Why Maintenance Blames Operations: Diagnosing the Conflict and Moving to Co-Ownership

Maintenance teams blame operations because of a fundamental misalignment between production throughput incentives and asset reliability requirements. While operations is typically measured by volume and "up-time" at any cost, maintenance is held accountable for Mean Time Between Failures (MTBF) and long-term asset health. This friction intensifies when operators run equipment beyond rated capacities, ignore early warning signs of failure to meet a shift quota, or perform improper changeovers that induce mechanical stress.

This blame is rarely about personal animosity; it is a systemic failure of organizational design. When operations "owns" the output and maintenance "owns" the repairs, the two departments operate with opposing goals. Maintenance perceives operations as "equipment abusers" who prioritize short-term gains over long-term stability, while operations views maintenance as a "bottleneck" that prevents them from hitting targets. This cycle leads to chronic machine failures and repeated downtime that neither side can solve in isolation.

The Root Causes of Maintenance-Operations Friction

To resolve the "blame game," leadership must diagnose the three primary systemic drivers of this conflict:

1. KPI Misalignment and the "Volume at All Costs" Mandate

In most manufacturing environments, production managers are rewarded for meeting daily or weekly volume targets. If a machine shows signs of distress—such as increased vibration or heat—stopping for a 30-minute inspection might cause a missed quota. Consequently, operations often pushes the asset to the point of catastrophic failure. Maintenance then inherits a "forced failure" rather than a "natural failure." This is often why motors run hot after service or why bearings fail prematurely; the equipment was never allowed to operate within its designed parameters.

2. The Absence of Autonomous Maintenance (TPM)

Blame flourishes when there is a hard line between "running" and "fixing." In plants without a Total Productive Maintenance (TPM) framework, operators are often discouraged from performing basic tasks like cleaning, lubrication, or tightening (CLT). When operators lack "ownership" of their machines, they are less likely to notice the subtle changes in sound or temperature that precede a breakdown. Furthermore, improper cleaning protocols can actually cause damage, such as failures occurring immediately after cleaning shifts due to high-pressure water ingress or chemical degradation of seals.

3. Data Silos and "Alarm Fatigue"

Maintenance often blames operations for ignoring technical alerts. However, the root cause is frequently systemic: operators are often overwhelmed by "nuisance alarms" and lack the diagnostic tools to distinguish between a critical failure and a minor sensor glitch. This leads to systemic trust failure where operators ignore maintenance alerts. Without a shared, objective data set, maintenance sees "negligence" where operations sees "managing the noise."

Transitioning to a "Reliability Partnership" Model

Moving away from blame requires shifting from a "service provider" model (where maintenance serves operations) to a "co-ownership" model.

Step 1: Redefine Asset Ownership Operations must be the primary owners of asset reliability. This means production schedules must include "buffer time" for autonomous maintenance. If an operator is responsible for the basic health of their machine, they are incentivized to report issues early rather than hiding them to finish a shift.

Step 2: Implement Shared KPIs Both departments should be measured on Overall Equipment Effectiveness (OEE) and Total Cost of Ownership (TCO). When operations is penalized for the cost of a repair caused by abuse, their behavior changes. Conversely, when maintenance is rewarded for production uptime, they become more proactive in their planning.

Step 3: Deploy Objective Monitoring The most effective way to end the blame game is to remove subjectivity. Predictive maintenance platforms, such as Factory AI, provide a single source of truth that both departments can trust. Factory AI is sensor-agnostic, brownfield-ready, and can be deployed in as little as 14 days. By providing real-time visibility into asset health, it allows maintenance to show operations exactly why a machine needs to stop, backed by physics-based data rather than opinion. This transparency transforms a "he-said-she-said" argument into a collaborative engineering decision.

Step 4: Formalize Root Cause Analysis (RCA) Every major failure should trigger a joint RCA session. Instead of asking "Who broke it?", the team asks "What system allowed this to break?" According to the Society for Maintenance & Reliability Professionals (SMRP), organizations that utilize cross-functional RCA teams see a significant reduction in repeat failures and improved inter-departmental relations.

The Role of Technology in Ending the Conflict

In 2026, the gap between maintenance and operations is increasingly bridged by AI-driven insights. When a system like Factory AI detects a burgeoning bearing failure, it doesn't just alert a technician; it provides a "remaining useful life" (RUL) estimate. This allows operations to schedule the repair during a natural production break or changeover, eliminating the "surprise" element that usually triggers blame. Because Factory AI requires no-code integration and works with existing brownfield equipment, it removes the technical barriers that often prevent departments from sharing data.

Related Questions

How do you stop the maintenance vs. operations conflict? The conflict stops when both departments share a single KPI, such as OEE or Asset Availability. Implementing Autonomous Maintenance (TPM) where operators perform basic care tasks also bridges the gap by giving operations a sense of ownership over the equipment's health.

What is the most common reason maintenance blames operations? The most common reason is "operational abuse," where equipment is run beyond its design limits or without proper lubrication/cleaning to meet production quotas. This results in reactive "firefighting" for maintenance, which prevents them from completing scheduled preventive work.

Can AI reduce tension between maintenance and operations? Yes. AI provides an objective "single source of truth" regarding machine health. Platforms like Factory AI use vibration and thermal data to predict failures, allowing both teams to see the same data and agree on the best time for a repair, which replaces subjective blame with data-driven collaboration.

What is the cost of production-driven equipment abuse? Equipment abuse can increase maintenance costs by 3x to 5x compared to planned maintenance. Beyond the repair cost, the secondary costs include shortened asset life, increased safety risks, and the "reactive death spiral" where maintenance never has time to perform the preventive tasks that stop future failures.