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Predictive Process Mining in '24: Top 3 use cases & case studies

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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Predictive Process Mining in '24: Top 3 use cases & case studiesPredictive Process Mining in '24: Top 3 use cases & case studies

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Predictive analysis is one of the top 10 advanced process mining capabilities that have been identified for 2021, according to the latest process mining trends. Predictive process mining enables businesses to maintain their processes running smoothly by predicting possible outcomes, issues and steps in processes. Yet, there is confusion among business leaders about the predictive process mining types and how to implement them.

In this article, we explore how predictive process mining works, its types and top use cases.

What is predictive process mining?

Predictive process mining, also known as predictive process monitoring, is a process mining method that combines predictive analytics with process mining to predict the time and outcomes of ongoing cases by analyzing historical data. For example, in continuous sales processes, the result would be either finalizing the purchase or not.

Attention to predictive process mining has been increasing in academia over the years (See figure 1).

Figure 1: Predictive process mining applications in academic literature

Source: Predictive Process Monitoring: A Use-Case-Driven Literature Review

Leading vendors have leveraged ML algorithms for predictive analytics within their process mining solutions to help their customers predict possible bottlenecks and improve scenario capabilities.

1. Mitigating risk 

Predictions allow businesses to mitigate risks before any harm is done. Predictive process mining enables the discovery of potential issues and the reasons behind them. It can leverage either risk prediction or time prediction categories:

  • Risk predictions are categorial predictions that estimate and inform about risk levels in decision-making processes by configuring process risk indicators and examining historical data.
  • Time predictions are numeric predictions that are applied to measure the delays or remaining time in a continuing process. 

For example, logistics firms can leverage predictive process mining to detect the delays in the delivery of goods and can identify their root cause. Once the delays are classified depending on the root cause, predictive analytics can alert analysts of risk levels for areas or goods that are expected to have a late delivery.

2. Predicting costs

Businesses can leverage cost prediction to help identify present necessities and shortcuts based on insights gained from historical data. Cost prediction is a numeric prediction that consider production, volume and time to predict the cost of the ongoing process. As a result, it can reduce costs once businesses implement the shortcuts or correspond to the needs indicated by predictions.

For example, manufacturing companies can apply cost prediction method to forecast the cost of ongoing process considering production time and volume.

3. Generating recommendations 

Businesses can leverage predictive process mining to generate recommendations by forecasting future activities and outcomes. For example:

1. Next- activities 

Predictive process mining can be applied to predict the following activities in process executions, which is named as next-activity predictions. This type of prediction targets on-going cases for the sequence of future events and calculate the probabilities of each possible scenario. In this case, process mining analysts can leverage deep learning to predict the next activity by converting event logs into words and use natural language processing to classify the process instances.

For example, this method can be implemented in automobile insurance to estimate the potential fraudulent claims. To do so, predictive process mining tools profile customers and categorize them by leveraging the insights discovered from customer behavior and organizational process data.

2. Case outcomes

Predictive process mining can predict outcomes for all cases considering key performance indicators (KPI). This type of predictive process mining is named as outcome predictions which analyzes the sequence of events that are already performed in the same case and the latest activity from the current case. Based on these outcomes, it provides suggestions for the following events for each case. 

For example, customer satisfaction is an major KPI for many industries. A bank that can predict an issue in front and back offices can react in time to the issues and can improve their customer satisfaction significantly. Another example, healthcare facilities can leverage predictive process mining to estimate the failing cases in staff planning and optimize their strategy.

Predictive process mining work leverages machine learning and deep learning

Predictive process mining leverages different machine learning algorithm, such as:

Supervised machine learning algorithm: Supervised ML algorithms such as decision trees, Markov models, evolutionary algorithms, support vector machines, can be trained on historical data in order to predict the final result. These algorithms’ results can be easy to predict as the algorithm provides in detail the steps taken to obtain the final result. 

Unsupervised machine learning: Unsupervised machine learning algorithms such as deep learning and neural networks have a black box approach for predicting the final results, making it harder for process mining analysts to understand the root cause of a prediction. However, despite the difficulty, studies have shown that deep-learning-based models are more successful at predicting accurate results compared to supervised machine learning models.

Some of the capabilities of predictive process mining include:

  • Labeling normal or deviant case
  • Estimating activity duration and the time remaining to accomplish a task
  • Predicting following activity
  • Forecasting possible outcome

Further Reading

To learn more about process mining use cases, machine learning and AI applications, feel free to read our in-depth articles:

If you believe your business can benefit from process mining tools, you can check our data-driven list of process mining software and other automation solutions.

Check out our comprehensive and constantly updated process mining case studies list to understand predictive process mining with real-life examples.

And you can let us find you the right vendor:

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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