Maintenance turnarounds eat up to 70% of downtime in capital-intensive industries. Here’s how digital and AI tools help operators capture untapped value.
Maintenance turnarounds sit at the core of the asset life cycle in capital-intensive industries — oil and gas, chemicals, power generation, mining, metals, cargo shipping, cruise lines, and airlines among them. These planned shutdowns are essential to sustain, repair, and upgrade critical infrastructure, and they disproportionately shape long-term economics.
In oil and gas facilities, turnarounds represent 5 to 15 percent of a site’s cost base and account for 25 to 50 percent of total downtime. In mining, they’re responsible for 50 to 70 percent of downtime.
These events also carry real cash flow consequences, reducing a site’s output while sharply increasing capital and maintenance expenditure — and cost and schedule compliance challenges have only gotten more acute amid labor cost inflation and shortages of skilled maintenance personnel.
A poorly planned or executed turnaround can erode margins and introduce reliability risk for years afterward. A well-executed one can reset performance and unlock sustained value. For asset leaders, these events are often make-or-break moments — yet many organizations still treat turnaround performance as an operational issue rather than a strategic lever.
Why Turnarounds Are So Hard to Get Right, Consistently
Turnaround outcomes vary widely across organizations, and even between sites within the same portfolio. Part of the problem is structural: large-scale turnarounds happen only every few years at a given site, making it difficult to institutionalize learnings or transfer experience from one event to the next.
Accountability is typically delegated to site-level teams whose experience with events of this scale varies significantly, while skilled craft labor and experienced frontline supervisors grow scarcer. Heavy reliance on contractors for planning and execution can further dilute internal ownership and limit knowledge retention.
The operational complexity compounds this. Coordinating turnarounds across a network of assets means balancing timing, scope alignment, shared resources, and cash flow all at once — and even where standardized turnaround methodologies exist, leadership rotation, local practices, and time pressure often lead to inconsistent application.
What Excellence Actually Looks Like
High-performing organizations converge on a similar playbook: standardizing and institutionalizing best practices through a turnaround center of excellence that captures and disseminates lessons learned; setting aspirational, credible targets by applying a “challenge-in” mindset to cost, duration, and scope early, before constraints get baked into plans; and investing in people through explicit career paths in maintenance and turnarounds, rather than treating them as episodic assignments.
These practices show up directly in results. High performers extend turnaround intervals by 5 to 20 percent through disciplined risk management. They cut turnaround duration by up to 10 percent through tighter scope control and critical path optimization. They improve annual availability by 1 to 5 percentage points by aligning scope with asset strategy.
They also lower turnaround costs by 10 to 20 percent by eliminating non-value-added work while protecting reliability. In refining specifically, moving from average to top-quartile turnaround performance can lift EBITDA by 5 percent.
Where Digital and AI Tools Are Changing the Equation
The most advanced organizations are now layering digital and AI tools on top of these fundamentals — not as stand-alone innovations, but as enablers that reinforce process discipline, augment human judgment, and improve decision-making across all four core dimensions of turnaround performance: interval, scope, schedule, and execution.
On interval optimization, gen AI tools can now systematically analyze the factors that determine the maximum safe interval for an asset, combining top-down benchmarks with bottom-up equipment-specific knowledge.
One refinery operator used this approach, paired with strong process fundamentals, to extend a crude unit’s full turnaround interval by 30 percent — replacing time-based inspections with a refreshed risk-based inspection program and continuous corrosion monitoring, while deferring a full turnaround with a targeted mid-cycle pit stop instead.
Scope definition is one of the most contested parts of turnaround planning, since operators are tempted to over-include work items during a rare shutdown window — which compounds cost and schedule risk — or to cut too aggressively and expose the site to unplanned failures. Digital scope optimizers apply historical failure data and ROI logic to individual work items to set objective, data-backed thresholds.
One mining company’s early-failure-prediction system reduced total downtime by 5 percent; in one case, it predicted a greater than 90 percent failure probability for one conveyor while giving a similar asset less than a 10 percent probability — letting the company remove the second conveyor’s replacement from scope entirely, and it ran reliably through the next cycle.
Schedule optimization tackles a problem that’s simply too large for manual planning — turnaround schedules can involve tens of thousands of interdependent activities. Generative scheduling tools can test thousands of scenarios rapidly; one chemicals company used this to remove three days from an original 35-day turnaround window by moving certain system start-up times earlier and increasing night-shift capacity on a critical-path resource by 50 percent.
And on execution, where decision cycles are measured in hours rather than days, real-time analytics and digital control towers are giving turnaround leaders visibility they’ve historically lacked. One refining company introduced digital cameras and ID badges to track worker locations in real time — improving confined-space safety monitoring, giving leaders real productivity data on actual “wrench time” by team, and even allowing some inspection tasks to be completed remotely.
Scaling These Tools the Right Way
Capturing the full value of these tools requires more than deploying technology. Organizations need to get the basics right first, since digital tools amplify existing practices rather than fix broken ones. They also need to shape decision culture, so leaders actually trust and act on data-driven insights even when those insights challenge experience-based judgment.
And they need to pilot first before thinking enterprise-wide — proving impact through a single event, then embedding what works into the broader operating model through a deliberate road map to scale.
Maintenance turnarounds will remain one of the most consequential, and most challenging, performance drivers in capital-intensive operations. The organizations already treating them as a strategic capability are capturing disproportionate value — and the ones now adding digital and AI tools on top of that discipline are pulling further ahead.