How Plant Directors in Food and Beverage Have Reduced Portfolio-Wide Downtime

The most credible input to a portfolio-level reliability investment decision is not a vendor claim. It is a peer outcome: a company with assets like yours, production conditions like yours, and a portfolio risk profile like yours, that deployed a monitoring program and can document what happened in dollar terms.

In food and beverage, the conditions that make peer evidence compelling are specific. A case study that shows a reduced unplanned downtime rate at a single dairy facility is useful context. A case study that shows how a multi-site F&B operation standardized condition monitoring across facilities with different equipment vintages, reduced their aggregate peak-season failure rate, and built a financial case that justified the program to their CFO is a directly applicable reference.

This article covers what the Tractian case study library shows from F&B operations, how to read peer results without over-applying them to your own portfolio, and the common patterns across the F&B operations that have reduced portfolio-wide downtime most effectively.

What F&B Case Studies Show

Tractian's case study library at tractian.com/en/case-studies includes results from food and beverage operations ranging from ingredient processors to packaged food and beverage producers.

The consistent pattern across F&B case studies:

Continuous monitoring identifies faults during production that PM schedules cannot capture. F&B processing assets run at load during production and at low or zero load during CIP and maintenance windows. Vibration data collected during a sanitation cycle captures the low-load state, not the production state. Faults that develop at full production load are not detectable on a PM schedule that only accesses the asset during low-load windows. Continuous monitoring during production captures the condition data that determines whether an asset reaches the next planned maintenance window or fails mid-run.

The avoided failure on a single Tier 1 asset frequently justifies the program cost. In F&B operations, a mid-run failure on a Tier 1 processing asset produces the full four-component cost simultaneously: production loss, product disposal, sanitation restart, and emergency repair premium. For dairy operations during spring flush, add raw milk diversion. The aggregate cost of one prevented major failure at peak-period production value regularly exceeds the annual program cost for a single site. At the portfolio level, the ratio between program cost and aggregate risk protection is more favorable.

Asset health visibility before peak season changes the pre-peak intervention pattern. F&B operations using continuous monitoring shift from reactive discovery of failing assets (the failure occurs during peak and triggers an emergency response) to proactive intervention (the monitoring data identifies an asset trending toward failure before peak opens, and the repair occurs at planned cost before the peak window). The financial delta between those two scenarios is the emergency repair premium plus the five-component peak-season failure cost avoided.

Ingredion: Large-Scale F&B Operations

Ingredion is a large-scale ingredient processing operation with facilities processing corn and other agricultural inputs into food-grade starches, sweeteners, and other ingredients. Their North Kansas City plant represents a deployment where continuous monitoring across hundreds of machines running around the clock produced documented financial outcomes at program scale.

At the North Kansas City plant, traditional maintenance tools created specific gaps: hard-to-reach assets caused delays in inspections, no early warnings meant failures were caught too late, and PM schedules missed subtle issues like alignment problems or bearing wear. In one case, a critical DSM pump with no spare parts on hand failed, leading to a full three-day shutdown.

Key results at the North Kansas City plant:

• $1,000,000 in production savings
• $223,000 in maintenance savings
• 48 to 168 hours of avoided downtime across critical equipment
• A critical DSM pump shutdown avoided through early looseness detection

"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

Why this is relevant at the portfolio level: Ingredient processing facilities share key characteristics with many F&B portfolios: continuous process operations with high-consequence rotating assets, significant product disposal risk from mid-run failures, and high production value per operating hour. The standout example from this deployment -- a single DSM pump with no backup and a known three-day outage history -- illustrates how one avoided failure on a correctly prioritized Tier 1 asset can cover a significant portion of year-one program cost.

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

Unilever and Major F&B Producers

Unilever (Knorr, Hellmann's) represents a large-scale F&B producer deployment where the program produced documented financial outcomes at portfolio scale across a 112-day monitoring window in Q2 2025. For a Plant Director managing a multi-site F&B portfolio, this deployment is relevant because it reflects the enterprise scale and organizational complexity of standardizing a monitoring program across facilities with high-value branded production lines.

At the Latin America plant where Tractian was deployed, 40 critical assets were covered by 320 sensors. The monitoring program delivered outcomes within the first season of deployment.

Key results (Q2 2025, 112 days):

• $796,000+ in protected or avoided corrective costs
• 19 failures anticipated before they occurred
• 117 hours of avoided unplanned downtime
• 100% sensors online throughout the monitoring period
• Three standout saves: $250,000+ avoided on KSM Tank 02 mechanical looseness (24 hrs); $200,000+ avoided on Syrup Elevating Screw bearing wear (8 hrs); $135,000+ avoided on Vacushear bearing wear (8 hrs)

The portfolio-level relevance: This deployment demonstrates that a monitoring solution can be deployed consistently across a large asset population (40 assets, 320 sensors) without requiring site-by-site custom evaluation. The per-event financial documentation structure, where each avoided failure is logged with a specific asset, failure mode, and estimated cost, is the evidence format that makes a multi-site portfolio case defensible to leadership. One avoided failure at KSM Tank 02 represented more than a quarter million dollars of protected production value. That event-level specificity is what separates a portfolio case study from a percentage reduction claim.

