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Top 3 Process Diagnostics Applications in 2024

Updated on Jan 4
3 min read
Written by
Hazal Şimşek
Hazal Şimşek
Hazal Şimşek
Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation.

She has experience as a quantitative market researcher and data analyst in the fintech industry.

Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.
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A point of confusion usually arises: users believe process mining and diagnostics are one and the same. We want to clarify that process diagnostics is a method in process mining which reveals insights about the problems and deviations within a process.1 As a result, it helps business leaders and experts develop appropriate solutions for the issues in their day to day processes.

Process diagnostics has three main applicationsproblem classification, root-cause analysis, and trend capturing. However, process mining users do not use process diagnostics or these applications but the software.

In this article, we aim to explain each of these use cases/applications in more detail. We will also provide real-life examples of enterprises that have used process mining to efficiently and effectively diagnose the faults in their processes.

1. Problem classification

Problem classification refers to predicting categorical classes for inputs or data varieties to generate a model, such as a logistic regression 2or a decision tree 3.

The classification model categorizes inputs, and concludes a classification result, by predicting outcomes from them. Machine learning algorithms, for instance, facilitate the classification of problems by running these models automatically.

In the case of process mining, classification models can help classify issues and deviations detected by conformance checks.

For example, a compliance or an audit company’s responsibility is to check if the process cases that it comes across are fraudulent or authorized. In this specific use case, these companies can use process mining to seamlessly separate the legitimate from the illegitimate by employing special algorithm for classification.

2. Root-cause analysis

This methodology helps businesses find the underlying cause behind problems by listing the potential reasons, as well as the data-driven correlations that exist between the issues. and the data-driven correlations between them. 

Some process mining vendors offer automated root-cause analysis capability, enabling users to automatically obtain root causes for each deviation, delayed activity or error that is discovered in a conformance check and performance analysis.

Automated root-cause analysis deploys machine learning algorithms to diagnose factors that might impact the tasks and operations. For example, root-cause analysis uncovers customer dissatisfaction by delivering insights from event log data that contains information about customer preferences and their journey experience.

3. Process trend analysis

Trend analysis allows for identifying patterns in data and understanding the changes over time. Sales operations or performance metrics, for instance, are some of the applications of trend analysis in sales data.

When applied to process data, trend analysis detects principles and changes in sales-related variables and analyzes them for diagnosis and control. In essence, process trend analysis enables data-driven decision making by improving process supervision that traditionally relies on visuals and humans.

Moreover, process mining applies trends analysis to pinpoint the positive and negative patterns so that analysts can set their well-performing processes as standard and reference models while looking for ways to improve the ill-functioning operations. Performance analysis and conformance checks are the process mining attributes to achieve this.

For example, supply chain firms can benefit from trend analysis to monitor their complex logistical processes in real-time and to identify their operations’ patterns and pain points to alert the employees in charge.

Case Study

Process diagnostics propose a new approach for improving healthcare facilities and patient diagnosis and treatment. For instance, a hospital in Galicia, Spain, utilizes process mining for process diagnostics of cancer patient journeys. 4

Early diagnosis and treatment are crucial for cancer remedies. But the hospital noticed a trend in which some patients started treatment earlier than others. Therefore, the analysts were interested in understanding the root-cause behind the time-gap that existed between the admission of some the different cohorts of patients.

Process diagnostics results indicated that only 20% of the patients proceeded through the “fast path.” These patients were directed from the Oncology department. For example, analysts observed loops in the processes of the radiology department, which was tasked with processing patient requests, and had caused a significant loss of valuable patient time. The analysts identified that 12 patients repeated the loop 14 times, leading to an average of 31.6 weeks of delays.

By understanding the cancer treatment path more thoroughly and meticulously, the hospital established procedures to prevent loops and time loss while improving healthcare quality and diagnosis time.

To explore other applications of technologies that work by simulating the optimal version of a process and pinpoint deficiencies, read more on the digital twins and process mining in healthcare.

Further Reading

You can learn more on machine learning applications of process mining, predictive process mining and other trends in the market by reading:

If you believe your business can benefit from process mining, you can start reviewing and comparing tools from our comprehensive and data-driven lists of vendors.

And, if you think you need more help:

Find the Right Vendors

Hazal Şimşek
Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation. She has experience as a quantitative market researcher and data analyst in the fintech industry. Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.

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