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5 Benefits & Use Cases of Predictive Process Monitoring in 2024

The future of process mining will be more predictive and actionable than descriptive. Process mining vendors are adopting ML algorithms to enable predictive and prescriptive process mining.

These capabilities facilitate optimizing business operations, enhancing performance, and mitigating risk by shortening the time dedicated to process analysis and visualization.

However, these are novel capabilities and most business leaders are not aware of them. We’ll explain what predictive process monitoring is and its 5 applications business leaders and analysts must know before adopting any improvement tools.

What is predictive process monitoring?

Predictive process monitoring is a sub-category of process mining that forecasts ongoing processes. Predictive process monitoring delivers information on: 

  • Execution trace
  • The outcome of the process
  • Termination date
  • Future activities
  • Sequence of events

Figure 1 shows how predictive process monitoring generates predictions. It leverages historical data to encode traces and classify the cases. Based on the results, the feature can provide predictive insight into the firm’s operations.  

The visual shows how predictive process monitoring operates by capturing historical data and encoding them against the real-time data to classify and predict
Figure 1: Predictive process monitoring workflow, Source: Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

Predictive process monitoring can allow users to:

1. Identify and tackle inefficiencies 

Process inefficiencies might increase costs and employee churn rates while decreasing customer satisfaction and productivity, leading to long-term disruptions of processes and businesses. This is why 73% of business executives declared their accelerating interest in improving and managing their businesses. 

Predictive process monitoring can be a useful approach for business leaders targeting to pinpoint improvement areas and develop BPM projects and strategies. It can do this by seamlessly pointing out potential frictions such as bottlenecks, errors, and variations. 

For instance, a multinational manufacturer can predict bottlenecks in their production or supply chain processes to improve their on-time delivery. 

2. Plan and allocate resources effectively 

Effective resource allocation can boost profits while preventing the incorporation of unnecessary features or functions.    

Many companies rely on traditional tools such as excel while managing their resource allocation. However, such tools are slow and not easy to gain insights or develop faster strategies.  

On the other hand, predictive process monitoring can ensure the development of efficient and effective resource allocation against companies’ desired goals. 

Predictive monitoring can estimate the future activities that employees will perform in the process workflow. With such predictions, analysts can detect the specific actions requiring more resources and re-allocate them between high-priority and low-priority cases. 

3. Improve customer experience

According to studies,  70% of customers expect their shopping experience to be smooth and digitally connected. Such a smooth journey is possible if the firm can predict customers’ expectations of the processes in which the customers interact with the employees the most.

Predictive process monitoring can help analysts and customer reps predict important steps and activities that might impact the customer experience. Also, analysts can estimate the customer reactions to products and services delivered at the end of a process before the process is completed and react to it. This way, they can update the outcome of the process by skipping any step or activity that might lower customer satisfaction. 

For example, a healthcare organization can utilize predictive process monitoring to detect if a patient might need an ultrasound and at which specific time (See Figure 2). The tool leverages historical process data and maps clinical pathways for patients with a similar diagnosis to provide such predictive insights. Consequently, healthcare administration can improve the facility schedules to improve patient satisfaction. 

Predictive process monitoring is applied to a healthcare process to estimate if the patient will need an ultrasound or not, if yes, when it will happen and what other events will follow it.
Figure 2: Predictive process monitoring applied to a healthcare process, Source: Predictive Process Monitoring

4. Promote data-driven decision-making  

Motorola’s wrong prediction led the company to invest in iridium satellites, costing them billions of dollars. One way to overcome such crucial mistakes is to make sure that decisions come from data-driven predictions rather than gut feelings. 

However, cleaning and wrangling with company and market data might slow down the decision-making, allowing competitors to gain market share. 

Predictive process monitoring can promote data-driven and agile decision-making because it provides generic process KPIs, such as delivery time and process execution time to evaluate the future performance of the process. 

For instance, supply chain executives can benefit from predictive process monitoring for demand forecasting. This way, they can develop inventory strategies in a data-driven manner, reducing additional costs. 

5. Enable Prescriptive Process Monitoring

AI in analytics sets up complex rules to automatically identify insights and take action. The same trend has been observed for process mining tools, where vendors develop tools that can estimate potential issues and warn the teams in charge or take action. 

Such capability is named prescriptive process monitoring or mining, which leverages predictions to generate an action or advice. Prescriptive Process Monitoring helps prevent an undesired scenario, mitigate the risk or adapt to the changing environment.

One example of prescriptive process monitoring would be to inform customers through emails in cases of delays. To do that, the software must predict the delays through predictive process monitoring and set an action to send emails through prescriptive process monitoring. 

Further reading

Check out our articles if you want to discover more on process mining developments and trends:

Compare different process mining tools through our data-driven and comprehensive vendor list if you want to start using process mining.

And, if you need more help, let us know:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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