How Manufacturing Engineers in Food and Beverage Have Led OEE and Reliability Improvements

The OEE improvement projects that produce durable results in food and beverage processing share a pattern: they start with an availability problem that could not be properly attributed without better asset health data. The availability RCA was incomplete. The HACCP FMEA detection rating was estimated rather than calibrated. The pre-peak preparation was calendar-based rather than asset health-based.

What changed in each case was the quality of the data available to the manufacturing engineer. This article covers the types of engineering work that produce those results, with placeholders for facility-specific case study data that Tractian customers have reported, and with Tractian reference accounts where published case study data is available.

The Availability Attribution Problem

The most common scenario manufacturing engineers describe when discussing the value of continuous monitoring is a version of the same story: an availability problem that appeared to be one thing and turned out to be another once failure mode data was available.

The scenario:

A continuous processing line in an F&B plant shows a pattern of centrifugal pump failures on the same line, clustered in the same time window (high-production periods). The work order history records all of these as "pump failure, bearing replacement." The maintenance team replaces the bearings on schedule. The failures continue at roughly the same frequency.

The manufacturing engineer classifies this as a maintenance strategy problem. The PM interval is too long. Bearings are degrading beyond what the quarterly inspection interval catches. The proposed solution is increasing PM frequency.

The correct diagnosis: the pumps are running at the right flow rate during normal operations but are being run at increased flow rates during production peaks to compensate for upstream process variability. At the elevated flow rate, the pumps operate outside their design operating range (left of the best efficiency point on the pump curve) and cavitate. The cavitation damages bearings faster than any PM interval can compensate. The failure mode is hydraulic, not a maintenance interval problem.

What continuous monitoring reveals:

A monitoring platform with frequency spectrum analysis shows the characteristic high-frequency spectral signature of cavitation on these pumps during the high-production periods. The bearing defect frequencies are also present but are a consequence of the cavitation damage rather than the primary failure mode.

The corrective action is a process engineering change: flow control logic adjustment to keep the pumps within their allowable operating region during production peaks, not a shorter PM interval. The RCA is correct. The failure stops recurring.

This is the engineering value that continuous monitoring provides for availability RCA: the failure mode data that makes the RCA accurate enough to produce a corrective action that actually works.

The four-component cost calculation for this pattern:

A pump failure event on a continuous dairy processing line of this type typically produces:

• Production loss: 4 to 6 hours at the line's production value per hour
• Product disposal: any product in the system at time of failure that requires hold-and-test or disposal
• Sanitation restart: 2 to 4-hour CIP cycle before restart
• Emergency repair premium: after-hours call for the bearing replacement, plus expedited pump seal freight if the seal was also damaged

The full event cost, aggregated across 5 to 8 events per year on a single line, represents a material annual cost that a process engineering change (correct operating point for the pumps) eliminates almost entirely.

The HACCP FMEA Update That Changed Monitoring Priority

The scenario:

A dairy plant's FMEA for the HTST pasteurization system rates the detection control for HTST feed pump bearing failure as 6 on the standard 10-point scale (meaning: detection is moderately likely). The detection control listed in the FMEA is: quarterly PM inspection including bearing inspection and thermography.

A manufacturing engineer reviews this rating against the actual maintenance history. In the last 3 years, the HTST feed pump has experienced two failures. Both failures occurred within 6 weeks of a quarterly PM inspection that showed no anomaly. In both cases, the bearing failed to catastrophic stage without any warning detected by the inspection.

The detection control is not performing at a detection rating of 6. It is performing at a detection rating of 9 or 10: unlikely to detect the failure before it causes the consequence.

The F&B consequence:

At a HACCP CCP, "unlikely to detect" is not acceptable for a Severity 10 failure mode. The FMEA recalculation with detection rating 9 produces an RPN that classifies this as a critical gap requiring immediate additional control. The recommended control is continuous condition monitoring on the HTST feed pump.

What monitoring changes:

After continuous vibration monitoring deployment on the HTST feed pump, the failure mode progression data shows that a Stage 1 bearing defect signal appears 4 to 8 weeks before the pump reaches a production-affecting failure at production load. The same bearing that showed no anomaly during a quarterly CIP-window inspection had detectable fault frequencies 6 weeks earlier during production operation.

