- Deployment creates an unprecedented level of streaming, cloud-based global insights into machine utilization and overall equipment effectiveness
- Arundo software identifies opportunities for Coats to improve operational performance
- Arundo deployment is part of Coats global ‘Factory of the Future’ initiative, which aims to connect and improve operations internally and across the value chain
London – Arundo Analytics, a software company enabling advanced analytics in heavy industry, today announced the successful deployment of Arundo software to improve manufacturing operations at Coats, the world’s leading industrial thread manufacturer.
“Coats has been a pioneer in industrial thread manufacturing for over 250 years,” said Edoardo Jacucci, Arundo General Manager EMEA. “We are excited to work with them as they continue to lead their industry by embracing IoT implementations for advanced analytics.”
The initial deployment of Arundo’s software collects over 800 machine signals at 1hZ intervals at the Coats manufacturing site in Shenzhen, China, in conjunction with operator, job, shift and site data. This access will create an unprecedented level of streaming, cloud-based global insights into machine utilization and overall equipment effectiveness. Based on the detailed granularity of these insights, concrete improvement actions can be rolled out across the business.
Coats has kicked off an ambitious global ‘Factory of the Future’ initiative, with the aim to connect and improve operations internally and across the value chain through machine learning and streaming data analytics. With 19,000 employees in over 50 countries, Coats operates a global supply chain and production operation across six continents.
“Our goal is to lead our industry by adopting and embracing the new technologies that may fundamentally change the way we do business,” said Hizmy Hassen, Chief Digital and Technology Officer, Coats. “Arundo provides deep data science, software and industrial domain expertise that can identify opportunities for us to improve operational performance.”