Benchmark Electronics · Manufacturing · San Antonio, TX · 12 weeks
OEE monitoring automated across 6 plants
A precision parts manufacturer with six plants relied on spreadsheets to track Overall Equipment Effectiveness, causing a two-day delay in reporting downtime events. We developed an automated OEE monitoring system that uncovered $3.7 million in annual recoverable downtime and cut unplanned stoppages by 31 hours each month.
Challenge
The manufacturer relied on spreadsheets to track OEE, causing a two-day delay before downtime showed up in reports. Plant managers couldn’t act quickly because the data was always outdated. They suspected $3M to $5M in lost production annually but lacked accurate measurements to confirm or address the issue.
Approach
We connected machine data from six plants using OPC-UA, then calculated OEE in real time. We created a Tableau dashboard that highlighted downtime patterns and identified root causes. We also developed predictive maintenance alerts based on anomalies in vibration, temperature, and cycle time to catch failures early.
Outcome
We cut OEE data delays from two days to 15 minutes, enabling faster decision-making. By analyzing data across six plants, we uncovered $3.7 million in avoidable downtime annually. In the first three months, unplanned stoppages dropped by 31 hours each month. Predictive maintenance alerts caught four major equipment failures within six months, preventing costly shutdowns.
How We Got OPC-UA Connectors Talking and Data Moving Fast
Here’s the deal: those six plants were all running different machine control systems, four in total. So, we rolled up our sleeves and built OPC-UA connectors for each one. That let us grab machine data directly and keep it consistent across the board. The kicker? We ditched that painful 48-hour lag from manual data pulls. Everything just clicked after that.
How we slashed downtime and saved big with predictive maintenance
Real-time OEE tracking gave us some good data, but the real win was the predictive maintenance alerts. They flagged odd spikes in vibration, temperature, and cycle times way before things got serious. Thanks to that early warning, we fixed problems before they became full-blown breakdowns. Over six months, that helped us avoid four major shutdowns and saved between 8 and 16 days of surprise downtime.
Getting Plant Managers to Use the System, No Headaches, No Hassle
Plant managers are swamped, right? They don’t have time to sift through messy reports. So, we built a dashboard that’s super simple. Each machine gets a health score from 0 to 100. We threw in some color coding, red, yellow, green, so they can instantly see which machines are acting up. No guesswork. No stress. Just clear, quick info.
Results
- $3.7M Recoverable downtime identified annually
- 31 hrs/mo Unplanned stoppages reduced
- 48 hrs to 15 min OEE data lag
- 12 wks All 6 plants live
We knew some downtime was recoverable, but we didn’t have a clear number on the cost. It’s about $3.7 million a year. Since we started using the predictive maintenance alerts, we’ve prevented four major failures. The system pays for itself every month.