The FMEA detection rating for the HTST feed pump bearing failure mode is updated to 2 or 3 (likely to detect, with 4 to 8-week lead time documented). The RPN drops from critical gap to managed risk. The monitoring deployment is the detection control that closes the FMEA gap.

This is the HACCP FMEA update that changes monitoring from a desirable improvement to a required control. The manufacturing engineer who makes this analysis is demonstrating the mechanical reliability and food safety integration that the career article identifies as the differentiating F&B profile.

The Pre-Peak Audit That Saved a Production Season

The scenario:

A beverage plant enters its annual holiday production run in October. For the previous two years, the holiday run has had a major availability event on the filling line drives in November or December. Both events involved gearbox failure on the filling line primary drive. Both were classified as "normal wear" failures. Both occurred with 3 to 4 weeks of peak production remaining.

The manufacturing engineer, in their first year at the facility, builds the pre-peak equipment health audit described in the career and tools articles. Six weeks before the October holiday run, the monitoring data for the primary filling line drive shows gear mesh frequency anomalies consistent with Stage 2 gear wear, trending upward at a rate that suggests Stage 3 before the end of November.

The intervention is a gearbox replacement scheduled for the first planned maintenance window in October, 5 weeks before the holiday run begins. The gearbox is replaced without a production stoppage. The holiday run completes without a filling line availability event.

The documented value of the avoided event:

• Production loss from the equivalent unplanned event (based on previous year's events): 36 to 48 hours of production on the highest-throughput line of the year
• Product disposal: packaging line stoppages during holiday run create product hold requirements in the buffering system
• Sanitation restart: 3-hour CIP cycle at holiday production value per hour
• Emergency repair premium: the previous year's events both required after-hours gearbox work with expedited bearing kit freight

The four-component calculation for a single prevented event at holiday peak production value demonstrates a monitoring ROI for this single asset in this single season.

The manufacturing engineer presents this documented case to plant management in January: the failure mode detected, the timeline from detection to intervention, the planned repair cost, and the estimated cost of the unplanned event that did not occur. This is the one-page ROI framework from the previous article, built from a real event.

Tractian Customer Results in Food and Beverage

Ingredion (North Kansas City, specialty food ingredient manufacturing):

Ingredion's ingredient processing facility operates continuous processing lines where the four-component failure cost on critical rotating equipment is high. The manufacturing challenge was that PM schedules were missing condition degradation during high-load production periods: hard-to-reach assets delayed inspections, and interval-based PMs missed subtle alignment problems and bearing wear.

The deployment documented continuous monitoring on critical rotating equipment. Availability outcomes included detection of a lubrication fault on a critical asset (confirmed resolved by rechecking on the platform), and early detection of looseness on a DSM pump with no backup and a known three-day outage history. The DSM pump event is the engineering-relevant data point: looseness detected at early stage, work order issued, fault corrected before failure. The production stoppage that would have been a three-day availability event became a planned repair.

Engineering-level results:

• $1,000,000 in production savings at one plant
• $223,000 in maintenance savings
• 48 to 168 hours of avoided downtime on critical equipment
• Failure mode detection documented in real time, with platform confirmation that corrective action resolved the issue

"There were some issues that I would say, if not for having Tractian, we would have never noticed. For example, a lubrication problem: we could go out and lubricate it and recheck it on Tractian platform and see that it fixed the problem. It was pretty impressive for that, the results we got early on." -- Jacob Hoffine, Reliability Engineer, Ingredion

Read the full case study: Ingredion Adopts AI to Detect Failures and Boost Machine Uptime

Danone (dairy production):

Danone's dairy facility presents the type of FMEA-relevant engineering case study described earlier in this article: failures on food-contact processing equipment (cheese-processing vessel, homogenizer) with direct product quality and production continuity consequences.

The deployment caught two separate developing failures. The engineering significance: a lubrication failure on a cheese-processing vessel was identified before it progressed to gearbox damage; pulley wear and misalignment on a homogenizer was detected before catastrophic failure. Both failure modes were detectable with vibration and temperature monitoring at early stage. Both were caught and repaired at planned cost.

