure compliance (process conformance)
  3. Identify bottlenecks for process improvement (process enhancement)

Typically, in mapping processes people tend to draw these up in the ideal circumstances and the desired process flow, which rarely reflects reality. Process mining overcomes this limitation by tapping into the event logs of any given data source and mapping all the process steps, including alternative paths and exceptions. All that is needed in an event log is a unique instance or case ID, information about the activity/step performed, and the timestamp for that activity.

Use cases for process mining

In the beginning, the field of process mining was of academic interest. Soon process mining gained wider adoption, and specialized data scientists and companies started using algorithms for process analyses where the first use cases for process mining emerged. As such, it was not much later when platforms like Celonis started offering cloud-based process mining solutions around these use cases that are directly linked to the organization’s systems. By implementing such process mining solutions, businesses can real-time map, monitor and identify opportunities for process automation and improvement.

Although process mining can be used for all processes, it is most used where there are proven use cases. For example, the traditionally strongest use cases i.e., accounts payable, accounts receivable, procurement, and order management. In these cases, although the process might be complex, the ability to mine these processes are relatively easy. The use cases for these processes are clear as process mining’s value can be linked to cash flow when taking into account the accounts payable process for instance.

Emergence of new use cases

Laurens Dols, CEO of tech company and (hyper)automation leader Ofi Consulting, has seen the development in the field of process mining firsthand. With his background in consultancy, process improvement and extensive knowledge in the field, Laurens has an excellent understanding of how process mining is used in businesses and how developments in fields like AI add to the field and create new use cases for businesses.

The first use cases where more out of the box solutions are implemented are emerging, yet there are still many new ways to use process mining that might be valuable which are yet to be deployed.

Every industry can benefit from process mining, some use cases are more obvious than others, but essentially any process may benefit from process mining.

Laurens Dols, Ofi Consulting

So far, looking at recent developments, we see three major new trends and use cases for process mining which we will cover more in depth. The three trends we see that are leading the way for new use cases are leveraging both modern technologies but are also setting businesses up for future success, i.e.  

- AI to mine new data points
- Flexibility and anticipating change quickly with new data
- Predictive analytics to mine future process data

1. AI to mine new data points

Intuitively process mining best lends itself to processes where data is readily available. However, changing perspective and looking at it from a business needs perspective, process mining might even bring more value where there are fewer datapoints available to discover and map complex processes that would otherwise be hard to track and quantify like a sales process. Combining the best of both worlds, we see great opportunity for businesses where parts of the processes are well logged and data driven, but some steps have fewer datapoints.

We see a shift from the core four processes to other functional areas where process mining can bring value, one where I see a lot of value is logistics and supply chain.

Laurens Dols, Ofi Consulting

Where there are fewer datapoints logged, advancements in AI and integration with process mining can bring the solution. For instance, event and process data can now be retrieved from camera imagery. AI models scan the footage and recognize the specified activities in the process and logging the time and date of when they happened. Process mining models can then analyze this data similarly to event logs retrieved from ERP (Enterprise Resource Planning) systems. This might prove useful particularly in supply chain and logistics seeing that not all steps of such processes are performed in ERP systems and are not as well-logged in IT systems as most financial processes are for example.  

2. Flexibility and anticipating change quickly with new data

Another emerging use case is where businesses have incorporated process mining, it allows them to quickly shift once there is a change in the environment or litigation and act on that. One concrete and relevant example for today's challenges is sustainability reporting and reducing environmental impact. This shift is partly driven by EU legislation, where companies need to report on their sustainability.

The integration of CO2 emissions in process mining data can in this example offer a solution. Once businesses have insight into their carbon emissions the next step is reducing these. Process mining presents itself as a key tool in helping businesses reduce their footprint by, for example, linking CO2 data directly to transportation modes in their processes. Analyzing these processes real-time can be leveraged to then optimize their logistics and make data driven decisions so that CO2 emissions are minimized.

3. Predictive analytics to mine future process data

With advancement in machine learning and AI models, businesses can train models on their historical data and make predictions about the future given a set of variables. The integration of such technology with process mining would mean that instead of utilizing process mining solely to mine and monitor historical process data, businesses can start optimizing their future process in advance and solve potential bottlenecks before they appear.

The demand for such integration of process mining and predictive analytics is already there, due to the relatively recent adoption advancements in these areas it is now key to prove the business case with new use cases that have never been done before.

Laurens Dols, Ofi Consulting

BearingPoint and Ofi Consulting

As technology and management consultancy practice, BearingPoint has built up a strong presence in process optimization and transformation around the world. Leveraging the experience and data capabilities of BearingPoint and technical implementation expertise of Ofi Consulting, BearingPoint and Ofi Consulting are uniquely positioned to fully advise, implement, and analyze process mining solutions for any process.

Interested to learn more about process mining and find out if there is an effective use case for your processes? Contact one of our experts in the field and read more of our recent articles on the topic:


Rick van der Gulik
Senior Management Analyst

Lucas van Luijtelaar
Senior Manager

Laurens Dols
CEO, Ofi Consulting