Download PDF
General Electric > Case Studies > The Convergence of Predictive and Preventative Maintenance for Mill Reliability
General Electric Logo

The Convergence of Predictive and Preventative Maintenance for Mill Reliability

 The Convergence of Predictive and Preventative Maintenance for Mill Reliability - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
  • Metals
Applicable Functions
  • Maintenance
Use Cases
  • Asset Health Management (AHM)
  • Predictive Maintenance
The Challenge

Gerdau was looking to reduce their annual maintenance spend while also improving productivity, thus targeting margin improvements within their manufacturing operations. 

The Customer
Gerdau
About The Customer
A leading steel manufacturer
The Solution

Gerdau partnered with GE to implement some of our industrial solutions, including Asset Performance Management (APM) and Historian, as well as leverage GE's Industrial Managed Services for remote monitoring—allowing them to focus on their critical subset of assets using predictive diagnostics. This technology enabled Gerdau to model their expected performance of each individual asset and its failure modes against actual conditions of various measurement points on an asset or system. 

Operational Impact
  • [Efficiency Improvement - OEE]

    Early identification of issues to allow significant time to plan the appropriate PM activities to avoid functional failures. 

Quantitative Benefit
  • Avoided130 hours of downtime in their Sinter plant

  • Annual savings from their new capabilities are expected be $4.5 million per year

Related Case Studies.

Contact us

Let's talk!

* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.
Submit

Thank you for your message!
We will contact you soon.