A mid-size North American pet food manufacturer producing 280 million pounds of extruded kibble and coated treats annually was bleeding revenue to unplanned downtime — extruder barrel failures, dryer belt slips, vacuum coating drum imbalances, and conveyor bearing seizures were costing the operation 14% of total production hours and triggering emergency repair spend that ran nearly 5× planned intervention pricing. After 11 months operating on OxMaint's predictive maintenance platform across all three production lines, equipment failures dropped 58%, mean time between failures rose 2.4×, and the maintenance department reallocated $1.8M in reactive spend toward capital improvements. This case study walks through the asset inventory, sensor architecture, condition rules, and operational changes that produced the results — and shows how the same framework applies to any extrusion or coating-based food operation. To see how OxMaint would map to your production lines, start a free trial or book a demo with our food manufacturing reliability team.
Case Study · Pet Food · 11 Months · Multi-Line Plant
58%
Pet Food Manufacturer Reduces Equipment Failures by 58% with OxMaint Predictive Maintenance
How a 280M-pound-annual-volume pet food producer eliminated more than half of unplanned equipment failures, recovered $1.8M in reactive spend, and turned a reactive maintenance team into a proactive reliability operation in under a year.
Headline Outcomes
The Numbers That Changed the Maintenance P&L
58%
Fewer unplanned equipment failures across all lines
42%
Reduction in overall maintenance spend
2.4×
Increase in mean time between failures
99.2%
FDA, FSMA, and SQF audit pass rate
Chapter 01
The Plant
The Operation Before OxMaint
The plant runs three twin-screw extrusion lines producing dry kibble, two horizontal belt dryers handling 8 tons per hour each, a vacuum coating drum applying fat and palatant systems, and a downstream packaging hall feeding both 4-lb retail bags and 50-lb bulk containers. Total fixed assets covered by the maintenance program exceed 1,400 items, ranging from extruder barrel sections and die plates to dryer fans, conveyor motors, weigh hopper load cells, and ingredient blender agitators.
Before deployment, the team operated on a hybrid spreadsheet-and-paper CMMS that had been patched together over a decade. PMs were time-based and frequently skipped during peak production. Vibration analysis was performed quarterly by a third-party contractor who emailed PDF reports that nobody systematically reviewed. Mean time between failures had been declining 6-8% per year for four consecutive years. Reactive emergency repairs were costing nearly 5× planned pricing, and the plant manager had escalated the issue to the parent company's VP of Operations. To explore how OxMaint maps to a similar plant footprint, start a free trial or book a demo.
"
Pet food extruder barrel failures average 16 hours of lost production per event — at $42,000 per hour of contribution margin.
Plant Reliability Engineer Report
Chapter 02
The Rollout
The 6 Pillars of the Predictive Maintenance Deployment
The 90-day deployment followed a six-pillar framework that has since been repeated across other pet food and human food manufacturing sites. Each pillar maps to a specific failure mode that was previously costing the plant unplanned downtime, and each was operationalized inside OxMaint within the first quarter.
01
Asset Hierarchy Build-Out
1,400 assets imported from legacy spreadsheets and structured into Portfolio > Property > System > Asset > Component. Every extruder barrel, dryer fan, and coating drum component logged with parent-child relationships.
02
Vibration Sensor Deployment
Wireless triaxial vibration sensors mounted on extruder gearboxes, dryer fan bearings, and coating drum support bearings. ISO 10816 thresholds configured per asset class.
03
Motor Current Signature Analysis
Current transformers installed on critical motor circuits. OxMaint pulls current data continuously, detecting rotor bar defects, eccentricity, and impeller damage weeks before traditional vibration analysis would catch them.
04
SCADA Integration
Direct connection to plant SCADA for extruder torque, dryer temperature, throughput rates, and downstream conveyor speeds. Process drift triggers maintenance investigation automatically.
05
Threshold-Based Work Orders
Each asset configured with severity tiers. Vibration crossing 7.1 mm/s RMS auto-generates a priority work order routed to the right technician within seconds, not hours.
06
FSMA and SQF Documentation
Every work order, inspection, and condition reading time-stamped and tied to the asset. Evidence trails meet FDA FSMA, SQF Level 3, and AAFCO documentation requirements with zero paper backup.
Chapter 03
The Problem
The Failures the Plant Used to Live With
Before OxMaint, six recurring failure modes drove the vast majority of unplanned downtime hours. Each had been treated as inevitable for years. Predictive maintenance with structured condition monitoring eliminated or substantially reduced each one within the first three quarters. If your plant lives with similar issues, start a free trial to see how OxMaint catches them, or book a demo for a line-by-line walkthrough.
Mode 01
Extruder Gearbox Bearing Failure
Twin-screw gearbox bearings would seize mid-batch, taking the line down for 14-20 hours. Vibration monitoring now detects bearing wear 4-6 weeks before failure.
Eliminated
Mode 02
Dryer Belt Mistracking
Belt drift caused tearing events that contaminated batches with belt material. Real-time position monitoring catches drift before tearing and triggers immediate work orders.
90% reduced
Mode 03
Coating Drum Imbalance
Built-up fat residue created drum imbalance that destroyed support bearings every 9-14 months. Vibration trending now flags imbalance buildup at the cleaning stage.
Eliminated
Mode 04
Conveyor Motor Burnout
Motor current signature analysis catches rotor bar defects and bearing wear that previously caused 6-8 motor burnouts per year. Failures dropped to 1 in the last year.
85% reduced
Mode 05
Weigh Hopper Load Cell Drift
Out-of-calibration load cells caused product weight giveaway and SQF audit findings. Continuous SCADA monitoring flags drift before calibration deadlines.