Engineering-level results:

• $7,600 gearbox replacement cost avoided (lubrication failure, cheese-processing vessel)
• Up to $40,000 in maintenance repair costs avoided (homogenizer pulley wear and misalignment)
• 3 to 30 days of production stoppage avoided
• $120,000 to $600,000 in commercial and production loss impact avoided

"With condition monitoring, we can see our assets much more clearly. Today we're able to identify potential failures early and plan interventions before they turn into breakdowns and stoppages that would impact production." -- Renato Rosalini, Maintenance Manager, Danone

Read the full case study: Danone Case Study

Lyka (pet food manufacturing, Australian scale-up):

Lyka's engineering challenge was operational: maintenance workflows were reactive and inconsistent, with information buried across binders, spreadsheets, and whiteboards. The availability impact was direct -- finding a part could take 22 minutes, and there was no early visibility on developing asset failures.

After CMMS deployment (integrated with Oracle NetSuite) and condition monitoring sensor deployment, the engineering outcomes were immediate. Within the first week, two critical failures were detected: a temperature spike on fans attached to two key motors was identified before the failure mode escalated to full motor replacements or spoiled product. The engineering-relevant pattern: temperature monitoring identified the failure mode at the component level (fan failure) before the downstream consequence (motor failure, product loss) occurred.

Engineering-level results:

• Warehouse part lookup time reduced from 22 minutes to 22 seconds (operational efficiency enabling faster repair execution)
• Two critical equipment failures detected within the first week of condition monitoring deployment
• Temperature spike caught on two key motors before escalating to full motor replacements or spoiled goods

Read the full case study: From CMMS to Condition Monitoring: How Lyka Built a Proactive Operation

Unilever (food and beverage manufacturing, Latin America plant):

Unilever's Latin America plant represents one of the larger documented Tractian F&B deployments: 40 critical assets, 320 sensors, across a plant producing Knorr and Hellmann's branded products. The Q2 2025 monitoring period (112 days) produced engineering-level documentation of failure prevention at scale.

Engineering-level results (Q2 2025):

• 19 failures anticipated before they became production events
• 117 hours of avoided unplanned downtime
• $796,000+ in protected or avoided corrective costs
• 100% sensor uptime across the 112-day monitoring period
• Three highest-value prevented failures: KSM Tank 02 mechanical looseness ($250,000+ avoided, 24 hrs); Syrup Elevating Screw bearing wear ($200,000+ avoided, 8 hrs); Vacushear bearing wear ($135,000+ avoided, 8 hrs)

The engineering significance of the Vacushear and Syrup Elevating Screw events: both are bearing wear failure modes on food-contact rotating equipment. Both were caught at early stage. Both were resolved in 8 hours of planned downtime versus the production-affecting failure event that would have occurred without detection.

Read the full case study: Unilever Case Study

What the Data Shows Across F&B Deployments

Without specific published case study figures, the following engineering patterns are consistent with Tractian's F&B deployment experience and with the failure mode literature for continuous F&B processing equipment:

Availability improvement timeline: In continuous F&B processing operations, the first monitored asset detection events typically occur within 60 to 90 days of deployment on Tier 1 assets with existing degradation. The availability improvement from planned repair conversions becomes measurable within 6 to 12 months as the monitored asset pipeline moves from reactive to planned.

Failure mode distribution on F&B rotating equipment: Across continuous processing pump and drive populations, bearing-related failure modes (inner race, outer race, ball defect, cage defect) account for the majority of progressive failure events. Gear mesh wear is the next most common in gearbox-equipped drives. Cavitation-related bearing damage is a significant source in centrifugal pump populations where operating point control is imprecise. Each of these is detectable in advance with frequency spectrum analysis.

FMEA detection rating calibration: When manufacturing engineers rebuild FMEA detection ratings for CCP-adjacent equipment using actual monitoring data (progressive failure timeline from Stage 1 detection to production-affecting failure), detection ratings for quarterly PM inspection-based controls typically increase by 3 to 5 points on the standard 10-point scale. This change in detection rating is often the trigger for prioritizing monitoring as a required additional control rather than a desirable improvement.

Pre-peak audit value concentration: In facilities with distinct seasonal production patterns, the pre-peak audit consistently identifies a subset of assets where monitoring-enabled intervention before the peak window produces a disproportionate fraction of the annual monitoring ROI. Peak-season failures cost 1.5 to 2.5 times off-season failures in four-component cost terms. Preventing one peak-season major failure often recovers a year or more of monitoring cost.