- Functional Applications - Computerized Maintenance Management Systems (CMMS)
- Functional Applications - Manufacturing Execution Systems (MES)
- Equipment & Machinery
- Additive Manufacturing
- Digital Thread
- Hardware Design & Engineering Services
- System Integration
Service Thread, a leading American manufacturer of commercial thread and yarn, faced significant challenges in monitoring and analyzing their factory performance. With over 3,000 spindles across 115,000ft2 of floor space and 24 different machine types, the company struggled to effectively determine the actual utilization percentage of their factory. The traditional method of frequent and selective in-person inspections of the machines was not only time-consuming but also lacked accuracy. The company was in dire need of a solution that could provide real-time, accurate data on machine utilization to help them make informed business decisions and optimize their operations.
About The Customer
Service Thread is a leading American manufacturer of commercial thread and yarn. The company operates a large factory in Laurinburg, NC, which houses over 3,000 spindles across 115,000ft2 of floor space and 24 different machine types. Service Thread is committed to optimizing its operations and was seeking a solution to effectively monitor and analyze its factory performance. The company's goal was to increase efficiencies, reduce costs, and fully optimize their production.
Service Thread partnered with Logical Advantage to implement an IoT-based monitoring solution using the Particle Photon. This hardware solution was designed to gather data from the machines’ existing sensors, capable of monitoring 16 spindles simultaneously. The collected data was then transmitted through the Particle Cloud and onto Microsoft’s Azure IoT Hub using Wi-Fi. Logical Advantage also developed a mobile app for spindle sensor provisioning. This IoT solution provided Service Thread with real-time data on machine utilization, enabling them to identify inefficiencies and make informed decisions on equipment purchases, labor allocation, and overall operational optimization.
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