In our last IoT blog post, we discussed the technology and infrastructure to start collecting data. We’ll now dive into how we utilize advanced analytics techniques to turn IoT data into business value. There are many use cases for IoT data including: real-time monitoring, preventative maintenance, machine configuration, and defect detection. These uses often drive resiliency in production, cost savings, increased machine utilization, and an overall improved production process. 

We helped a local client improve operational efficiency through analysis of IoT data.

We went through this process with a regional client in the metals industry operating a $2B+ revenue facility. It started with questions including: How can we best utilize the high-resolution data we’re collecting? How can we improve machine efficiencies and reduce part defects? Is there a manual process that can be done better utilizing this data?  

We focused on the thickness gauge between 2 critical machines in their process and sought to improve the machine configuration to best account for gauge differences among them. The machine data was pulled from a cloud data lake and included 3 data sets with records at a frequency of 100ms. This data included the gauges, temperatures, machine settings, and other metadata on the part being ran. After heavy pre-processing of the data to prep for analysis, we trained a machine learning model to determine a key setting to dynamically adjust while the machine was running.  

Predicted values from the machine learning model are able to better reflect reality than the manual machine configuration process.

 

Implementing this model led to a potential 30% accuracy improvement on the gauge adjustment compared to the manual machine configuration process. It led to decreased part holds due to incorrect calibration, leading to less defects and machine downtime. Additionally, it saves 3 hours of work per week spent adjusting and updating machine code. 

 

This was just one example of how to utilize analytics to drive business value. Other approaches include: 

  • Artificial intelligence to anticipate maintenance needs and resource allocation 
  • Optimization and simulation to prescribe the most efficient routes for delivery trucks 
  • Anomaly detection to identify deviations from expected patterns such as part defects 

IoT data analytics can add value throughout your business, and we can help you identify these opportunities and see it through to tangible ROI. 

Give us a call!

AMEND has a proven track record of being an IoT leader and delivering a competitive advantage by creating a strategy and seeing it through to implementation. With a focus on speed and efficiency, we enable businesses to quickly implement and validate IoT solutions, ensuring rapid returns on investment. If you’re interested in learning more, please request more information below! 

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