Read the full case study: Unilever Case Study

Common Patterns Across F&B Results

Across Tractian's F&B case study library, four patterns appear consistently.

Pattern 1: Separator and compressor failures are the highest-value catches.

High-speed centrifuges (separators) and refrigeration compressors are the assets where bearing failure is most destructive and most costly. A separator running to catastrophic failure wraps a bearing failure into structural damage across the whole unit: the spinning mass and energy involved mean a late-stage bearing failure can cause cascading damage that turns a component replacement into a major overhaul. A refrigeration compressor failure at a dairy facility during spring flush creates not just production loss but milk diversion cost that can be substantial.

Early-stage detection on these assets, before the fault develops into structural damage, is the highest-value outcome in F&B condition monitoring. Case studies that show early-stage bearing fault detection on separators or compressors, with documented repair cost versus estimated catastrophic failure cost, are the most financially compelling peer evidence available.

Pattern 2: The gap between monitoring cost and risk exposure is most visible at lagging sites.

F&B case studies consistently show the highest ROI at facilities that were most reactive before deployment: high reactive maintenance rates, no pre-peak monitoring practice, and a history of peak-season failures. For a Plant Director whose portfolio has lagging sites with those characteristics, the comparable case study evidence is the most directly applicable.

The implication for portfolio deployment: start with your highest-risk lagging sites. The ROI is fastest there, and the case study comparables are most directly relevant. Use those first-phase results to build the business case for extending the program to lower-risk sites.

Pattern 3: The financial documentation starts at the first avoided failure, not at year-end.

Operations that build a strong financial case for their monitoring program typically document each avoided failure at the time it is prevented, not in an annual review. For each asset that generates an alert, a maintenance team responds, inspects, and confirms a developing fault. At that point, the five-component failure cost is estimable: what would it have cost if that asset had failed mid-run? The estimated avoidance is logged against the monitoring program.

That practice, consistently applied, produces a cumulative avoided failure log that is more compelling than a percentage reduction in a year-end report. It shows the board specific events, specific assets, and specific dollar figures, which is the form of evidence that moves capital decisions.

Pattern 4: Pre-peak health reviews become a standard portfolio management practice.

F&B operations that have used Tractian for more than one full peak season typically adopt a pre-peak health review as a standard protocol: a formal review of asset health data across all monitored sites in the six to eight weeks before each seasonal peak. The review identifies assets with developing faults that would likely produce failures during peak, allowing intervention at planned cost before the peak window opens.

Over two or three peak seasons, this practice produces a documented record of pre-peak interventions and their estimated financial value. That record is the portfolio-level career documentation described in the career article in this series.

How to Apply Peer Results to Your Portfolio Decision

Case study evidence is input to your decision, not a projection of your outcome. Three disciplines for applying peer results correctly:

Match the operational context before the percentage. Before focusing on the improvement percentage in a case study, verify: Is the asset category comparable to your Tier 1 assets? Is the production model continuous process, batch, or discrete? What was the pre-deployment maintenance maturity? A 40% reduction in unplanned failures at a highly mature dairy operation with modern equipment is a different benchmark than a 40% reduction at an aging poultry facility with reactive maintenance practices.

Use the case study to anchor your baseline, not to project your outcome. The most credible use of peer evidence in a business case: "Operations comparable to our lagging sites have documented avoided failure costs on Tier 1 assets that exceed their annual program costs within the first 12 to 24 months of deployment. We are proposing a conservative estimate of [percentage] risk reduction at our two highest-risk sites as the basis for Phase 1 ROI." That framing is defensible. A direct projection is not.

Present a range, not a single projected outcome. Derive a conservative estimate using 20 to 30 percent of the peer result as your projection for a lagging site. Use 50 to 60 percent as a moderate estimate. Use 70 to 80 percent as an optimistic estimate. Show the board the range. The conservative estimate is the one you commit to. The moderate and optimistic cases show the upside if the program performs closer to peer evidence.

What Validated Results Look Like

For F&B operations using Tractian, validated results follow a consistent documentation structure:

1. Baseline documented before deployment: Current annual downtime cost per site using the five-component calculation. Current reactive maintenance rate. MTBF on Tier 1 assets.
2. Alert confirmed by maintenance inspection: The first time an alert leads to a confirmed developing fault and a planned repair, that event is documented as a confirmed avoided failure.
3. Financial value estimated at time of avoidance: Five-component failure cost estimated for each confirmed avoided failure, using actual production value per hour and actual emergency repair premium data from the facility.
4. Cumulative log maintained per site: Each confirmed avoided failure adds to the cumulative program value log.
5. Year-end summary presented against program cost: Annual program cost versus cumulative avoided failure cost and any improvement in availability, MTBF, or peak completion rate.

That documentation structure is what makes the financial case defensible to a CFO or board. It is built from confirmed events, not projected percentages.

For the full library of Tractian F&B case studies, including Ingredion, Kraft Heinz, and other major F&B operations, visit tractian.com/en/case-studies.