How VPs of Maintenance in Food and Beverage Have Standardized Reliability Across Plants

The VP of Maintenance who presents an enterprise reliability program to the board for the first time is typically presenting a plan: a maturity assessment, a standardization roadmap, a technology platform, and a financial projection. The CFO who approves that investment is accepting a set of assumptions.

The VP of Maintenance who presents the same program 18 months later is presenting results: a documented reduction in unplanned downtime events, an improved planned-to-unplanned ratio across the portfolio, a measurable change in FSMA compliance coverage, and a board-level downtime cost comparison against the baseline from Year 1. That second presentation is what builds sustained board credibility and what positions the maintenance function as a capital protection program rather than a cost center.

This guide covers how food and beverage enterprises have used Tractian to make that transition (from reliability program plan to documented results) at enterprise scale. It also covers the most common mistakes VPs of Maintenance make when applying single-site vendor results to enterprise deployment decisions, and the questions that separate useful references from curated case studies.

Enterprise Case Studies

Ingredion

Ingredion operates ingredient processing facilities across multiple regions, producing starches, sweeteners, and texturants for food and beverage manufacturers globally. Their maintenance function faces the enterprise-scale challenge of monitoring processing equipment across chemically demanding environments, while meeting food safety documentation requirements for ingredient-grade production.

At the North Kansas City plant, Tractian deployed continuous monitoring on critical rotating equipment. The deployment documented early detection of degradation on high-criticality assets before failure events occurred, including a DSM pump with no spare parts on hand and a known history of three-day outages.

Documented results at North Kansas City:

• $1,000,000 in production savings
• $223,000 in maintenance savings
• 48 to 168 hours of avoided downtime on critical equipment
• A critical DSM pump shutdown avoided after looseness detected before failure

"I said, 'We need to do something different. The way we're doing maintenance -- it can be easier, faster, smarter.' This is the moment to bring in technology. We're not heading toward job loss -- we're heading toward a new era. Reliability roles are evolving. We're becoming data scientists, not just wrench turners." -- Rafael Padilha, Maintenance and Reliability Director, US/CAN, Ingredion

For the VP of Maintenance evaluating this reference: Ingredion's operation represents an enterprise deployment across chemically demanding F&B environments, a meaningful comparator for food ingredient and specialty food manufacturing portfolios.

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

Unilever

Unilever (Knorr, Hellmann's) operates one of the largest food and beverage manufacturing enterprise portfolios globally. At enterprise scale, the maintenance challenge is standardizing reliability programs across facilities with high-value branded production lines, diverse processing assets, and continuous regulatory scrutiny.

Tractian deployed at a Unilever Latin America plant with 40 critical assets covered by 320 sensors. The monitoring program produced documented financial outcomes within the first 112 days of the Q2 2025 monitoring period.

Documented results (Q2 2025, 112 days):

• $796,000+ in protected or avoided corrective costs
• 19 failures anticipated before occurring
• 117 hours of avoided unplanned downtime
• 100% sensor uptime throughout the monitoring period
• Three highest-value saves: 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)

For the VP of Maintenance evaluating this reference: Unilever's deployment scale (40 assets, 320 sensors) and the speed of financial documentation (19 failures anticipated within 112 days) demonstrates enterprise deployment capability in a demanding F&B processing environment.

Read the full case study: Unilever Case Study

Lyka

Lyka, an Australian pet food scale-up targeting 5x expansion in three years, represents a food manufacturing environment where maintenance program standardization across production lines is directly connected to product quality consistency and food safety outcomes.

Tractian's deployment at Lyka combined CMMS (integrated with Oracle NetSuite) and condition monitoring sensors deployed on-site by Tractian's customer success team. The deployment addressed both the information management challenge (centralizing equipment manuals, SOPs, drawings, and PM checklists) and the asset health monitoring challenge.

Documented results:

• Warehouse part lookup time reduced from 22 minutes to 22 seconds after CMMS deployment
• Two critical equipment failures detected within the first week of condition monitoring sensor deployment
• Temperature spike caught: failed fans on two key motors identified before escalating to full motor replacements or spoiled product

"Nothing was too much trouble for them, and they went to the nth degree to understand Lyka's business and asset care short and long-term strategic plan. This level of seeking to understand made the implementation particularly user-friendly." -- Andy Baxter, Head of Operations, Lyka

For the VP of Maintenance evaluating this reference: Lyka's case illustrates both the connection between condition monitoring and food safety documentation and the speed of first-detection events (two critical failures within week one), which is the benchmark for enterprise deployment velocity.

