energy used by industry in the U.S.
estimated energy waste in industrial manufacturing
global roofing market size in 2021
By using TensorIoT SmartInsights, The Garland Company improved its manufacturing process line resulting in a 17% increase in throughput and a 15% decrease in energy use.
How can you improve manufacturing processes using IoT for visualization and data analysis?
Garland discovered changes leading to a 15% energy savings and half hour time reduction, enabling them to mix more batches in a day with less energy use and less waste, resulting in improved quality and throughput.
The Garland Company has been a leading provider of commercial roofing and building envelope solutions for over 125 years. Like any market leader, Garland pursues innovation to develop the highest quality products and improve internal processes. One product that Garland manufactures is bituminous roofing membranes, a product manufacturing line that the company identified as a candidate for process improvement.
To improve the manufacturing process, Garland needed a way to access and make use of the large amount of process data generated during each shift. The production process involves many steps, including the mixing of polymers with asphalt and limestone. Without a way to visualize and analyze live data directly during the manufacturing process, opportunities for improvement would not be immediately apparent to the production and engineering teams. TensorIot took on the challenge of making the wealth of telemetry from the manufacturing line more accessible and actionable with SmartInsights, a pre-built customizable solution directly deployed in Garland’s own cloud environment.
In order to better understand the process and facilitate root cause analysis, telemetry readings from across the line needed to be liberated from the underlying hardware and organized in a central dashboard, making SmartInsights the perfect solution for analysis and visualization. SmartInsights is built on AWS, utilizing the power of AWS IoT SiteWise to gather data from connected devices, then creating a robust scalable database with Amazon DynamoDB to allow Amazon OpenSearch to search, aggregate, view, and analyze data. OpenSearch visualizations were embedded into the SmartInsights platform for Garland to have a single pane glass view of their machine telemetry.
When examining plant processes using SmartInsights, the QA Manager noticed that the mix times were taking longer than desired, and that a mixer had lower amperage than an identical machine. This insight helped save 30 minutes of time in a 3 hour mix time.