Eliminated
Mode 06
Ingredient Blender Agitator Wear
Agitator paddle wear created inconsistent mixing that affected product palatability scores. Vibration and current signatures now flag wear patterns at early stages.
75% reduced
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Plants switching to structured predictive CMMS see 30-58% lower breakdown costs in the first year — measurable on your own production data in 30 days.
Industry Maintenance Benchmark
Chapter 04
What OxMaint Did
How OxMaint Operationalized the 58% Reduction
OxMaint provided the asset hierarchy, IoT and SCADA integration, threshold-based work order engine, technician routing, and portfolio reporting that turned siloed sensor data into a coordinated reliability program. To see how the same framework would apply to your plant, start a free trial today or book a demo with our team.
Asset Hierarchy and Condition Scoring
Full Portfolio > Property > System > Asset > Component model with continuous health scoring across all 1,400 plant assets.
IoT Sensor Integration
Wireless vibration, temperature, and current sensors connect via standard gateways. Data feeds asset records continuously and triggers automated work orders.
SCADA and Process Data
Direct integration with plant SCADA pulls extruder torque, dryer temperature, throughput, and weigh hopper data into the CMMS for continuous condition tracking.
Threshold-Based Work Orders
Per-asset thresholds for vibration, temperature, and current. Breaches auto-generate priority work orders routed to the right technician within seconds.
Mobile-First Work Order Execution
Technicians complete work orders, capture photos, and update asset records from the production floor — no return trips to the office to update records.
CapEx Forecasting and Portfolio View
Rolling 5-10 year replacement model for extruders, dryers, and coating systems. Investor-grade reporting eliminated surprise CapEx requests to the parent company.
Chapter 05
Before vs After
The Plant Before and After 11 Months on OxMaint
The transition from reactive to predictive maintenance produced structural changes across every operational metric the plant tracks. The numbers below come directly from the plant's monthly maintenance and operations dashboards.
| Operating Metric | Before OxMaint | After 11 Months on OxMaint |
|---|
| Unplanned failures per quarter | 32 events | 13 events |
| Mean time between failures | 1,180 hours | 2,830 hours |
| Reactive repair share of spend | 62% | 21% |
| Cost per critical repair | 4.8× planned baseline | 1.1× planned baseline |
| Production hours lost to maintenance | 14% of total | 4.2% of total |
| SQF and FSMA audit findings | Multiple per cycle | Zero in last 2 audits |
| CapEx forecast accuracy | ±38% variance | ±6% variance |
| Technician overtime hours | 2,100 per quarter | 740 per quarter |
Chapter 06
ROI Breakdown
Where the $1.8M Came From
The reclaimed spend broke down across four primary categories, each tied directly to an operational shift enabled by predictive maintenance discipline. The numbers below are normalized to a 12-month projection based on the 11 months of recorded data.
$720K
Avoided Emergency Repairs
Reactive emergency repairs averaging 4.8× planned pricing eliminated by catching faults at the early degradation stage with sensor monitoring.
$540K
Recovered Production Hours
14% of production hours lost to unplanned downtime dropped to 4.2%, releasing roughly 870 additional production hours per year of contribution margin.
$320K
Reduced Technician Overtime
Quarterly overtime dropped from 2,100 hours to 740 hours as emergency callouts disappeared and planned interventions ran inside scheduled windows.
$220K
Spare Parts Optimization
Critical spares stocking tied to actual condition data eliminated panic rush orders at premium pricing and reduced overall inventory carrying cost.
12-Month Reclaimed Spend
$1.8M
FAQ
Common Questions
Pet Food Predictive Maintenance: Frequently Asked Questions
Can OxMaint integrate with our existing plant SCADA and DCS systems?
Yes. OxMaint integrates with major SCADA and DCS platforms via OPC and API connections. Process data — extruder torque, dryer temperature, throughput, weigh hopper readings — feeds directly into asset condition records and triggers automated work orders when drift exceeds defined thresholds.
What vibration sensors does OxMaint support for extruders and coating drums?
OxMaint supports wireless and wired triaxial vibration sensors from major manufacturers including Emerson, SKF, ABB, and IoT-native vendors. Sensors mounted on gearboxes, fan bearings, and drum supports feed ISO 10816-compliant data directly into the CMMS for threshold-based work order generation.
Does OxMaint support FSMA, SQF, and AAFCO documentation for pet food plants?
Yes. Every work order, inspection, and condition reading is time-stamped and tied to the asset record. OxMaint produces evidence trails that meet FDA FSMA, SQF Level 3, AAFCO, and internal food safety audit requirements with zero paper backup.
How quickly can OxMaint be deployed at a multi-line food manufacturing plant?
Most food manufacturing plants are live in days, not months. Asset hierarchies can be imported from existing spreadsheets or legacy CMMS exports, sensor integrations activate via standard API connections, and PM templates roll out across every line in the same workflow.
Your Next Chapter
Turn Every Extruder, Dryer, and Coating System Into a Predictable Asset
Whether you operate a single pet food plant or a multi-site portfolio of food manufacturing facilities, OxMaint converts vibration, temperature, current, and SCADA data into automated work orders — catching failures weeks before they happen. No heavy implementation. Live in days, not months. See measurable results in the first 30 days.
- Real-time visibility into extruder, dryer, and coating equipment health
- Predictive failure alerts from vibration, temperature, and current data
- 5-10 year CapEx forecasting tied to actual asset condition scoring
Used by food manufacturers managing 10,000+ production assets · Limited onboarding slots available this quarter
