A mid-size automotive parts manufacturer in the US Midwest was running at 65% OEE across its 4 production lines, losing an estimated $3.1 million annually to unplanned downtime, changeover delays, and quality rejects driven by unmaintained equipment. Line supervisors had no visibility into asset condition between scheduled maintenance windows. The maintenance team was spending 58% of its time on emergency callouts. PM compliance sat at 52% on a paper-based scheduling system that had not been updated in 5 years. Leadership knew the gap between 65% and 85% OEE was not a workforce problem. It was a data and systems problem. Sign up free on Oxmaint to see how real-time OEE dashboards and automated maintenance scheduling transform production performance, or book a demo to model the OEE improvement case for your facility.
85%
OEE achieved at Month 14 versus 65% pre-deployment baseline across all 4 production lines
$2.4M
annual saving from downtime reduction, quality improvement, and elimination of reactive repair premiums
11 mo
full deployment cost payback including software, IoT sensors, integration, and technician onboarding
94%
PM compliance rate at Month 12 versus 52% pre-deployment baseline across all asset categories
Case Summary
Before Oxmaint, this automotive parts manufacturer managed 180 production assets across 4 lines using paper PM schedules, spreadsheet-based OEE tracking, and reactive maintenance as the default operating mode. Within 14 months of Oxmaint deployment, OEE climbed from 65% to 85%, PM compliance rose from 52% to 94%, the emergency callout ratio dropped from 58% to 21%, and annual downtime cost fell by $2.4 million. The OEE improvement was driven by three factors: automated PM preventing the equipment failures that caused availability losses, IoT condition alerts catching deteriorating assets before production impact, and real-time OEE dashboards giving line supervisors and maintenance teams a shared view of performance and constraint for the first time.
The Problem: OEE Stuck at 65% With No Diagnostic Visibility
OEE of 65% on a 4-line production facility consuming $3.1 million annually in downtime cost is not a performance problem — it is a maintenance systems problem. The three OEE loss categories — availability, performance, and quality — were all traceable to maintenance failures that a structured CMMS programme would have prevented.
01
58% Emergency Callout Ratio
More than half of all maintenance events were unplanned emergency responses. Each emergency event cost 4.8 times more than the planned equivalent and generated an average of 3.2 hours of unplanned line downtime, directly reducing availability in the OEE calculation.
02
No Real-Time OEE Visibility
OEE was calculated weekly from manual production logs and estimated downtime records. By the time the number reached line supervisors, the data was 5 to 7 days old. No live view of availability, performance, or quality losses meant no real-time constraint identification or rapid response capability.
03
52% PM Compliance on Paper Schedules
Paper-based PM scheduling with no automated escalation meant nearly half of all scheduled maintenance tasks were deferred or missed. Missed lubrication on CNC spindle bearings and deferred coolant system PM were the two most frequent root causes of unplanned stoppages across all 4 lines.
04
Quality Losses From Unmaintained Tooling
Tooling wear beyond PM interval was generating a 4.2% scrap and rework rate on Line 3 and Line 4. Quality losses in OEE directly reduce the effective output available to meet production targets. No CMMS meant no tooling life tracking, no wear-based replacement triggers, and no quality event traceability back to maintenance history.
Why Oxmaint Was Selected
The plant engineering director evaluated three CMMS platforms over 45 days. Two were eliminated: one required an 8-month ERP integration and had no production-native OEE dashboards; the second had OEE reporting but no IoT sensor integration for real-time condition data. Oxmaint was selected on four decisive criteria.
Real-Time OEE Dashboard at Line Level
Live availability, performance, and quality metrics per production line updated continuously from work order closures and IoT sensor data. First live OEE view in plant history within Week 6.
IoT Condition Alerts on Critical Assets
Wireless sensors on 22 critical assets including CNC spindles, hydraulic presses, and coolant systems. Condition alerts generating work orders automatically before performance degradation hits the production floor.
Automated PM Scheduling With Production Triggers
PM work orders generated by both calendar intervals and production-based triggers including cycle counts, run hours, and units produced. Maintenance timed to planned changeover windows to eliminate PM-driven unplanned stoppages.
35-Day Deployment — No ERP Integration Required
All 180 assets registered, PM schedules configured, IoT sensors active, and technician mobile access live within 35 days. No ERP dependency, no consultant-led implementation, no downtime for system migration.
Your OEE Gap Is a Maintenance Systems Gap. Oxmaint Closes It.
This plant's 20-point OEE improvement was driven by three CMMS capabilities that did not exist before deployment: automated PM preventing availability losses, IoT alerts catching performance degradation early, and real-time dashboards giving supervisors live constraint visibility. Book a demo to model the OEE improvement case for your production facility.
