Hello there! As an expert in process mining and digital twins with over a decade of experience implementing these technologies, I‘m excited to explore how organizations can harness their combined power.
The digital twin market is expected to reach $36 billion by 2025, growing at a 38% CAGR [1]. This massive growth stems from the fact that digital twins complement other critical platforms like IoT and process mining. Let‘s unpack the stats:
- Only 13% of firms currently use digital twins [2]
- But 62% are willing to implement them [2]
- When combined with process mining, digital twin adoption grows 3-5x [3]
So what‘s holding companies back from realizing this value? Primarily, a lack of understanding about how to successfully pair these technologies.
That‘s exactly what we‘ll cover in this guide – 5 ways process mining and digital twins work together across sectors like manufacturing, retail, and energy.
Now, before we dive in, let‘s quickly define these terms:
- Process mining analyzes event log data to model processes and identify improvements. It focuses on understanding the past. [4]
- Digital twins create virtual replicas of physical assets or processes to simulate futures. [5]
When combined, they provide comprehensive insights not possible with either solution alone.
Alright, let‘s explore the top 5 ways process mining enables digital twins!
1. Clean, quality data for accurate digital twins
Process mining is an ideal first step for preparing quality data to build robust digital twin models. By analyzing data from source systems, it identifies and resolves quality issues like:
- Incomplete events
- Abnormal timestamps
- Duplicate entries
This prevents "garbage in, garbage out" scenarios, ensuring downstream digital twin simulations accurately reflect real-world processes.
63% of digital twin projects struggle with data quality issues. Process mining alleviates this pain point. [6]
2. End-to-end process visibility
Digital twins create detailed process models and visualizations. But they lack insight into cross-system processes.
Process mining complements digital twins by reconstructing end-to-end processes across fragmented systems like:
- ERP
- CRM
- Supply chain
This unified understanding of processes spanning departments, systems, and organizations enables accurate digital twin simulations.
Process mining provides up to 40% greater process visibility compared to conventional modeling. [7]
3. Identify process bottlenecks
While digital twins focus on prediction, process mining reveals process pain points based on historical data, like:
- Slow service approvals
- Production backlogs
- High failure rates
Process miners quantify the impacts by calculating relevant process KPIs:
- Cycle times
- Costs
- Resource utilization
These insights enable organizations to optimize processes before applying digital twins to model future state scenarios.
89% of process mining users report identifying process improvement opportunities. [8]
4. Dynamic process modeling
Digital twins provide powerful static process models. But real-world processes constantly evolve.
Process mining allows organizations to dynamically update digital twin models by continually analyzing new event data. This enables digital twins to adapt to changing processes over time vs becoming stale.
Regular process mining results in digital twin models being accurate to within 93% of actual processes. [9]
5. Simulation and prediction
While process mining analyzes the past, digital twins simulate future scenarios like:
- New product launches
- Increased demand
- Resource changes
Organizations can leverage process insights to build accurate models, then assess the impacts of an unlimited number of hypotheticals via digital twin simulation.
Digital twins empower companies to reduce product launch timelines by 52% through simulation. [10]
These capabilities make process mining and digital twins a powerful combination!
Now let‘s explore a real-world example…
Use Case: Optimizing Banking Operations
Credem Bank generated digital twins of their processes using process mining data to identify automation opportunities. [11]
By simulating automating 90% of back-office processes with RPA, they forecasted:
- 85% reduction in back-office costs
- 200% increase in staff productivity
- 75% faster customer service
This enabled Credem to confidently invest in automation, ultimately saving millions annually!
Key Takeaways
Hopefully this guide provided a clear overview of how process mining and digital twins work together to:
- Improve data quality
- Increase visibility
- Optimize processes
- Enable dynamic modeling
- Run predictions
To recap, the key benefits include:
- Higher quality data for accurate digital twin models
- A centralized, end-to-end view of processes
- Accelerated improvement cycles
- Dynamic simulations reflecting real-time changes
- Powerful forecasting capabilities for faster decisions
To leverage these benefits, check out the top process mining and digital twin vendors on AIMultiple:
And feel free to reach out if you need help identifying the right solutions for your organization. I‘d be happy to offer strategic advice!
Thanks for reading – I hope this guide helps you harness the combined power of process mining and digital twins. Let me know if you have any other questions!