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Process Mining Use Cases: Possible Outcomes 2

Previously, we have looked at who uses process mining and why. Another way to understand the different process mining use cases is to look at the possible outcomes of a process mining analysis.

Process mining requires a skilled human analyst

Process mining allows you to analyze very complex processes. Furthermore, you don’t need to know what the process looks like (just identify the three parameters case ID, activity name, and timestamp) and you can even look at the same process from different perspectives. In turn, process mining is not an automated activity but needs a human analyst to understand the data and interpret the results, and some skills and experience to do it well.

But what exactly could be the outcome of such a process mining analysis?

Possible outcomes of a process mining analysis

On a high level, there are four main outcomes of a process mining analysis (see also picture above). For any process mining project, a combination of these outcomes can apply.

1. Answer

Sometimes, the outcome is just an answer. For example, imagine you are the manager of a process and have received complaints that this process is taking too long. There is an internal Service Level Agreement (SLA) and you want to know whether the complaints are justified (and if so, how often it happens that the SLA is not met). Getting an answer to this question is the primary goal of the process mining analysis.

Another example would be a data science team that supports a customer journey project, where the customer experience is completely re-designed. To make sure that the new system supports the customers in the best way, the data scientists have been asked to analyze what the most common interaction scenarios are.

Finally, think of an auditor who assesses the compliance of a process. The audit report with the summary of their findings will be the main outcome of the process mining analysis.

2. Process change

In many situations, the outcome will be a process change. For example, a particular process step may be automated. There might be organizational changes to address the high workload and shortage of resources in a certain group. An update to the FAQ or website of the company could be made to prevent unnecessary customer calls. Based on the assessment of the audit team, a new control could be implemented in the IT system to reduce the risk of fraud. Or based on the analysis of an outsourced service process at an electronics manufacturer, the contracts with the outsourcing partners will be renegotiated in the next year.

Typically, the analysis will be repeated after some time to see whether the change was as effective as one had hoped. It is easy to repeat a process mining analysis with fresh data to investigate these effects. The outcome of the follow-up analysis can then again be just an answer or result into more process changes.

3. Monitoring

Sometimes, you can also discover a new KPI that was not known before. For example, imagine you are analyzing a payment process where the company can get 2% discount from their suppliers if they pay within 10 days. You realize that there are two main phases in this process: (1) the posting of the invoice to the system and (2) several approval steps, before the payment can be run on two fixed days in the week. You implement an additional reminder to the approvers in the financial system (a process change), which reminds the managers who need to approve the invoice to do so more quickly. But now the late posting of the invoices is the main problem. You realize that if they are not posted within 3 days, there is almost no chance to get the payment through on time. And you want to monitor this new KPI in an automated way.

Like the process change, this will be outside of the process mining tool. But after understanding the process and the data (to know where the measure points for the KPI need to be placed) it is typically easy to add such a new KPI to your existing dashboard or BI system.

4. Optimization and further analysis

Finally, sometimes further analysis is needed after the process mining analysis has been completed. For example, let’s say you analyze the fall-out from a sales process, which means that you are looking at those customers who were interested in your products but for whichever reason never completed the ordering process (their revenue has been lost). You want to follow up with them and be pro-active offering help before it is too late. However, you only want to follow-up with the customers who are most likely to buy.

This would be a scenario, where a data science team sets up and trains a prediction algorithm in one the available data mining or machine learning frameworks. It will be a custom application that is targeted at one very specific problem (predicting which customers you should call). The prediction algorithm gets better over time, learning from the historical data, but to set it up in the first place it helps to understand the process and possible process patterns that might have an influence and, therefore, could be a good parameter in the model.

In addition, there are many scenarios where process miners will perform further analyses in other, complementary tools. For example, a Lean Six Sigma practitioner will want to perform additional statistical analyses in Minitab, data scientists might use data mining tools to discover correlations between the process variants and other attributes in the data, process improvement experts might want to run alternative what-if scenarios in a simulation software, and auditors might take some of the findings from their explorative analysis in Disco to their regular audit tools to include them in the standard check procedures.

All of these tools are specializing in different areas and can be used together. Process mining provides important input for these follow-up analyses by providing a process perspective on the data.

Conclusion

So, what outcomes can you expect from process mining for your own work?

To find out, first start learning more about process mining to fully understand how it works and what it can do. Download the process mining software Disco and contact us for an extended evaluation license to explore some of your own data sets.

Consider joining one of our process mining trainings. Perform a small pilot project and learn about the success criteria for process mining. To create your business case, keep thinking about how process mining fits into your daily work and how exactly it will help your organization.

Comments (2)

Dear Anne, I agree with the logic on the graph, but would like to add the Continuous aspect to it. In my experience Continuous Improvement tools should be matched with high level of management proficiency, particularly in areas of Manager of mini-business, team leader, team coach, effective communications, managing people and workplace relations (Supervision by Prof. Wickens). Here manager / supervisor is looking for tools that enable him to continuously hone its processes, by removing waste and variation as well as proactively working towards better customer satisfaction. For such a manager Process Mining is a blessing because it provides them with easily accessed, detailed, actionable, meaningful insights about their processes and enables them to continuously improve the processes with help of Lean Sigma tools.

Dear Alexei, Thanks for your note! Yes, this is one particularly powerful aspect of process mining: If you have done the analysis once, you can easily repeat it on new data to see how much you have actually improved.

I also see more and more companies who try to drive the continuous improvement not just from a central unit but to bring this into the business units (by, for example, educating them on the Lean Six Sigma methods there).

A nice example, where process mining was really driven by the actual domain experts and line managers can be found here: http://fluxicon.com/pmoty/2016/


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