Manufacturers Optimize Asset Life and Quality Control with Flowfinity IoT Solutions
![interactive operational intelligence dashboards](/company/news/images/Factory-Worker-2-Stream-M1.webp)
April 10, 2024, Vancouver, B.C. – Flowfinity Inc., an industry leading no-code platform is introducing new solutions to help smaller manufacturers maximize uptime and reduce waste by lowering the barrier to entry for IoT integration and Operational Intelligence.
Advances in smart manufacturing are driving growth and efficiency in the sector, but so far the complexity and cost of deploying a custom solution has prevented all but the largest and most sophisticated firms from reaping the benefits.
Savvy manufacturers want streamlined production value and increased quality with reduced errors. So, Flowfinity has introduced an integrated solution to do the following:
- Stream sensor data in real-time to operational intelligence dashboards to see that everything is as it should be and receive alerts when it's not.
- Collect and analyze real-time operational data 24/7 to predict equipment failure and trigger maintenance protocols to avoid replacing machinery prematurely.
- Monitor temperature, vibration, pressure, and environmental factors critical to equipment's optimal operations, and receive anomaly notifications.
Retrofitting your existing equipment with smart sensors allows you to improve maintenance, prolong asset life, enhance quality control, reduce material waste and energy use, and improve insight into your supply chain and operations to make better informed, data-driven decisions.
Now you have everything you need to integrate and automate your processes with IoT hardware and data management workflows retrofitted throughout your facility.
"Flowfinity IoT sensor data can be combined with our Streams database optimized to collect millions of records 24/7 with an efficient storage structure so high-volume data is available for interactive visualization. This empowers managers to monitor crucial equipment in their manufacturing plant for quick response to anomalies, perform detailed analysis of historical data for preventative maintenance and continuous improvement of manufacturing operations." says Larry Wilson, VP of Sales & Marketing, Flowfinity.
If you need to have precise tracking, timing, and coordination of any movement-related processes you can achieve this by connecting the relevant sensors directly to your Flowfinity M1 controller. Fast Track IO allows you to measure this sensor activity with sub-millisecond accuracy and react to sensor inputs via discrete outputs with a near-instantaneous response time automating corrective responses saving materials and preventing down time.
Hardware and software is natively configured for an IoT asset monitoring solution so you can collect, store, and manage your data using Flowfinity Streams, which uses significantly less space than traditional relational data storage models.
For a manufacturer, monitoring sensor data from industrial equipment optimizes runtime and maintenance schedules and accumulates usage statistics and results in greater ROI through a reduction in errors and raw material waste. When a threshold is reached, Streams will pass that variable to Actions where a software robot will create a preventative maintenance work order and notify the appropriate team members.
To help managers make informed decisions, Streams data can also be visualized in interactive operational dashboards to easily see any changes in data while for conditional monitoring of equipment status. You will know precisely when a machine went offline or exceeded its optimal thresholds and for how long.
About Flowfinity
Flowfinity is an intuitive no-code platform solution for manufacturers to deliver business process applications, based on four pillars of data management: accurate data collection, interactive data visualization, workflow automation and IoT integration.
To learn how Flowfinity can help you digitize and automate your business processes, contact Flowfinity experts for a complimentary consultation.
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