How to Reduce Unplanned Downtime in the Dairy Industry?

In the food industry, and more specifically in dairy plants, unplanned downtime is never just a simple technical issue. It impacts food safety, regulatory compliance and the stability of production flows.

Reducing unplanned downtime sustainably requires more than a rapid reaction: it means anticipating failures, securing critical assets and structuring maintenance around food safety risk.

 

 

1. Precisely identify which unplanned stops are truly critical

Effective identification of unplanned stops relies on a factual, prioritised analysis. The goal is not to treat all incidents the same way, but to focus maintenance resources on those that cause the greatest losses, food safety risks or process disruption.

 

Which unplanned stops cause the biggest production losses in dairies?

In dairy workshops, the most costly stops usually combine production impact + food safety impact:

• Failure of cooling assets (refrigeration units, cold rooms, cooling tunnels)
• Stop or drift in CIP cycles, preventing return to production
• Breakdowns in critical thermal steps: pasteurisation, UHT sterilisation, heat exchangers
• Blockage of packaging lines requiring a full cleaning before restart

 

These stops lead to:

• loss of volumes,
• quality non-conformities,
• overload on teams,
• increased pressure during IFS/BRC audits.

 

The first concrete action is to qualify and precisely classify stops (technical, cleaning, quality, food safety) in order to focus efforts where the risk is highest.

 

2. Use FMEA to prioritise sensitive assets

The FMEA method, which identifies and ranks failure risks on assets, remains a relevant tool provided it is adapted to the specific constraints of milk processing and maintained over time.

Effective structuring involves logging and analysing past events, made possible by tools like the CMMS, which make it easier to update analyses and prioritise critical assets.

« The CMMS now allows COPAG to better manage its maintenance. We move from a traditional organisation based on human memory to a modern setup that provides instant visibility of activities. It facilitates FMEA analysis thanks to a reliable history. »
— Hicham LAABADI, Deputy Head of Methods Department, COPAG

 

FMEA principle diagram: analysis and ranking of failure risks

 

ASSET / PROCESS
⬇
FAILURE MODES
(How can it fail?)
⬇
EFFECTS OF THE FAILURE
(Impact on production, quality, safety…)
⬇
ROOT CAUSES
(Wear, misadjustment, fouling, human error…)
⬇
RISK ASSESSMENT

• Severity (S)
• Occurrence (O)
• Detectability (D)
  ⬇

CALCULATION OF RPN
(Risk Priority Number = S × O × D)
⬇
MAINTENANCE ACTION PLAN

• Preventive
• Predictive
• Targeted corrective
  ⬇
  CONTINUOUS UPDATING
  (Experience feedback analysis via the CMMS)

 

3. Move from systematic preventive maintenance to anticipating drifts

The most damaging stops are rarely sudden: they are preceded by weak signals or drifts that only cross-monitoring can detect.

Which critical parameters should be monitored to prevent stops?

Dairy assets operate with very narrow margins. The most critical drifts concern:

• temperatures (cooling, pasteurisation),
• pressures and flows,
• conductivity and CIP results,
• abnormal cycle times.

Cross-reference these process data with:

• failure history,
• maintenance interventions,
• quality non-conformities,
• to act before a stop.

 

Proactive analysis of alarms, quality drifts and downtime is essential. This requires structured access to the complete history of assets.

« Thanks to the CMMS, during an IFS/BRC audit we were able to retrieve work orders and cleaning records by date in just a few clicks. It is an invaluable time saver and a guarantee of compliance. »
— Romain Bernard, Intervention Team Manager, Eurial

 

From preventive maintenance to predictive maintenance in the dairy industry

While preventive maintenance reduces recurring failures, it can reach its limits in an environment as constrained as the dairy industry.
Intervening too early can generate unnecessary asset openings, and therefore additional cleanings. Intervening too late exposes you to an unplanned stop with food safety risk.

It is within this balance that predictive maintenance makes sense. Paired with a CMMS, it links process data, failure histories and past interventions to trigger targeted actions at the most relevant moment.

 

What is predictive maintenance?

Predictive maintenance consists in anticipating a failure from actual operating data, rather than relying on simple calendar periodicity.
It is based on analysing weak signals that indicate a progressive drift of an asset.

Maintenance then becomes a lever to improve process reliability, not just a cost centre.

 

4. Manage downtime reduction over the long term

 

The most effective maintenance KPIs are often familiar indicators, but adapted to the sanitary and process context.

Unplanned downtime by asset family / MTTR (Mean Time To Repair)

This indicator measures the total downtime resulting from unplanned failures, broken down by asset type (cooling, pasteurisation, packaging, etc.).

• It helps identify the assets that keep lines immobilised the longest.
• In the dairy industry, it often includes cleaning and requalification time, not just repair time.

Objective: reduce the duration and impact of stops, not just repair faster.

 

Recurrence rate of critical failures / MTBF (Mean Time Between Failures)

The recurrence rate measures how often the same critical failure reoccurs on an asset.

• A low MTBF indicates a recurrent failure that is poorly addressed.
• In the dairy context, these failures are often linked to fouling, thermal drifts or continuously stressed components.

Objective: make critical assets reliably robust over time, by adjusting preventive and predictive maintenance plans.

 

Time between alert and intervention / Maintenance responsiveness (time to start)

This KPI measures the time elapsed between the occurrence of an alert (process alarm, quality drift, operator signal) and the actual start of the maintenance intervention.

• It depends not only on technical skill but also on organisation.
• A CMMS helps reduce this time by structuring alerts and priorities.

Objective: intervene before a total stop or a food safety non-conformity.

 

Number of stops leading to lot withdrawals or destructions

This indicator is not directly an MTTR or MTBF, but it is fundamental, because it is specific to the dairy industry

• It directly links maintenance to quality and food safety.
• It helps identify failures whose impact goes beyond mere machine availability.

Objective: prioritise high food-safety-risk assets, even if they fail infrequently.

These indicators require a solid, shared database — a role naturally filled by a well-deployed CMMS.

 

Conclusion

Reducing unplanned downtime in the dairy industry does not rely on a single action, but on a collective ability to identify, anticipate and capitalise.

This approach requires rigorous structuring of information from the field, assets and quality. The CMMS then becomes more than a planning tool: it is the foundation that allows past incidents to be turned into controlled decisions, serving plant availability and food safety.