Steel Industry: cold rolling process in
a corrosive environment

Are all of the maintenance
activities necessary or can
maintenance intervals
be expanded?

For MAILLIS, UPTIME platform monitors and predicts the wear on the moving parts of the equipment based on real time data, coming from a group of sensors.  Evaluating the deviation from the healthy state, Maillis becomes capable to predict future machine conditions; that way, the company’s maintenance policy relates directly to the level of the equipment’s health. At the end of the day, the cost of maintenance drops, allowing further improvements on the critical parts’ entire life cycle.

Sensors operating
in an extremely aggressive environment

Time to change over the milling rollers > 8 h leads to a big impact on the OEE

24 hours a day
running production

in other production lines.

• Reduction of failure rates: 30%
• Reduction of downtime due to repair: 10%
• Reduction of unplanned plant/production system outages: 15%
• Extension of component life: 15%
• Improvement of OEE average: by 7%
• Reduction of Total Cost of Maintenance: 20%

After the success of the first implementation, MAILLIS plans to implement UPTIME platform
in other production lines.


“Optimizing Machine healthcare along with developing-faults
detection capability, are key advantages in addition from the obvious
value of predictive maintenance”

vasilis boursinos, vice president Operations, m.j. MAILLIS S.A.