How to Use IoT & Process Mining Together in '24?

IoT systems and devices, abbreviation for Internet of things, offers real-time monitoring, improves efficiency, and provides accurate analysis in many fields, such as in manufacturing, smart cities, smart logistic, banking, and more. IoT applications, such as pgathering and exchanging large swaths of data means that in the future, more process mining vendors could integrate their platforms with IoT.

In that light, we provide a detailed list of 5 use cases, where business leaders can leverage process mining and IoT to pair the advantages of both tools together. Moreover, we included 3 best practices to tackle the most common challenges while integrating these two technologies.

Capture real-time process data

Process mining techniques extract and analyze event log data, generated in IT systems and web applications, to identify the activities and stakeholders that are involved the process. Thus, process mining capabilities such as performance analysis and conformance checks helps shed light on the context of the event, their execution time, and their overall impacts over business performances.

IoT has the potential to enhance these tasks by automatically collecting data in real time while the process is executed through embedded sensors, cameras, and other related technologies.

Process mining can leverage this large amount of generated data to visualize operational workflows, identify deviations, or pinpoint areas for improvement. And by doing so, it enables an effective usage of IoT sensor data, while providing analysts more data than ever before to understand their business processes better.

Increase efficiency & process control 

Business analysts can utilize process mining with IoT devices to determine whether there are any performance variations in their ongoing operations. As a result, the analysts can address the variations when they occur, thereby increasing efficiency in task processing and process control.

Enable automated & real-time compliance

Process mining tools are preferred for compliance and auditing use cases due to conformance checks and root cause analysis functionalities. Conformance checks indicate if the processes are complying with the reference models or the defined procedures while root cause analysis reveal the root causes behind the deviations.

By integrating IoT devices with process mining, the analysts can compare the actual processes against the operation procedures and parameters to measure the conformance rate automatically. Analysts can also leverage process mining to set a threshold for deviations in the IoT devices to detect any moment that the threshold is crossed, to notify the employees and departments to address it. This will ensure an automated operational compliance and quality control.

Standardize your processes & avoid deviations 

Process mining can assist business analysts to standardize their operations and develop data-driven strategies. IoT improves this capability of process mining by delivering objective insights on paths that achieve higher efficiency with lower costs, which can serve for standardizing operations.

Business analysts can also use the actual data in IoT sensors to identify the root causes behind deviations, assess their impact on the process performance, and ultimately to avoid them. 

Identify and measure the IoT investment projects

Business analysts deploy process mining to determine processes that require automation or any other improvement in a data-driven manner. They can also employ process mining while deciding areas to implement IoT in. 

Some process mining tools enable users to generate a digital twin of an organization (DTO) to simulate counter-factual scenarios in order to see the potential changes in the process and to estimate their ROI.

By employing process mining and DTO, business leaders can predict and assess the financial and operational impact of IoT implementation prior a financial commitment.

A relevant case study

The following case study embodies all aforementioned use cases thus far:

Siemens Healthineers in Germany applied IoT data for understanding their digitalization project of computed tomography services¹. Siemens merged and analyzed IoT data and event logs data gathered from IT systems to identify the causes of long lead times, process deviations, and bottlenecks.

With process mining performance analysis, Siemens managed to standardize their processes and achieve harmonization. By pairing IoT with process mining, Siemens was able to assess the level and impact of AI algorithms applications in their processes by evaluating automation rates and monitor the applications.

Best practices for process mining & IoT implementation

Below you can find the top 3 best practices we recommend for businesses that want to deploy process mining for IoT-generated data. With these practices, you can enhance the accuracy of your models.

1. Implement data science to improve data quality

IoT data can deliver specific information about the activity, such as its time and duration. But it’s insufficient to immediately apply process mining to make sense of the data. Therefore, business analysts need a step in between gathering and analyzing the IoT data to improve data quality.

They can implement data science tools to aggregate and annotate the data with appropriate activity labels to prepare the data for process mining. 

2. Visualize the data to deal with uncertainty 

Sensor limitations restrict sensor reading, such as, temperature reading and GPS positions, which can cause uncertainty and low data quality. Thus, timestamps can be affected by local clock time changes, which is another uncertainty.  

Analysts can use petri nets, Directly-follows Graph other behavioral graphs to handle such uncertainty while discovering process models in the data extracted from IoT devices. Also, conformance check can be helpful to compare process models and select the best fit for such data.

3. Leverage AI to transform the data format

IoT data is heterogeneous which means it is structured, semi-structured or unstructured due to extraction of images, videos or sensor readings. Unstructured data is considered as a challenge for traditional process mining tools.

However, new software includes ML algorithms, such as Fuzzy Miner, which integrates with OCR and NLP to transform the unstructured data formats for the process discovery phase.

Further reading

To learn more on process mining integrations with other solutions and technologies, you can read:

• Top 3 Predictive Process Mining Use Cases
• 4 Ways Process Mining Uses Automated Root Cause Analysis

If you believe your business can benefit from process mining, you can start reviewing process mining software on our comprehensive, data-driven list.

Check out comprehensive and constantly updated list of process mining case studies to find out more IoT process mining real-life examples.

Moreover, to leverage IoT functionality, head over to our IoT hub, where you’ll find data-driven lists of vendors in various IoT domains, such as IoT software and IoT security solutions.

And, if you need help from us to find the right vendor for you:

Sources

1. Siemens’ case study

• 1. Bertrand, Y., De Weerdt, J., & Serral, E. (2021, October). “A bridging model for process mining and IoT.” In International Conference on Process Mining (pp. 98-110). Cham: Springer International Publishing.