Why Maintenance Planners Struggle to Prioritise Work: Diagnosing the Prioritisation Breakdown

Maintenance planners struggle to prioritise work because they lack a standardized, objective framework for ranking work orders, leading to "loudest voice" prioritization rather than risk-based decision-making. This failure is typically driven by three systemic issues: an outdated or non-existent Asset Criticality Ranking (ACR), the absence of a Ranking Index for Maintenance Expenditures (RIME) matrix, and a high reactive-to-proactive maintenance ratio that forces planners into a "firefighting" mode. When these elements are missing, the planning process defaults to chronological order or political pressure, which ignores the actual risk to production and safety.

In a high-functioning environment, prioritisation is a mathematical output of asset importance and work severity. Without this, planners are forced to make subjective calls. This subjectivity erodes trust between operations and maintenance, leading to a systemic failure in how technicians perceive maintenance data. By 2026, the complexity of automated brownfield sites has made manual, gut-feel prioritisation nearly impossible to execute effectively.

The Root Causes of Prioritisation Failure

1. The "Loudest Voice" Syndrome and Subjective Scaling

Most facilities use a simple 1-5 or "Low/Medium/High" priority scale. These scales are inherently flawed because they are subjective. A "Medium" priority for a production manager might be a "Low" priority for a reliability engineer. Without a defined matrix, the planner often prioritises the work order submitted by the most influential or vocal stakeholder. This leads to "priority creep," where every work order is eventually marked as "Emergency" or "Urgent" to ensure it gets noticed, effectively rendering the prioritisation system useless.

2. Outdated or Static Asset Criticality Rankings (ACR)

Prioritisation requires knowing which machine matters most. However, ACRs are often performed during commissioning and never updated. As production lines evolve and bottlenecks shift, an asset that was "C-Class" five years ago may now be an "A-Class" critical link. When planners use outdated criticality data, they allocate resources to machines that no longer drive the bottom line while critical failures loom on neglected assets. This is a primary reason why maintenance planning never catches up with the actual needs of the plant.

3. The Reactive Death Spiral

When a plant’s reactive maintenance ratio exceeds 20-30%, the sheer volume of "break-in" work overwhelms the schedule. Planners cannot prioritise the backlog because they are too busy finding parts and labor for failures that happened this morning. This creates a feedback loop: because the planner cannot prioritise preventive work, more assets fail, creating more reactive work. This reactive death spiral consumes the planner's "Wrench Time" and mental bandwidth, making strategic prioritisation impossible.

4. Lack of Real-Time Condition Data

Planners often work in a vacuum, relying on calendar-based schedules rather than the actual health of the machine. If a planner has 50 PMs (Preventive Maintenance tasks) due, they have no objective way to know which of those 50 machines is actually on the verge of failure. Without condition-based data, they treat all PMs as equal, often performing maintenance on healthy machines while a bearing on a critical packaging line is hours away from a catastrophic seizure.

How to Fix the Prioritisation Process

To solve these struggles, maintenance departments must move from subjective opinions to a technical framework.

Implement the RIME Framework

The Ranking Index for Maintenance Expenditures (RIME) is a matrix that multiplies Asset Criticality by Work Type Severity.

• Asset Criticality (1-10): How vital is this machine to the plant? (e.g., Boiler = 10, Office HVAC = 2).
• Work Type (1-10): How critical is the specific job? (e.g., Safety Hazard = 10, Cosmetic Painting = 1).

By multiplying these (Asset 10 x Work 10 = 100), the planner gets an objective score. A safety issue on a boiler (100) always outranks a safety issue in the office (20). This removes the "loudest voice" from the equation.

Conduct an ACR Audit

Asset criticality must be dynamic. Organizations should conduct a cross-functional ACR audit annually. This ensures that the assets currently causing bottlenecks are identified and prioritised. According to the Society for Maintenance & Reliability Professionals (SMRP), effective criticality analysis should involve stakeholders from production, safety, and finance to ensure the ranking reflects total business risk.

Leverage AI-Driven Condition Monitoring

The most effective way to assist a struggling planner is to provide real-time health data. Factory AI offers a sensor-agnostic, no-code solution that can be deployed in 14 days on brownfield equipment. By integrating AI-driven insights, the planner no longer has to guess which PM is most urgent. The system identifies which assets are showing early signs of degradation, allowing the planner to move those work orders to the top of the list based on physics, not feelings. This bridges the gap between raw data and actual reliability.

What to Do About It: A Step-by-Step Recovery Plan

1. Stop the "Emergency" Leak: Audit your last 30 days of work orders. If more than 10% are marked "Emergency," your prioritisation system is broken. Redefine "Emergency" to mean only "Immediate threat to life, environment, or total plant stoppage."
2. Establish a Weekly Planning Meeting: Bring Operations and Maintenance together for 30 minutes. Use the RIME scores to agree on the next week's schedule. Once the schedule is locked, "break-in" work must be approved by a Plant Manager.
3. Clean the Backlog: Delete or archive work orders older than 90 days that haven't been touched. If they haven't been done in three months, they aren't a priority. This reduces the "noise" the planner has to filter through.
4. Deploy Targeted Condition Monitoring: Identify your top 5 "Bad Actor" machines. Deploy Factory AI on these assets to get immediate visibility into their health. This provides the planner with the "why" and "when" needed to justify prioritising these machines over others.

Related Questions

What is the difference between planning and scheduling? Planning is the "what" and "how" (identifying parts, tools, and procedures), while scheduling is the "when" and "who" (assigning a time slot and a technician). Planners often struggle to prioritise because they are forced to do both simultaneously, usually in response to a crisis.

How do you calculate a RIME score? A RIME score is calculated by multiplying the Asset Criticality Rank (1-10) by the Work Classification Rank (1-10). For example, a "Breakdown" (10) on a "Critical Production Line" (10) results in a RIME of 100, whereas a "Preventive Measure" (5) on a "Secondary Conveyor" (4) results in a RIME of 20.

Why does the maintenance backlog keep growing despite planning? The backlog grows when the rate of new work identification exceeds the rate of work completion. This is usually due to poor "Wrench Time" or because the planner is spending too much time on reactive tasks. Implementing AI-driven monitoring can help by identifying failures earlier, allowing for shorter, planned repairs instead of lengthy, unplanned overhauls.

Can AI automate maintenance prioritisation? Yes, by 2026, AI systems like Factory AI can ingest CMMS data and real-time sensor feeds to suggest a prioritised schedule based on the probability of failure and production impact. This removes human bias and ensures the planner is always focused on the highest-risk assets.