Definition and Validation of Process Mining Use Cases

You’ve implemented the latest tech and expect results, but the numbers don’t add up. That’s how most businesses realize something in their processes isn't working. Now imagine being able to extract this hidden knowledge with data. Welcome to the power of process mining.

You didn’t start here though. You began by questioning what was wrong with your production line, your customer service, or your supply chain. It wasn’t obvious at first—then came process mining: the breakthrough you didn’t know you needed.

What Is Process Mining?

Process mining is a data-driven technique that helps organizations analyze and improve their business processes. It bridges the gap between traditional business process management (BPM) and data analytics. Rather than relying on hypothetical models or gut feeling, process mining looks at the real data—event logs from IT systems—and uncovers how processes are actually carried out.

Unlike traditional methods, process mining doesn’t require heavy manual documentation or subjective reporting. Instead, it extracts information directly from your systems, uncovering patterns, bottlenecks, and inefficiencies. This process is automatic, precise, and often reveals insights no one expected.

Real Use Cases

Where does process mining fit into real-world applications? Here’s the twist—it’s not in the places you would expect.

Case Study 1: Improving Customer Experience in Telecom

A major telecom company was facing rising complaints about slow customer support response times. The existing processes were already well-documented, and management didn’t understand where the problem was. Enter process mining.

Using data from their customer interaction systems, they identified a hidden bottleneck in the customer escalation process. The reality was that support tickets were being delayed at the middle management level, something previously unnoticed. By tweaking this part of the process, customer satisfaction skyrocketed within months.

Case Study 2: Optimizing Supply Chain in Manufacturing

In a large manufacturing firm, delivery delays were costing the company millions. Management initially blamed external suppliers, but the real issue turned out to be internal. By applying process mining to their logistics data, they discovered inefficiencies in how products were packaged and loaded onto trucks. A simple adjustment increased delivery speed by 15%, drastically cutting costs.

Validation: Why Some Use Cases Fail

While success stories abound, not every attempt at process mining leads to significant improvements. A lack of proper validation is often the culprit.

Mistake 1: Focusing on the Wrong Data

In many cases, businesses become too focused on one specific part of the process—overlooking broader issues. In a failed implementation for an e-commerce company, process mining was applied to their order tracking system, while the real problem lay in inventory management. The insights gained were irrelevant, wasting both time and resources.

Mistake 2: Ignoring Stakeholder Input

Another common pitfall is neglecting to involve key stakeholders. Process mining may reveal hidden inefficiencies, but without buy-in from the people running the day-to-day operations, these insights are often ignored. In a failed banking sector case, management overlooked employee feedback when trying to optimize loan approval processes. The resulting automation clashed with existing workflows, leading to a temporary drop in approval rates.

The Validation Framework

So how can you ensure success? The validation of process mining use cases must involve a multi-step approach:

1. Data Alignment: Ensure the data you’re mining directly correlates with the processes you want to improve. This may sound obvious, but it’s often overlooked.
2. Stakeholder Engagement: Involve people who are deeply familiar with the day-to-day workings of the process. They will help refine the focus of the analysis.
3. Feedback Loops: After implementation, create a system for continuous feedback. Processes evolve, and so must the data analysis.

Process Mining Tools and Validation Techniques

There are various process mining tools, each offering unique capabilities. Common tools include Celonis, Disco, and ProcessGold, and validation techniques vary across them.

• Celonis offers a comprehensive suite that allows real-time insights into inefficiencies. It's commonly used in supply chain and finance departments to identify bottlenecks.
• Disco focuses on process discovery and performance analysis, making it useful in understanding previously undocumented processes.
• ProcessGold integrates with existing BPM tools, making it easier to align mined data with existing workflows.

Once a process has been mined, validation techniques include root cause analysis, simulation, and A/B testing. These methods allow you to confirm that the changes you make will have a measurable impact on your process’s performance.

Future of Process Mining: Predictive and Prescriptive Analytics

Looking ahead, process mining is evolving from being a retrospective tool into a predictive and prescriptive one. By applying AI and machine learning, businesses can not only understand what went wrong but also predict future bottlenecks and suggest specific corrective actions. This is the future of automated process improvement—a system that doesn’t just tell you what’s wrong but also how to fix it before it becomes a problem.

Final Thoughts

In the end, the definition and validation of process mining use cases come down to asking the right questions, involving the right people, and ensuring that you’re solving the right problem. It’s not always straightforward, but the potential rewards make it worth the effort. When done correctly, process mining can provide you with a competitive edge that’s hard to beat. The trick is in the validation—without it, even the best insights can be rendered useless.