Predictive process monitoring is poised to transform business operations in 2024. By leveraging the power of process mining, machine learning, and AI, organizations can gain invaluable visibility into the future of their processes to drive optimization.
In this post, we‘ll explore the top 5 benefits of predictive process monitoring along with real-world use cases. After a decade implementing predictive analytics solutions, I‘m excited to share these insights to help your organization evaluate this emerging capability.
A Quick Introduction to Predictive Process Monitoring
Before diving in, let‘s briefly overview how predictive process monitoring works:
- Analyzes historical event log data from your systems
- Identifies trends, patterns and correlations
- Builds ML models to predict future process execution
- Forecasts outcomes, flows, bottlenecks in real-time
This animation summarizes the workflow:
Predictive process monitoring workflow. Source: Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
The predictive insights unlock significant opportunities to optimize processes and resources. Now let‘s explore the top 5 benefits:
1. Pinpoint Waste and Inefficiency
Process inefficiencies lead to higher costs, lower productivity, and poor experiences. A recent study found that 49% of companies struggle with process waste and rework.
Predictive process monitoring helps uncover these inefficiencies via:
Activity Analysis: Highlights redundant steps, delays, and variability
Flow Optimization: Identifies bottlenecks and constraints
Resource Waste: Flags overprocessing and misallocation
Error Prediction: Detects anomalies and risks proactively
With real-time predictive insights, you can rapidly pinpoint and address process waste. This drives significant efficiency gains. For example, an insurance firm used predictive monitoring to find duplicative reviews in their claims process, reducing processing time by 5 days on average.
2. Dynamic Resource Planning and Allocation
Aligning resource capacity to fluctuating process demands is tricky. 86% of business leaders say inadequate resources impact process outcomes according to a Simon-Kucher survey.
Here are two ways predictive process monitoring empowers dynamic resource planning:
Volume Forecasting: Estimate future workload across processes
Activity Analysis: Identify resource burn rate for specific tasks
These insights allow you to forecast resource needs and re-allocate staff, systems, inventory and other capacity. For instance, a logistics firm used predictions to balance warehouse staffing with order volumes, cutting overtime costs by $2.2M annually.
Predictive monitoring drives data-driven resource optimization.
3. Deliver Standout Customer Experiences
Customer experience is a key competitive differentiator, with 89% of companies competing mainly on CX. Predictive process monitoring can profoundly impact CX by:
Foreseeing Customer Pain Points: Detect steps that may frustrate customers
Proactive Interventions: Notify, accommodate, reroute to avoid pain points
Continuous Tuning: Monitor CX metrics to refine processes
For example, a healthcare system used predictions to avoid unnecessary patient appointments. By foreseeing a likely ultrasound order, they scheduled it proactively rather than making patients return after their initial physician visit. Small tweaks like this add up to better experiences.
4. Empower Data-Driven Decisions
Reliable process forecasts enable leaders to base decisions on real data versus intuition. As Jack Welch said, "In business, you don‘t get what you deserve, you get what you negotiate."
Predictive insights help leaders negotiate with reality by providing:
- Volume and demand forecasts
- Cycle time and throughput projections
- Resource utilization predictions
- Estimates of plan vs. actual
These metrics quantify expected process performance. Leaders can leverage these KPIs to guide process improvement initiatives, technology investments, budgeting, and strategy.
For example, supply chain organizations can leverage demand forecasting models to optimize inventory levels precisely. Data-driven decisions minimize risks and lead to better business outcomes.
5. Trigger Prescriptive Actions
Mature predictive process monitoring systems can prescribe corrective actions based on forecasts. By combining predictions with business logic, organizations can:
- Proactively reassign stuck tasks
- Dynamically reallocate resources
- Initiate customer notifications
- Trigger process workflow changes
- Take other prescribed steps automatically
This transforms process mining from passive insights to active optimization. For example, when predictions indicated an order would miss its service level agreement (SLA), a delivery firm automatically notified customers and upgraded the shipment to express. Small prescriptive tweaks like this optimize outcomes.
Predictive capabilities unlock prescriptive process optimization.
Key Takeaways
Here are the main benefits that predictive process monitoring brings to the table:
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Identifies process inefficiency and waste in real-time
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Allows data-driven planning of resources and capacity
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Foresees customer pain points to improve experience
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Provides process KPI forecasts to guide better decisions
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Triggers automated prescriptive actions to optimize processes
These capabilities make predictive monitoring a must-have capability for any organization seeking continuous process improvement. While still evolving, adoption will accelerate rapidly as machine learning models grow more accurate and the technology matures.
Interested in learning more? Register for our upcoming webinar on best practices for implementing predictive process monitoring.
