The Internet of Things (IoT) is exploding. There are currently over 10 billion connected IoT devices worldwide, generating vast amounts of data every second. And Gartner predicts enterprises will use 66% more IoT endpoints by 2023.
But how can organizations actually derive value from torrents of IoT data? This is where process mining comes in.
As an expert in data analytics and process optimization with over 10 years of experience, I see the integration of IoT and process mining as the future. In this 3,500 word guide, I‘ll explore how to leverage process mining to capitalize on IoT investments and accelerate data-driven transformation.
The Rise of IoT and a Data Deluge
First, let‘s examine the scale of IoT expansion. According to IDC, global IoT spending will reach $1.2 trillion in 2024 as adoption grows across industries:
Manufacturing, transportation, and utilities make up over 50% of IoT spending, but expansion is also seen in:
- Retail – predicting demand, inventory management
- Healthcare – remote patient monitoring devices
- Smart cities – traffic optimization sensors, pollution monitors
- Agriculture – tracking microclimates, soil conditions
And this is just the beginning. IoT-enabled smart devices will number 75 billion by 2025 according to IHS Markit.
The challenge is translating enormous volumes of IoT data into tangible business value – a task perfect for process mining.
What is Process Mining?
Process mining uses data science algorithms to analyze business process event logs from IT systems. It helps uncover:
- Process bottlenecks and deviations
- Automation opportunities
- Root causes of issues
- Ways to optimize workflows
By visualizing processes and quantifying variations, process mining drives process excellence. It‘s estimated the process mining software market will reach $14.3 billion by 2030.
Blending Real-Time IoT Data with Process Mining
Traditionally, process mining is used to mine historical event log data from company databases and ERPs to model as-is processes.
IoT allows tapping into live, real-time data from connected devices and systems. This enables:
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True dynamic process discovery – Rapidly adapt process models by analyzing streams of IoT events as they occur vs periodic snapshots
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Early anomaly detection – Identify deviations and bottlenecks in near real-time instead of after-the-fact
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Continuous process improvement – Constantly fine-tune processes vs periodic optimizations
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Mining high-frequency transactions – Analyze processes involving many IoT events per second, like supply chains
To handle massive IoT data flows, process mining employs techniques like:
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Online process discovery algorithms – Incrementally analyze events in small batches vs processing after data collection
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Streaming analytics – Analyze live feeds and adapt to concept drift in processes over time
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Distributed computing – Utilize clusters to parallel process high-volume IoT data
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Edge analytics – Perform real-time analysis on local devices before transmitting data
Top Use Cases and Examples
Let‘s explore the top applications of converging IoT and process mining to improve business performance:
1. Smart Manufacturing Optimization
IoT sensors across factory equipment and the production environment, combined with supply chain data, can help optimize manufacturing.
Process mining provides contextual analysis to turn IoT data into operational insights. Companies like Siemens are already utilizing this combination:
Metrics improved by Siemens with process mining of IoT data:
- 65% faster root cause analysis
- 57% increase in quality assurance
- 48% improved productivity
Process mining illuminated the real production issues so Siemens could continuously improve.
2. Asset Monitoring and Predictive Maintenance
Combining IoT sensor data from machinery with process mining enables better asset utilization across industries like manufacturing, transportation, and energy.
By applying algorithms to sensor outputs like vibration, temperature, and torque, companies can:
- Discover equipment failure processes and predictors
- Optimize maintenance planning and scheduling
- Reduce unplanned downtime through predictive maintenance
- Assess asset health and performance patterns over time
McKinsey estimates predictive maintenance can deliver 8-10% cost savings in manufacturing, while improving uptime by 10-20%.
3. Supply Chain Visibility and Optimization
IoT tracking devices across transport vehicles, distribution centers, and inventory enable end-to-end supply chain data. Process mining helps parse the flood of events into meaningful insights. Use cases include:
- Route optimization – Analyze delivery processes to optimize routes and fuel efficiency
- Predicting delays – Flag patterns like traffic that precede late deliveries
- Inventory management – Sense stockouts/oversupply earlier to streamline orders
- Fleet management – Profile driver behaviors and vehicle usage to reduce maintenance costs
One company saw a 17% drop in fleet operating costs after applying process mining to IoT data from vehicles.
4. Improving Product Design
IoT product usage data combined with process mining can bolster R&D and customer experience. Telemetry from connected devices helps uncover:
- Usage patterns – Identify common product interactions and pain points
- Failure processes – Apply root cause analysis to detect flaws in product design
- Feature optimization – Quantify feature usage to double down on popular capabilities
- Customer segmentation – Group customers with similar usage behaviors for targeting
Software company ABBYY saw a 15% increase in product usage after analyzing IoT customer data with process mining.
5. Boosting Business Decision Making
IoT data can feed real-time dashboards, but process mining adds context for smarter decisions across the business:
- Dynamic operational KPIs – Combine IoT metrics with process context for live visibility
- Rapid response to emerging trends – Detect pattern changes in market, customer, or operational data
- Continuous auditing – Allow compliance issues to be flagged as they occur rather than in hindsight
- Predicting customer churn – Analyze telemetry data to identify usage drops that precede cancellations
With process mining, organizations move from reactive to proactive, data-driven decision making.
Best Practices for Implementation
To succeed with process mining and IoT convergence, here are critical best practices:
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Start with a focused initiative – Pilot on a defined process and use case before scaling across the enterprise. Manufacturing and logistics are ideal starting points.
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Get the right team – Include data engineers to handle IoT data collection/ingestion and data scientists for cleansing and feature engineering.
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Monitor data quality – Profile live data streams to check for completeness, noise, and outliers. Catch issues early.
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Use visualization – Visual process flows and anomaly alerts help users rapidly interpret insights from massive IoT data.
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Build in agility to adjust – Expect to refine techniques as new IoT devices get added and processes change. Adopt agile principles.
The Future of Connected Process Excellence
We‘ve only scratched the surface of what‘s possible with the fusion of IoT data and process mining intelligence. The technology is still in its early days, but real results are already emerging.
Some predictions for the road ahead:
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Exponential growth of sensors and use cases – 5G rollout and cheap IoT hardware will expand monitoring across all industries
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Rise of embedded analytics – More data processing at the edge, closer to IoT devices themselves
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AI-driven process modeling and optimization – Machine learning agents autonomously improving workflows based on IoT data
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Accelerating digital transformation efforts – Companies leaning more on technology and data-driven insights
With the deluge of IoT data only intensifying, process mining serves as a critical mechanism for filtering signal from noise to enable smart, connected organizations.
To discuss how combining IoT and process mining can accelerate your digital initiatives, feel free to connect with me on LinkedIn here: [linkedin.com/in/john-data]