TensorIoT
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TensorIoT

The Garland Company Unlocks Industrial Data with TensorIoT SmartInsights

By

Nicholas Burden, Senior Technical Evangelist at TensorIoT, John Traynor, VP & GM Products & Solutions at TensorIoT and Derek Scavuzzo, Quality Assurance Leader at The Garland Company

The Internet of Things (IoT) collectively describes the use of sensors and software in physical objects to connect and exchange data with other systems (or devices) over the internet, in the cloud, or on the edge. One of the industries seeing major shifts thanks to IoT is manufacturing. Industrial IoT (IIoT) is the term used to describe the application of IoT technology in industrial settings, including sensor and device controls linked to cloud technologies for data capture and analysis. IoT connectivity unlocks new opportunities for businesses to collect data, and one of the challenges faced by companies is figuring out how to correctly use and interpret data. To help manufacturing companies make the most of their gathered data, AWS Advanced Consulting Partner, TensorIoT, developed SmartInsights, a customizable end-to-end solution that industrial and commercial customers use to rapidly connect and derive actionable insights from operational systems. Today, we’ll be looking at how The Garland Company used TensorIoT SmartInsights to quickly realize a 15% energy savings and 17% throughput increase in one of their manufacturing processes only a few short weeks after SmartInsights was first deployed.

The Garland Company has been a leading provider of commercial roofing and building envelope solutions for over 125 years. The company produces materials used in commercial, industrial, and public buildings in multiple countries across the world. Given its reputation for leading performance within the industry, Garland continually pursues innovation to develop the highest quality products and improve internal processes.

One product line Garland manufactures is bituminous roofing membranes, and one of those product manufacturing lines was identified as a candidate for process improvement. In order to improve Garland’s operations, the goal for TensorIoT was to make the wealth of telemetry data coming from the roofing membrane manufacturing line more accessible and actionable through SmartInsights. 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.

By deploying SmartInsights, Garland’s operations staff had immediate visibility into any variations in the production process. With all key process data visible in a single pane of glass, production team members could easily see anomalies and take immediate action to adjust equipment and the production process.

Derek Scavuzzo leads Quality Assurance at Garland’s Cleveland plant and works with counterparts at other manufacturing sites. With a multitude of manufacturing sites and processes, a successful SmartInsights launch offered Garland the opportunity to bring similar improvements across their locations.

After adding SmartInsights to their production line, Derek Scavuzzo at Garland used SmartInsights as a “way to visualize baseline data to check for changes to various tags (e.g. mixer speed) and identify causes of downtime or improve uptime.” SmartInsights also provides an intuitive plant overview to allow leadership comprehensive visibility of their manufacturing processes.

When examining plant processes using SmartInsights, the controls engineer noticed an anomaly in one mixing tank, as compared to another identical machine. Their maintenance team fixed the malfunctioning mixer, which saved about 17% of processing time per batch and 15% energy savings. Now, Garland can mix more bituminous material in a day with less energy use and less waste, resulting in improved quality and throughput.

In a future deployment phase, SmartInsights will route data through machine learning services to spot more anomalies in the production process. The production team will then be able to address even more issues before they can impact production quality or throughput, further improving operational efficiency and increasing product quality.

Watch our video with an overview of this case study here!

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