How to structure maintenance in agricultural industries?

Table of content

In agricultural industries, maintenance is subject to short, intensive production cycles with little room for error. In this type of organization, performance depends not only on the robustness of the assets, but on how maintenance is structured, prepared and managed over time.

 

Key takeaways

  • Maintenance in agricultural industries is largely driven by campaigns: it must be planned before, during and after intensive production periods.
  • Reducing unplanned downtime starts with a clear prioritization of critical assets, supported by failure histories and risk analyses such as FMEA.
  • Preparation before the campaign and the standardization of interventions help secure restarts and limit deviations under pressure.
  • The CMMS plays a structuring role to manage, capture knowledge and improve from one campaign to the next, relying on simple indicators (MTBF, MTTR) and formalized field feedback.

 

 

Agricultural processing: heavily stressed assets

The agricultural processing industries face strong industrial constraints: large volumes, assets used continuously over concentrated periods and little tolerance for interruptions. Dryers, grinders, conveyors, presses or granulation lines run at high speed, sometimes continuously, for the duration of the campaigns.

In this configuration, production stops are not exceptional. They often follow recurring patterns linked to the processes and the assets themselves.

These assets are exposed to harsh operating conditions:

  • high mechanical loads
  • thermal stresses
  • dusty environments or abrasive materials,

Agricultural Processing

Before a stoppage, assets typically show warning signs such as:

  • increased vibrations or unusual noises,
  • performance or throughput drift,
  • unusual overheating,

When continuity of flow is prioritized, these alerts sometimes go unaddressed until the asset completely fails. Thus, repeated stops are not due to a lack of knowledge about the installations, but to the difficulty of anticipating and addressing these drifts before they become blocking.

 

Structuring maintenance to secure production campaigns

The repetition of stops during campaigns usually reflects a maintenance approach still too focused on emergencies. Structuring maintenance means shifting effort towards anticipation, targeting the assets, periods and failure modes that truly cause loss.

Identify and prioritize the assets that are truly critical

Not all machines carry the same level of risk for production. An asset is critical not because of its value or complexity, but because of its operational impact:

  • Impact on throughput and line continuity: an upstream conveyor or a dryer acting as a bottleneck will have immediate consequences on the whole flow.
  • Time to restore service: some assets require several hours or even days to be repaired or restarted.
  • History of campaign stoppages: recurring failures are reliable indicators of vulnerability.

This prioritization allows efforts to be concentrated where stoppages truly cost production, rather than spreading maintenance across the entire fleet.

It is possible to rely on a structured risk analysis, of the FMEA type, to objectify the most penalizing failure modes during campaigns and focus maintenance efforts where they have real impact on production.

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Prepare assets before ramp-up

The phase before the campaign largely determines production stability. Structured preparation relies on targeted actions adapted to the expected level of use.

  • Targeted pre-campaign maintenance: preventive replacement of sensitive components, mechanical reconditioning, functional checks.
  • Systematic checks: repeatable verifications based on known drift points rather than general inspections.
  • Adjust frequencies according to usage intensity: an asset used continuously cannot follow the same rules as during low-use periods.

The goal is not to over-maintain everything, but to secure weak points before production pressure makes any intervention more complex.

 

Standardize interventions to limit deviations

During campaigns, variability in practices is often an aggravating factor. When every intervention depends on individual experience, reproducibility is no longer guaranteed.

Standardization provides an operational framework:

  • Clear intervention procedures: defined steps, identified control points, controlled intervention times.
  • Reference settings: validated parameters that avoid empirical adjustments under pressure.
  • Reduced dependence on individual expertise: continuity is ensured even with team rotations or temporary reinforcements.

This approach does not freeze maintenance; it makes it more reliable. It limits deviations, secures restarts and facilitates the analysis of malfunctions when they occur.

 

Organize maintenance during intensive production

Once the campaign starts, room for maneuver is greatly reduced. Production stability then depends less on technical choices than on the daily organization of maintenance.

