This case study documents how a major beverage FMCG manufacturer — operating 8 production facilities across three countries, producing 2.4 billion units annually — deployed Oxmaint's AI-powered predictive maintenance platform and robotic inspection programme to eliminate the unplanned downtime that was costing them $33M per year. The results delivered within 18 months: 45% reduction in unplanned downtime, $15M in annual savings, and a 12-point OEE improvement across every facility in the network.
Facing the same downtime challenges?
See how Oxmaint's AI-powered platform delivers measurable results for FMCG beverage manufacturers — from first sensor alert to full ROI in under 18 months.
45%
Reduction in Unplanned Downtime Across All 8 Facilities
$15M
Annual Maintenance Cost Savings Achieved
+12pts
OEE Improvement Across the Production Network
18mo
Full Deployment to Measurable ROI
The Client: A Global Beverage FMCG Manufacturer
The client is a multinational beverage company producing carbonated soft drinks, juices, and water across 8 manufacturing sites in the UK, Germany, and Poland. With over 340 production lines, 12,000+ assets under maintenance, and a workforce of 2,200 — including 180 maintenance technicians — the business operates in a high-volume, low-margin environment where every hour of unplanned downtime directly erodes profitability. Annual maintenance spend at programme start was $42M, with $33M of that attributable to reactive maintenance, emergency parts procurement, and production loss from equipment failures that were not predicted or prevented.
Industry
Beverage Manufacturing — Carbonated Soft Drinks, Juice, Water
Facilities
8 Production Sites — UK, Germany, Poland
Production Volume
2.4 Billion Units Annually
Production Lines
340+ Lines Across the Network
Assets Under Management
12,000+ Assets — Filling, Packaging, Utilities, CIP
Maintenance Team
180 Technicians Across 8 Sites
Annual Maintenance Spend (Pre)
$42M — 78% Reactive at Programme Start
Downtime Cost (Pre)
$33M Annual Loss — Unplanned Stoppages
The Problem: Four Systemic Failures Driving $33M in Annual Losses
Before Oxmaint deployment, the maintenance operation was characterised by reactive firefighting rather than planned intervention. Root cause analysis conducted at programme start identified four interconnected failure patterns that together accounted for 91% of all unplanned downtime events across the network.
Root Cause Analysis — Unplanned Downtime by Failure Category (Pre-Oxmaint)
01
No Predictive Visibility on Critical Assets
38% of downtime events
Filling machines, carbonation systems, and pasteurisers had no vibration, temperature, or pressure monitoring. Failures were only detected when production stopped. Average time from fault onset to detection: 4.2 hours. Average repair time after detection: 6.8 hours. 11 hours of avoidable downtime per event.
02
Calendar-Based PM That Did Not Match Actual Asset Condition
27% of downtime events
Preventive maintenance schedules were based on manufacturer intervals, not actual run hours or asset condition. 34% of PM tasks were performed on assets with no deterioration, while assets running at 160% of rated capacity received identical schedules. Premature and delayed intervention contributed equally to failure rates.
03
Manual Inspection Missing Deterioration in Inaccessible Zones
18% of downtime events
Conveyor systems, elevated packaging lines, and CIP pipework in confined areas were either inspected infrequently (quarterly manual rounds) or not at all. Corrosion, seal degradation, and mechanical wear in these zones were only discovered during failure — not before it.
04
Fragmented Work Order Management Across 8 Sites
8% of downtime events
Each site operated its own maintenance system — three different CMMS platforms, two paper-based processes. Cross-site visibility was non-existent. Parts inventory was managed locally, resulting in emergency procurement at 3–4x standard cost. Mean time to repair was extended by an average of 2.3 hours per event due to parts availability failures.
The Solution: Oxmaint Full Platform Deployment Across 8 Facilities
Oxmaint's implementation team designed an 18-month deployment programme structured in three phases — each building on the last to progressively eliminate the four root causes identified in the pre-deployment audit. The solution combined AI-powered condition monitoring, robotic inspection, and a unified CMMS platform to give the maintenance operation the predictive capability and cross-site visibility it had never had.
