Deferring a turnaround can be a rational business decision when margins are strong, and every additional day of throughput has value. Recent industry activity also shows that major refiners have reduced projected turnaround and capital spending while continuing to run at high utilization.
Though the business case is clear, extending the operating window only protects margin when the maintenance strategy adapts as operating conditions change.
Recurring scope growth between turnaround planning and execution validates this concern. Asset Performance Networks — an independent firm that benchmarks turnaround and capital-project performance — has tracked more than 1,350 turnarounds. Across that database, scope grows by an average of 19% between planning and execution, while top-quartile operators hold it to 7%.
19% average turnaround scope growth across 1,350+ turnarounds.
Top-quartile operators hold it to 7%. The gap is a maintenance visibility failure, not a planning failure.
(AP-Networks)
Deferring a turnaround while relying on a maintenance strategy that no longer reflects plant conditions can put future-quarter margins at risk.
The Real Risk in Every Deferral Decision Is Lack of Expertise
Your monitoring tells you something is changing. It does not tell you how your PM plan should respond.
The difference between a defensible deferral and an exposed one is the quality of the maintenance reasoning behind every preventive maintenance (PM) decision. Teams that rely only on alert monitoring systems struggle to adjust preventive maintenance plans as operating parameters change. They also lack a scalable way to reason through legacy maintenance work orders and reduce corrective maintenance (CM) costs.
That requires ingesting multiple data sources and interpreting legacy operating data with the judgment of seasoned reliability engineers. But amid the expertise exodus, most teams still rely on a legacy maintenance strategy written once based on a reliability-centered maintenance (RCM) study, a vendor recommendation, or inherited engineering practice.
Extending a run with stale maintenance strategies can compound margin loss because late scope discovery is expensive.
This plan no longer reflects how the plant runs today, and every deferral decision made with a static maintenance strategy compounds scope growth later. Scaling expertise and decision capacity is key to reducing future scope-growth risk as teams make real-time adjustments: should the preventive maintenance interval shorten, should the task list expand, should the job be bundled differently, or is the real cause somewhere upstream?
From Static Plans to Continuous Strategy with Maintenance Optimization Agent
The standard deferral framework — maintenance history, engineering judgment, and past reliability-centered maintenance (RCM) assessments — answers whether the probability of failure was acceptable at the last assessment. But it does not always answer whether the current operating envelope, feed slate, asset condition, and failure trajectory still support extending the run.
As operating conditions shift, static RCMassessments lose relevance.
Static preventive maintenance plans even faster.
The Maintenance Optimization Agent operationalizes this continuous maintenance strategy by re-evaluating PM intervals as conditions change, weighing asset criticality, reliability, economics, and failure data to recommend evidence-backed interval changes. Updates approved by reliability engineers flow directly into the computerized maintenance management system (CMMS) or enterprise asset management (EAM) system for execution by maintenance teams across all sites.
The shift from static maintenance planning to continuous maintenance strategy changes how PM intervals, runtime risk, maintenance prioritization, and execution decisions are evaluated across the operation.
Maintenance Optimization Agent Makes the Extended Run Defensible
Every PM decision is traced to its evidence. Every task is prioritized based on current condition, not last cycle’s plan.
The Maintenance Optimization Agent ingests historian data, CMMS records, engineering documents, and process context — then applies embedded expert reasoning across asset criticality, failure modes, economics, and operating evidence to re-evaluate the preventive maintenance (PM) plan at the individual equipment level, not at the generic asset-class level.
The engineer stays in the loop. Every recommendation carries transparent evidence explaining what is proposed and why. The reasoning is auditable to the reliability team, the board, and, where applicable, regulators.
Case Study
Industry Vertical: Power
Maintenance Optimization Agent Identifies ~$300K in Coal Power Plant CM & PM Cost Savings
Read Case Study
What Changes When PM Strategy Never Stops Updating
The deferral isn’t the risk. It’s the static plan.
When PM strategy adapts continuously, scope surprises stop showing up at the turnaround gate, and required changes are identified before contractor rates are locked. The work that can safely extend is justified by current process conditions, not a probability assumption from 18 months ago. The results of continuous PM optimization compound over time:
This is what scaling reliability judgment looks like: encoding PM reasoning into a system that runs continuously, learns from every cycle, and compounds institutional knowledge instead of losing it to retirement.
What Changes When PM Strategy Never Stops Updating
Replace one-size-fits-all PM plans with continuous, evidence-backed PM recommendations that adapt with your plant and integrate directly into your CMMS.
See the Maintenance Optimization Agent in action.
