Client is the pioneer in the field of Ring Travellers manufacturing in India. They started their operation in 1974 as a trendsetter in the highly sophisticated and accurate sphere of Traveller manufacturing. They have three units for manufacturing Ring Travellers first unit in Hosur, India second unit in Anamalai near Coimbatore and third unit in Coimbatore, India.

Client is part of India’s largest textile machinery and CNC Machine Tool manufacturer. As part of this dynamic group client has enormous resources technology, expertise and experience at its command.

Market Presence: Client supplies its ring travellers to over 40 countries worldwide.

Client plant team was experiencing frequent machine breakdowns, leading to high maintenance costs and production downtime.

They were using a traditional maintenance approach that involved regularly scheduled maintenance and reactive maintenance when a machine failed.

This approach was not effective in preventing unexpected breakdowns and was leading to significant production losses.

The company implemented an Hiotron Industry 4.0 predictive maintenance solution using machine vibration, temperature, and noise monitoring sensors.

The sensors were installed on critical machines, and the data was collected in real-time and analyzed using advanced analytics algorithms to predict potential machine failures.

The predictive maintenance solution also included an automated alert system that notified maintenance technicians when a machine required maintenance.

The technicians could then schedule the required maintenance during scheduled downtime to avoid unplanned production outages.

The implementation of the industry 4.0 predictive maintenance solution resulted in several significant benefits for the client:

• Reduced downtime: The predictive maintenance solution helped identify potential machine failures before they occurred, allowing maintenance technicians to address the issues before they led to unplanned downtime.
• Improved maintenance efficiency: With the automated alert system, maintenance technicians could prioritize maintenance activities and schedule them during scheduled downtime, leading to improved maintenance efficiency.
• Cost savings: By identifying potential machine failures early, the predictive maintenance solution helped reduce maintenance costs and avoid costly machine breakdowns.
• Increased productivity: With reduced downtime and improved maintenance efficiency, the manufacturing company was able to increase productivity and meet production targets more consistently.