Blog post OptiMinds Improve the real process with Process Mining

Improve the real process with Process Mining

Automation is the crucial, last part of the digital transformation. To reap the benefits of that, you must have insight into the process, be able to analyze the data, and make objective decisions. Process Mining can help with it.

Particularly financial departments can profit a lot from automation and robotization because there are typically processes and workflows in those departments that can be standardized. Before you start implementing automation, you need to have a clear picture of the different processes and the workflows involved. And there lies the challenge. Because by interviewing employees to get a picture of those workflows, you do not always get a view of the ‘real’ process’; you often end up with a ‘perceived’ workflow. Mostly this happens, because an employee cannot oversee the entire process, but rather just a part of that. What happens is that you get an interpretation of the workflow, instead of the raw data that maps the actual workflow.

Not so lean as you would like

For instance, the ideal workflow at the Purchase-to-Pay or Order-to-Cash department should be straight and lean: An order is placed, the order is delivered, an invoice is sent to the customer (or supplier), the payment is processed, and the case file will be archived. However, in reality, this is not the case; the workflow is complicated, circular, and can contain a lot of deviations. The issue is that what is perceived by employees as the real workflow, often is much more complicated if you look at the data. If you would analyze only the perceived process, you can end up with automation or robotization suggestions based on the wrong data. That can make automation less beneficial.

Mapping the real process

A way to get insight into the real process is by analyzing the raw data. Since there is a lot of data present, even in the leanest form of the Purchase-to-Pay or Order-to-Cash workflow, we think that automating this process can make a difference. We call it Process Mining. By using specialized software such as Minit, we are able to map every mutation that happens during the process. This happens because Minit acquires all the data from the ‘event logs’; data that is being entered or altered whenever there is a mutation in the status of a casefile: delivery confirmation, rejected payment approval, and so on. It also logs the time it takes for each action, including times between mutations. The software uses all the data generated by existing systems but is also able to record new data on its own, through the usage of ‘keylogging’ users’ input. And with the ‘Plug and Process’ principle of Minit, it is easy to implement it with existing systems, such as ReadSoft Process Director.

Accessible data, thorough analysis

The data is displayed in user-friendly ways, giving users an insight into the actual (real) workflow. It lays connections between the different steps in the process, where there are points of improvement and opportunities for automation and robotization. Because it uses data, rather than subjective interpretations, you can make more objective decisions. It is also a more efficient way to gather the information needed, rather than taking precious time away from employees by conducting interviews and workshops.

To benefit the most from automation and robotization, you need complete insight into the processes you are considering automating. Process Mining will make it able to fully map a process based on realtime data and give you an objective representation of workflows, points for improvement, and opportunities for automation. If you would like to know more about Process Mining, how it can help you understand your processes and workflows, allowing you to reap all the benefits of automation them, please do not hesitate to contact us.

Process Mining will make it able to fully map a process based on realtime data and give you an objective representation of workflows, points for improvement, and automation opportunities.

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