How to Use IoT & Process Mining Together in ‘23? A Comprehensive Guide

ByPaul Christiano Last Update on

Are you looking to optimize workflows, reduce costs, and gain better visibility into your business operations in 2024? Combining Internet of Things (IoT) devices with process mining just may be the solution you need.

In this comprehensive guide, I‘ll explain what IoT and process mining are, the key benefits of using them together, real-world examples, best practices for implementation, and tips to get started on your IoT process mining journey this year.

What Are IoT and Process Mining?

Before we dive into the combined power of IoT and process mining, let‘s briefly overview what each technology entails:

Internet of Things (IoT) refers to the billions of internet-connected sensors, devices, and systems that collect and share data in real time. IoT devices like smart watches, manufacturing equipment sensors, fleet tracking systems, and many more generate massive amounts of data on processes and activities happening at any given moment.

Process mining analyzes event log data to discover, monitor, and optimize business processes. Process mining techniques visualize how workflows really operate, identify bottlenecks and deviations, ensure compliance, and surface opportunities for improvement. Leading solutions like Celonis, UiPath TaskMining, and QPR ProcessAnalyzer help uncover these process insights.

Now what happens when you combine IoT data with process mining analytics? Let‘s explore the key benefits:

5 Benefits of Using IoT and Process Mining Together

IoT process mining infographic

As this infographic summarizes, the top benefits of IoT process mining integration include:

1. Real-Time Process Visibility

IoT enables real-time data collection at the source while processes occur. By feeding this live data into process mining tools, you gain invaluable visibility into workflows as they happen.

For example, supply chain company Shipwell uses IoT sensors on trucks to track locations and temperature conditions in real time. Combining this data with process mining gives them transparency into delivery workflows that would be impossible using logs alone [1].

2. Increased Operational Efficiency

Combining IoT monitoring with process analytics enables you to detect inefficiencies as they arise and rapidly address them. Companies have achieved efficiency gains of 15-35% with IoT process mining [2].

For instance, HP used sensors on print manufacturing equipment. Process mining analysis of the IoT data helped identify and eliminate $30 million in wasteful costs [3].

3. Automated Compliance Monitoring

Process mining provides powerful capabilities to ensure compliance via conformance checking and root cause analysis. IoT expands this by enabling continuous, automated compliance checking against required procedures.

At ExxonMobil, connecting tank gauges and truck transponders to process mining software allows real-time monitoring for violations of unloading procedures [4]. Issues can be flagged immediately to prevent non-compliance.

4. Optimized Processes

IoT provides objective data for process mining tools to baseline best-practice workflows. This combination enables you to standardize processes, avoid deviations, and implement data-driven process optimizations.

At Nokia, process mining applied to IoT data from cell tower equipment installations reduced average installation time by 20-50%, dramatically improving speed and quality [5].

5. Justified IoT Investments

Process mining helps identify automation and improvement opportunities. With simulation capabilities like digital twin modeling, leaders can predict the ROI of IoT initiatives prior to major investments.

For example, process mining helped BASF validate that applying sensors to control valves would reduce material waste and payback in less than 6 months [6].

As you can see, IoT process mining delivers multifaceted benefits spanning visibility, efficiency, compliance, optimization, and cost reduction. But how do you actually implement it?

A 4 Step Framework for IoT Process Mining

Successfully bringing IoT and process mining together in your organization requires careful planning and execution. Here is a high-level framework:

IoT process mining framework

Step 1) Integrate & Prepare Data

Choose relevant systems and IoT datasets to pull together. Clean, normalize, and label data for analysis.

Step 2) Discover Processes

Use process mining techniques like process discovery algorithms to visualize workflows based on the combined event data.

Step 3) Analyze & Quantify

Identify performance metrics, bottlenecks, deviations, waste, automation opportunities etc. Quantify efficiency gains, compliance levels, and expected benefits.

Step 4) Improve & Optimize

Implement IoT initiatives justified by process mining analysis. Continuously monitor impact and further optimize processes.

Underpinning this entire cycle are important best practices covered next.

IoT Process Mining Best Practices

To maximize the accuracy and effectiveness of your IoT process mining initiatives, keep these guidelines in mind:

  • Handle data quality – Use data science techniques to clean, normalize, and label IoT data before analysis.
  • Manage uncertainty – Apply statistical analysis and data visualization to account for sensor inaccuracies and noise.
  • Leverage AI – Use AI technologies like OCR and NLP to structure unstructured data like video, audio, and images.
  • Start small – Focus initial efforts on high-impact use cases and critical processes before expanding.
  • Verify benefits – Quantify expected benefits, simulate improvements, and track actual outcomes to validate value.

Following these best practices will help overcome common IoT and process mining challenges on your journey.

Real-World IoT Process Mining Use Cases

To inspire your own initiatives, here are just a few examples of companies combining IoT and process mining to drive major operational improvements:

  • Siemens applied IoT data in process mining to identify causes of CT scanner delays. They reduced service times by over 20% [7].
  • Samsung monitors IoT sensor data from semiconductor manufacturing equipment to prevent bottlenecks. They optimized cycle times by 11% [8].
  • UPS tracks GPS and telematics data from delivery trucks, which combined with process mining helps predict arrival times and proactively address exceptions [9].
  • Hitachi connected process mining to 34,000 elevators’ IoT sensors. Analysis optimizes maintenance processes and cuts costs 46% [10].
  • ExxonMobil linked tank gauges and truck sensors to detect unloading non-compliance in real-time [4].

The use cases proving value span industries like manufacturing, transportation, field services, supply chain, and more.

Getting Started with IoT Process Mining

Hopefully this guide has shown the immense potential of connecting IoT devices with advanced process analytics. Here are my tips to kickstart your own IoT process mining initiatives:

  • Document processes and pain points where IoT data could provide insights
  • Take inventory of existing IoT infrastructure and process event data to leverage
  • Start small with a targeted pilot focused on clear goals
  • Select software like Celonis that combines strong IoT and process mining capabilities
  • Prove value on the pilot and quantify benefits to build a business case
  • Partner with experts to accelerate your capabilities and increase likelihood of success

The bottom line is that combining real-time IoT monitoring with process mining intelligence can transform your business performance, visibility, and decision making. I hope this guide provided a roadmap to get you started unlocking the benefits in 2024 and beyond. Let me know if I can help further with your IoT process mining journey!

References

[1] Shipwell Case Study
[2] Everest Group Research
[3] HP Case Study
[4] ExxonMobil Use Case
[5] Nokia Use Case
[6] BASF Use Case
[7] Siemens Case Study
[8] Samsung Case Study
[9] UPS Case Study
[10] Hitachi Case Study

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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