Oxmaint Deployment Timeline — Three-Phase Implementation Across 8 Beverage Facilities
Phase 1 — Months 1–6
Platform Unification & Sensor Deployment
Unified CMMS deployed across all 8 sites — all assets migrated, historical maintenance records digitised
IoT vibration and temperature sensors installed on 847 critical assets — filling machines, carbonation units, pasteurisers, compressors
Real-time sensor data feeding Oxmaint AI engine — baseline condition profiles established per asset
Spare parts inventory consolidated across sites — network-level visibility into parts availability
180 technicians onboarded to Oxmaint mobile — digital work orders, mobile checklists, photo capture
Early Win: 22% reduction in emergency work orders within 90 days of sensor go-live
Phase 2 — Months 7–12
AI Predictive Engine Activation & Robotic Inspection
AI failure prediction models trained on 6 months of sensor data — asset-specific anomaly thresholds configured
Robotic inspection units deployed on conveyor systems, elevated packaging lines, and CIP pipework at 4 highest-risk sites
Robotic inspection generating weekly condition reports — replacing quarterly manual rounds in inaccessible zones
Condition-based PM schedules replacing calendar-based intervals — PM tasks triggered by actual asset state
AI-generated work orders automatically dispatched to nearest available technician when anomaly threshold breached
Mid-Point: Unplanned downtime down 31% vs. programme baseline at Month 12
Phase 3 — Months 13–18
Network Optimisation & Full ROI Realisation
Robotic inspection extended to all 8 sites — full network coverage of previously inaccessible asset zones
Cross-site AI benchmarking — asset performance compared across facilities, best-practice maintenance intervals identified and propagated
Predictive parts ordering integrated — AI failure forecasts triggering automatic stock replenishment before parts are needed
Digital twin models built for 12 highest-criticality asset classes — simulation used to optimise shutdown planning
Full OEE reporting dashboard live across all 8 sites — real-time visibility for plant directors and group maintenance leadership
Programme Complete: 45% downtime reduction and $15M annual savings confirmed at 18-month audit
The Results: Quantified Outcomes at 18-Month Audit
The 18-month programme audit was conducted by an independent operations consultancy engaged by the client's board. All metrics below are verified against baseline data collected in the 12-month period immediately preceding Oxmaint deployment.
Verified Performance Outcomes — 18-Month Post-Deployment Audit
45%
Unplanned Downtime Reduction
From 847 unplanned stoppages/year to 466 — across all 8 facilities. Average stoppage duration also reduced from 6.8 hrs to 3.1 hrs as AI-predicted failures were caught earlier and parts were available on first attendance.
$15M
Annual Maintenance Cost Saving
Breakdown: $8.4M production loss avoidance, $3.6M emergency repair elimination, $2.1M parts procurement cost reduction (emergency premium eliminated), $0.9M contractor call-out reduction.
+12pts
OEE Improvement
Network OEE moved from 71.4% to 83.6% — driven primarily by availability improvement (downtime reduction) and performance improvement (condition-optimised assets running closer to rated speed without degradation-induced throughput loss).
68%
Reduction in Emergency Work Orders
Emergency work orders fell from 2,340/year to 749/year across the network. Reactive maintenance as a proportion of total maintenance activity fell from 78% to 29% — a shift from predominantly reactive to predominantly planned operation.
4.2x
ROI on Oxmaint Investment
Total programme investment including platform, sensors, robotic inspection units, and implementation services: $3.6M over 18 months. Annual saving: $15M. Payback period: 2.9 months from full deployment. 3-year NPV: $41M.
91%
AI Fault Prediction Accuracy
Of all AI-generated predictive alerts at Month 18, 91% resulted in confirmed fault findings on inspection. False positive rate reduced from 34% (Month 6) to 9% (Month 18) as the AI model matured on asset-specific data.
3.1hrs
Mean Time to Repair (vs 6.8hrs Baseline)
MTTR reduction driven by earlier fault detection (less damage at intervention), parts availability on first attendance (predictive ordering), and mobile work order access giving technicians full asset history and repair procedure at the machine.
340%
Increase in Inspection Coverage
Robotic inspection increased inspection frequency in inaccessible zones from quarterly to weekly across all 8 sites. 23 previously uninspected asset zones brought into a regular inspection programme for the first time.
$0
Food Safety Incidents from Equipment Failure
Zero food safety or product quality incidents attributable to equipment failure in the 18 months post-deployment — compared to 3 product hold events in the 18-month pre-deployment baseline, each requiring lab testing and production suspension.
Key Milestones: The 18-Month Journey in Numbers
Programme Milestones — Oxmaint Deployment Timeline
Month 1–2
Platform go-live across all 8 sites — 12,000+ assets migrated to unified Oxmaint CMMS. 180 technicians onboarded. Historical maintenance records digitised.
Baseline established
Month 3
IoT sensor installation complete on 847 critical assets. Real-time condition data streaming to Oxmaint AI engine. First anomaly alerts generated within 72 hours of sensor activation.
First predicted fault caught
Month 4
First averted major failure: AI detected abnormal vibration signature on Facility 2 carbonation pump 14 days before failure. Planned replacement during scheduled downtime. Avoided: 18 hrs unplanned stoppage, $340K production loss.
$340K loss avoided — single event
Month 6
90-day review: Emergency work orders down 22%. Technician time on reactive tasks reduced from 74% to 51% of total maintenance hours. First robotic inspection units deployed at Facilities 1 and 3.
22% emergency WO reduction
Month 9
Condition-based PM fully live across all sites — calendar PM intervals retired for 634 assets. AI model accuracy reaches 84% on trained asset classes. Robotic inspection covering 4 sites, 67 previously inaccessible zones.
Calendar PM replaced by condition-based
Month 12
Mid-programme review: Unplanned downtime down 31% vs. baseline. OEE up 7 points network average. MTTR reduced to 4.1 hrs vs. 6.8 hr baseline. Reactive maintenance proportion: 42% (vs. 78% at start).