Implementation: 35 Days to Live OEE Dashboard
Weeks 1 to 2
Asset Registry and PM Schedule Configuration
All 180 production and facility assets registered across 4 lines in Oxmaint. Asset hierarchy built: Line level, Machine level, Component level. PM schedules configured using both calendar intervals from OEM manuals and production-based triggers from the plant's historical cycle count data. 22 critical assets flagged for IoT monitoring: 8 CNC machining centres, 6 hydraulic presses, 4 coolant systems, and 4 conveyor drive assemblies.
Weeks 3 to 4
IoT Sensor Deployment and Mobile Technician Access
Wireless IoT sensors deployed on all 22 critical assets during a planned 48-hour maintenance window. Temperature, vibration, and pressure data feeding into Oxmaint asset records within 72 hours of sensor deployment. All 14 maintenance technicians given mobile Oxmaint access. Paper PM sheets formally retired on Day 28. Mobile work order closure rate reached 91% within the first week of mobile operation.
Day 41 — The Pivotal Moment
First Condition Alert Prevents $74,000 CNC Spindle Failure
On Day 41, Oxmaint generated a condition-triggered work order for CNC Machining Centre 3 based on anomalous vibration trending on the main spindle bearing over 72 hours. The maintenance team inspected during the next scheduled changeover window and found progressive bearing wear at 67% of maximum allowable amplitude. Planned bearing replacement cost $3,200 in parts and 4 hours of planned downtime. The avoided emergency failure was estimated at $74,000 in spindle rebuild cost, emergency contractor fees, and 3.5 days of line downtime at $8,400 per day. This single event recovered 22% of the full annual deployment cost before the end of Month 2.
Week 6
Live OEE Dashboard Active Across All 4 Lines
Oxmaint's real-time OEE dashboard went live displaying availability, performance, and quality metrics per line updated in near-real time from work order data and IoT readings. For the first time in plant history, line supervisors and maintenance managers shared a live view of OEE by line, by shift, and by asset. The first week of live data identified Line 2 as the primary OEE constraint, with a 61% availability rate driven by 3 recurring hydraulic circuit faults that PM schedules had been missing.
Months 3 to 14
OEE Climbs From 65% to 85% as PM Compliance Reaches 94%
By Month 6, OEE had reached 74% across all lines as the emergency callout ratio fell from 58% to 34%. By Month 12, PM compliance had stabilised at 94%, the emergency ratio was at 21%, and OEE reached 83% portfolio-wide. Line 2 specifically climbed from 61% to 82% availability after the 3 recurring hydraulic faults were resolved through targeted PM interval adjustment informed by OEE loss data. Month 14 OEE: 85% across all 4 lines, precisely matching the pre-deployment target.
Results: Year 1 and Month 14 Outcomes
OEE Achievement
85%
Up from 65% pre-deployment baseline across all 4 production lines
Annual Net Saving
$2.4M
Downtime avoidance, quality improvement, and reactive repair elimination
Payback Period
11 months
Full deployment cost recovered including sensors, software, and onboarding
94%
PM compliance rate at Month 12 versus 52% pre-deployment baseline
21%
Emergency callout ratio at Month 14 versus 58% pre-deployment baseline
$74K
First avoided failure value on Day 41 — CNC spindle bearing prevented
1.9%
Scrap and rework rate at Month 12 versus 4.2% pre-deployment baseline
Before and After: Key Metrics
| Metric | Before Oxmaint | After Oxmaint (Month 14) |
|---|---|---|
| Overall Equipment Effectiveness | 65% across all 4 lines — tracked weekly from manual logs | 85% across all 4 lines — tracked live from Oxmaint OEE dashboard |
| PM compliance rate | 52% — paper-based scheduling with no automated escalation | 94% — automated digital work orders with mobile closure and escalation |
| Emergency callout ratio | 58% of all maintenance events were unplanned emergency responses | 21% — more than halved through condition monitoring and structured PM |
| Scrap and rework rate | 4.2% on Lines 3 and 4 — tooling wear beyond PM interval | 1.9% across all lines — tooling life tracking and wear-based replacement |
| OEE visibility | Weekly manual calculation from production logs — 5 to 7 day lag | Live per-line dashboard updated continuously from work orders and IoT |
| Annual downtime cost | $3.1M — unplanned stoppages, emergency parts, and contractor premiums | $0.7M — planned maintenance only, no emergency contractor events in Month 14 |
Total Deployment Cost
$218,000
Software, IoT sensors, integration, and technician onboarding
Annual Net Saving
$2.4M
Downtime avoidance, quality improvement, reactive repair elimination
Full Payback Period
11 months
Deployment cost fully recovered before Month 12
"We had been running at 65% OEE for three years. We knew where the losses were — availability on Line 2, quality on Lines 3 and 4 — but we had no live data to act on and no PM system that was actually being followed. Oxmaint changed both simultaneously. The live OEE dashboard showed us exactly which asset was constraining each line in real time. The automated PM programme stopped us from deferring the maintenance that was causing most of those stoppages. By Month 12 our PM compliance was at 94% and our emergency callout rate had dropped from 58% to 21%. The 85% OEE target we had been discussing for two years arrived 2 months ahead of schedule."