Move away from constant emergency handling

During intensive production, maintenance is often absorbed by emergencies. Teams follow one repair after another without a global view, with a high risk of decisions made under pressure.

Structuring the organization helps regain a minimum of control:

  • Clear prioritization: not all failures are equal. Distinguish what truly threatens production continuity from what can be deferred.
  • Anticipation of recurring interventions: some drifts always appear at the same times during the campaign. Identifying them allows planning short, targeted actions before a full stop.
  • Reduction of improvised decisions: simple rules for incident handling limit constant trade-offs and dependence on instant judgment.

The goal is not to eliminate emergencies—they are part of industrial reality—but to contain them so they do not become the dominant mode of operation.

Better coordinate maintenance and production

When cadence increases, relations between maintenance and production often become strained. Decisions are made quickly, sometimes with partial information, which fuels misunderstandings and frustrations.

More structured coordination relies on concrete foundations:

  • Shared data: asset status, recent incident histories, cumulative downtime. A common information base reduces subjective debates.
  • Objective trade-offs: deciding to stop, slow down or continue a line should be based on known criteria in advance, not only on momentary pressure.
  • Smoother communication under stress: short, regular, fact-focused checkpoints align teams without overburdening the organization.

This coordination aims to secure decisions. It allows maintenance to intervene at the right time and production to understand the technical consequences of certain choices.

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The CMMS: a lever for reliability and knowledge retention

When maintenance is heavily impacted by seasonal production cycles, the difficulty lies in keeping a reliable view of asset condition. Without structured information, each campaign starts with the same vulnerabilities. The CMMS provides a framework to make this operational memory reliable and usable over time.

Centralize and make maintenance data reliable

Before implementing a CMMS, some organizations recorded all their maintenance knowledge “on paper and in Excel files”, with very disparate information sources, as described by Alexandre Lelaurin, site manager at Luzeal, a multi-site agricultural cooperative.

 This organization makes intervention traceability difficult and weighs on operational decision-making.

The CMMS makes it possible to consolidate the elements useful for management:

  • Asset history: past interventions, recurring failures, modifications made. This traceability prevents starting from scratch each campaign.
  • Interventions and downtime: classification of stops, actual immobilization time, conditions for restoring service. These data provide a factual reading of weak points.
  • Parts and consumptions: tracking sensitive parts, anticipating needs before the campaign, limiting stockouts during high-demand periods.

Value lies not in the volume of data entered, but in their coherence and regular use.

Manage maintenance before, during and after the campaign

Seasonal maintenance requires phased management. The CMMS helps structure these phases without weighing down the organization.

  • Upstream preparation: scheduling preventive interventions, checking critical stocks, allocating resources before ramp-up.
  • Real-time monitoring: visibility on incidents, priorities and the state of sensitive assets.
  • Post-campaign analysis: identifying penalizing assets and tracking simple indicators like mean time between failures (MTBF) or mean time to repair (MTTR).

This continuous management allows practices to be gradually adjusted rather than enduring the same stops from one campaign to the next.

 

Capture field experience from one campaign to the next

Structuring also involves integrating field feedback. Luzeal’s experience illustrates the issue well:

“We created preventive procedures, which greatly simplifies the daily work of the maintenance manager at that level”, emphasizes Alexandre Lelaurin after implementing their multi-site CMMS.

Read the testimonial

This type of knowledge retention facilitates standardization of interventions and turns experience into concrete actions. The CMMS makes it easier to collect field feedback to adjust preventive measures and progressively reduce unplanned stoppages.

 

In agricultural industries, structuring maintenance depends as much on team organization as on the ability to rely on reliable, shared information. The CMMS fits into this logic as a management tool, allowing to consolidate asset histories, monitor campaign drifts and capture experience from year to year.

By providing visibility before, during and after campaigns, the CMMS helps maintenance and operations managers make objective decisions, adjust preventive measures and better control unplanned stoppages. It contributes to evolving maintenance toward a more stable, more predictable operation better aligned with the realities of agricultural production.

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