31% downtime reduction confirmed
Month 15
Predictive parts ordering live — AI failure forecasts integrated with procurement. Emergency parts orders eliminated at 6 of 8 sites. Digital twin models operational for 12 highest-criticality asset classes.
Emergency parts cost eliminated
Month 18
Independent audit confirms: 45% downtime reduction, $15M annual saving, +12pts OEE, 91% AI prediction accuracy, 4.2x ROI. Client board approves extension to 4 additional facilities in Southeast Asia.
$15M saving — programme confirmed
The Technology: Oxmaint Capabilities That Drove the Results
Oxmaint Platform Features Deployed — Capability to Outcome Mapping
AI Predictive Maintenance Engine
Machine learning models trained on vibration, temperature, pressure, and run-hour data from 847 sensors — generating failure probability scores per asset, per shift, updated in real time
38% of downtime root cause eliminated
Robotic Inspection System
Autonomous inspection robots navigating conveyor systems, elevated lines, and confined pipework — weekly thermal, visual, and acoustic inspection replacing quarterly manual rounds
18% of downtime root cause eliminated
Condition-Based PM Scheduling
PM intervals calculated dynamically from actual asset condition data — eliminating both premature and overdue maintenance that were generating failure events under the calendar-based system
27% of downtime root cause eliminated
Unified Multi-Site CMMS
Single platform across all 8 sites replacing 3 legacy systems and 2 paper processes — real-time work order management, cross-site parts visibility, network-level reporting for maintenance leadership
8% of downtime root cause eliminated
Mobile Work Order Platform
180 technicians with full asset history, repair procedures, parts availability, and safety permits on mobile — reducing time from fault detection to repair start by an average of 1.8 hours
MTTR reduced 54% (6.8hrs → 3.1hrs)
Predictive Parts Ordering
AI failure forecasts integrated with procurement — parts ordered automatically when AI predicts failure probability exceeding threshold, arriving before the intervention is needed
Emergency parts premium eliminated
Cross-Site OEE Dashboard
Real-time availability, performance, and quality metrics per line, per site, per network — giving plant directors and group maintenance leadership live visibility for the first time
+12pt OEE improvement tracked and verified
Frequently Asked Questions About This Case Study
The first measurable impact was visible within 90 days of sensor go-live — emergency work orders fell 22% in the first quarter of Phase 1. The first major averted failure (a $340K production loss avoided at Facility 2) occurred at Month 4. By Month 12 the mid-programme audit confirmed 31% downtime reduction. Full programme results of 45% downtime reduction and $15M annual saving were confirmed at the 18-month independent audit.
Total programme investment over 18 months was $3.6M — covering the Oxmaint platform licence, IoT sensor hardware and installation across 8 sites, robotic inspection units and deployment, data integration services, and implementation support including technician training. Against $15M in annual savings, the payback period was 2.9 months from full deployment, with a 3-year NPV of $41M and a 4.2x ROI on total programme investment.
The AI model was trained on 6 months of sensor data collected during Phase 1, combined with the client's historical maintenance records and failure event data digitised during platform migration. At Month 6, AI prediction accuracy was 66%. By Month 12 it had reached 84% as the model matured on asset-specific failure signatures. At the 18-month audit, accuracy stood at 91% — meaning 9 in 10 AI-generated alerts resulted in a confirmed fault finding on technician inspection.
Robotic inspection units were deployed during planned low-production windows — typically overnight shifts and scheduled changeover periods — and operated autonomously on pre-programmed routes through conveyor systems, elevated packaging structures, and CIP pipework corridors. Each unit captured thermal imaging, visual inspection footage, and acoustic data, uploading inspection reports to Oxmaint automatically on return. No production stoppages were required for robotic inspection at any of the 8 sites.
Yes. The multi-site scale of this programme amplified the financial return, but the core capability — AI predictive maintenance, condition-based PM scheduling, and unified CMMS — delivers measurable downtime reduction for single-site operations with as few as 200 assets. Single-site FMCG deployments typically achieve 30–40% downtime reduction within 12 months at a significantly lower investment level. The cross-site benchmarking and network OEE dashboard features become relevant as the operation scales.
Replicate These Results in Your Facilities
45% Less Downtime. $15M Saved. See How It Works for Your Plant.
This beverage manufacturer moved from 78% reactive maintenance to a predominantly planned, AI-predicted operation in 18 months. Oxmaint's platform — predictive AI, robotic inspection, condition-based PM, and unified multi-site CMMS — is deployable in your facilities with the same implementation approach.
45%
Average Downtime Reduction
4.2x
Return on Investment
90 days
To First Measurable Result
91%
AI Prediction Accuracy
✓AI predictive maintenance — fault detection before failure
✓Robotic inspection — weekly coverage of inaccessible zones
✓Condition-based PM — replace calendar intervals with real asset data
✓Unified CMMS — one platform across all sites, all assets
✓Mobile work orders — 180 technicians, zero paperwork
✓Cross-site OEE dashboard — live visibility for leadership
Oxmaint is used by FMCG maintenance teams across food, beverage, personal care, and household products manufacturing. Full implementation support included. Results typically visible within 90 days of sensor go-live.