Plant Engineering Director
Automotive Parts Manufacturer, US Midwest
How Oxmaint Drives OEE Improvement Across All Three Loss Categories
Availability: Automated PM Eliminates Unplanned Stoppages
Automated work orders generated by both calendar and production-based triggers. PM scheduled into changeover windows. Emergency callout ratio fell from 58% to 21% in 14 months. Every planned PM avoided an average of 3.2 hours of unplanned downtime per event.
Availability: IoT Condition Alerts Before Production Impact
Wireless sensors on 22 critical assets detecting bearing wear, hydraulic pressure loss, and coolant system degradation before failure. First alert on Day 41 prevented $74,000 CNC spindle event. Average alert-to-intervention window: 4 to 8 days of planned response time.
Performance: Real-Time OEE Dashboard With Constraint Identification
Live per-line OEE with availability, performance, and quality split visible to supervisors and maintenance teams simultaneously. Line 2 identified as primary constraint in Week 6 from live data — problem resolved in Week 10 through targeted PM interval adjustment. No more weekly lag reporting.
Quality: Tooling Life Tracking and Wear-Based Replacement Triggers
Tooling replacement triggered by cycle count and production hours rather than calendar intervals. Scrap and rework rate fell from 4.2% to 1.9% across all lines by Month 12 as tooling wear-driven quality losses were eliminated through data-based replacement scheduling.
Continue Reading: Case Studies
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Cement Plant Achieved 60% Fewer Unplanned Stops with Oxmaint60% reduction in unplanned kiln stops and $2.1M in avoided downtime costs through predictive maintenance.
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CRE Portfolio Saves $2M Annually in Energy Costs with Oxmaint Smart FM15-building portfolio cut $2M in annual energy costs through IoT monitoring and automated HVAC optimisation.
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Frequently Asked Questions
QHow does Oxmaint calculate and display OEE in real time for a manufacturing facility?
Oxmaint calculates OEE from work order data and IoT sensor readings: availability from planned vs unplanned downtime per asset, performance from cycle time deviation data, and quality from defect records linked to equipment condition. Live per-line OEE dashboard updates continuously. Sign up free or book a demo to see the OEE dashboard configured for your production lines.
QHow quickly can IoT sensors be deployed on existing CNC machines and production equipment without disrupting production?
Wireless IoT sensors deploy on CNC machines, presses, and conveyors during planned maintenance windows without production disruption or wiring installation. This plant deployed all 22 critical asset sensors in a single 48-hour planned maintenance window, with condition data live in Oxmaint within 72 hours. Book a demo to see a sensor deployment plan scoped for your asset inventory.
QWhat is the business case for a Plant Manager or VP of Operations to approve this investment?
At this plant: 65% OEE on 4 lines generating $3.1M annual downtime cost. Deployment at $218,000. Year 1 saving: $2.4M. Payback: 11 months. The approval case requires two inputs from your operation: current OEE and estimated cost per hour of unplanned downtime. Book a demo to model the OEE ROI case for your facility.
QCan Oxmaint handle production-based PM triggers as well as calendar-based intervals for manufacturing equipment?
Yes. Oxmaint generates PM work orders from both calendar intervals and production-based triggers including cycle counts, run hours, and units produced. Whichever threshold is reached first generates the work order automatically without manual calculation or monitoring. Sign up free to configure production-based PM triggers in Oxmaint for your equipment.
65% OEE to 85% OEE in 14 Months. $2.4M Annual Saving. Payback in 11 Months.
Your production team already has the skills. Oxmaint gives them the system: live OEE by line, IoT condition alerts, automated PM scheduled into changeover windows, and mobile work orders for every technician. Go live in 35 days. No ERP integration required.
Real-Time OEE Dashboard
IoT Condition Alerts
Production-Based PM Triggers
Mobile Work Orders
35-Day Deployment