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

The Enterprise Deployment Difference

The cases above share a pattern that distinguishes enterprise deployment from site-level deployment.

A central data layer replaces manual aggregation. In each enterprise deployment, the VP of Maintenance gains visibility into portfolio-wide asset health without manually consolidating site-specific reports. The platform aggregates condition data across all deployed sites into a single dashboard, allowing portfolio-level reporting on planned-to-unplanned ratio, FSMA compliance coverage, and aggregate alert frequency.

Pre-peak visibility is portfolio-wide. Peak season in F&B simultaneously pressures all sites. Enterprise-deployed condition monitoring gives the VP of Maintenance visibility into which sites have assets trending toward elevated risk before the peak window opens, not after a failure occurs during it. This capability is not available from a portfolio of site-level monitoring tools. It requires a platform whose data aggregates across all sites in a consistent format.

FSMA compliance documentation is enterprise-standard. Each deployment produces a consistent, timestamped record of condition monitoring data for regulated assets across all sites. The enterprise compliance rate (the percentage of FSMA-regulated assets with current, documented monitoring) can be reported from the platform across the entire portfolio. This is the documentation foundation that supports regulatory audit defense at the enterprise level.

Workforce knowledge is captured in the platform. As monitored assets accumulate condition history (vibration signatures, temperature trends, alert records, failure-avoidance events) that history becomes an institutional asset memory that persists regardless of personnel changes. The knowledge retention challenge that characterizes F&B maintenance departments does not disappear, but its impact on program continuity is reduced.

What to Ask a Reference Before You Decide

The VP of Maintenance who wants useful reference information needs to ask questions the vendor case study did not answer. Six questions produce the most decision-relevant responses.

Question 1: What was the planned-to-unplanned maintenance ratio across your portfolio at deployment, and what is it now?

This is the single most direct indicator of whether the platform is changing maintenance program behavior, not just generating alerts. A ratio that has not improved suggests the platform is producing data that is not being acted upon.

Question 2: How many FSMA-regulated assets are now continuously monitored that were not before the deployment?

This quantifies the compliance coverage improvement, which is the regulatory risk dimension of the enterprise business case. A reference who cannot answer this question may not have used the platform to address the food safety compliance layer.

Question 3: What did your enterprise downtime cost baseline show in Year 1, and how does it compare to the current year?

This is the financial outcome question. If the reference has a documented baseline and a documented current-year comparison, their results are verifiable. If they can only describe qualitative improvements, the financial evidence is thinner.

Question 4: How did the platform perform during peak season? Did it surface any critical alerts before failures occurred?

This validates the specific use case that is most financially significant in F&B: early detection during the highest-cost operating window. An answer that includes a specific asset, a specific alert, and a specific outcome is a meaningful data point. A general assertion that "the platform caught things early" is less useful.

Question 5: What was the deployment experience at sites 3 through 6, compared to the initial pilot sites?

This is the enterprise deployment model question disguised as a reference question. If sites 3 through 6 required the same level of effort as sites 1 and 2, the platform scales. If each new site required a standalone implementation engagement, the platform is a collection of site installations rather than an enterprise deployment.

Question 6: What would you do differently in the deployment process if you were starting today?

This is the question that produces the most honest responses. Deployment lessons that the reference company learned at their own cost are worth more to the decision-making VP of Maintenance than anything the vendor will volunteer.

Peak Season Visibility at Portfolio Scale

Peak season is the operating condition that makes enterprise reliability programs valuable rather than aspirational. It is also the condition under which an inadequate program becomes most visible.

In an F&B enterprise deploying condition monitoring across multiple sites, peak season creates a specific monitoring challenge: the VP of Maintenance needs to know, simultaneously, which assets at which sites are trending toward elevated risk, not after a failure occurs, but while there is still time to intervene.

A portfolio of site-level monitoring tools requires the VP of Maintenance to review eight separate site dashboards and manually consolidate the risk picture. By the time that process completes, a site that was showing early degradation signals may have already experienced the failure.

Tractian's portfolio dashboard aggregates asset health data across all sites in a single view. During the six to eight weeks before any seasonal peak, the VP of Maintenance can review the enterprise risk picture (which sites have assets showing elevated vibration signatures, elevated temperature trends, or declining MTBF) and intervene with capital, contract resources, or production schedule protection before the window closes.

This is the capability that makes peak season portfolio management a documented enterprise competency rather than a reactive emergency response. The enterprises in the case studies above have deployed this capability at their scale. Their reference conversations will describe how it works in practice.