Here's something worth being honest about when it comes to benchmarks.
Most of the numbers you'll find in analyst reports are modeled—derived from surveys, estimated from self-reported data, smoothed into nice round percentages. They're not wrong, necessarily. But they're not operational. They don't come from actually running the shop.
What you're about to see here is different.
These benchmarks are drawn from 62 Fortune 500 engagements across 42 enterprise environments that Allari has managed over more than two decades.
This is real data from real IT shops—manufacturing floors, healthcare systems, agricultural supply chains, financial services firms. Not estimates. Not projections. Operational truth.
And the gaps between what most organizations assume and what the data actually shows will probably surprise you.
How Capacity Gets Measured
Before getting into the industry breakdowns, here's what's actually being measured.
When Allari talks about IT capacity, six dimensions are in play: service interaction volume, average response and resolution time, ticket aging, unplanned work ratio, context-switching overhead, and first-contact resolution rate.
All of this comes directly from open-book engagement records—the same numbers clients see every month through the OpenBook™ transparency model.
Here's why that matters. Most internal IT shops don't track these numbers with any rigor. They know their ticket counts. Maybe they know their SLA compliance rate.
But ask them what percentage of their team's time goes to unplanned work versus planned initiatives, and you'll get a shrug. That blind spot is exactly where capacity dies.
Manufacturing — Where ERP Complexity Meets Shift Coverage
Manufacturing is Allari's deepest bench. The firm has supported global manufacturers like HellermannTyton, W.L. Gore, Channellock, and others across JDE and SAP environments. Here's what the data shows.
Typical unplanned work ratio in a mid-to-large manufacturing IT shop? 35–45%.
That lines up with the IT Process Institute's broader finding that 81–87% of IT organizations fall in that range.
But here's the kicker—manufacturing environments tend to cluster at the high end of that range because of ERP-specific overhead.
You've got shift-coverage complexity, multi-plant configurations, shop floor integrations, and EDI transactions that break at 2 AM when nobody's watching.
And when things break on the shop floor, nobody cares that the ticket is "in queue."
Production stops. Revenue stops.
Here's a real example.
When Allari engaged with HellermannTyton—a $750M global manufacturer running JDE—their ticket aging averaged 16.4 days. Think about that.
An issue comes in, and on average it sits for over two weeks before it's resolved. That number came down to 1.77 days—an 89% reduction. First-year cost compression hit 19%, and 40% of their capacity was recovered. Zero re-opened items during the stabilization phase. That's across 8,759 service interactions.
Or take W.L. Gore—45 countries, 3,500+ users, massive JDE footprint. Allari has managed 26,518 service interactions there. Twenty-five FTEs absorbed by the shared services model. Zero degradation, 100% global uptime.
The point isn't to brag—it's to show what's achievable when the operational fundamentals are in place. Those numbers didn't come from heroic effort.
They came from disciplined process, documented runbooks, and relentless attention to capacity management.
The benchmark for a well-run manufacturing IT operation? Ticket aging under 3 days. Unplanned work below 20%. Change success rate above 90%.
If you're not hitting those numbers, you've got execution drag eating your capacity.
Healthcare — Compliance Isn't Just a Checkbox
Healthcare IT is a different animal.
You're not just managing an ERP—you're managing an EHR, an identity system, a billing platform, and a compliance framework that can trigger a federal audit if something goes sideways. The complexity isn't just technical. It's regulatory.
This shows up clearly in Allari's work with organizations like Allegiant Health, where 5,399 service interactions were managed across a multi-platform environment.
The pattern is consistent: healthcare IT teams spend a disproportionate amount of their capacity on compliance-adjacent work—access reviews, audit trail maintenance, security patching—that crowds out everything else. After-hours coverage requirements add another layer.
You can't just have on-call rotation; you need people who understand the clinical workflow and the regulatory implications of downtime.
The benchmark?
Healthcare IT shops should target an unplanned work ratio below 25%, but most are running at 40% or higher because compliance overhead isn't treated as planned work—it's treated as interruptions. Fix the classification, and you fix the capacity model.
Agriculture and Distribution — Seasonality Will Break You
If you've never supported an agriculture or distribution company through peak season, you might not appreciate how violent the capacity swings can be.
Across engagements like Wilbur-Ellis, 8,166 service interactions revealed a pattern: ticket volume can spike 60–80% during harvest and planting seasons, and if you haven't pre-positioned capacity, your team drowns.
The geographic distribution challenge compounds this.
You've got field offices, rural connectivity issues, mobile users on tablets in grain elevators, and supply chain integrations that have to work in real time or product literally rots.
The benchmark for agriculture and distribution IT isn't just about average performance—it's about peak performance.
Can your team absorb a 70% volume spike without degrading response times?
If not, you're going to lose operational trust with the business during the exact moments that matter most.
Financial Services and Construction — Specialized Burdens
Financial services IT carries a regulatory compliance burden that's second only to healthcare.
Audit trail requirements mean every change, every access modification, every system interaction has to be documented and defensible.
The capacity tax is real—typically 20–30% of IT team time gets consumed by compliance and audit preparation alone.
And here's the thing about compliance overhead—it doesn't scale down when your team is busy with other work. It just piles on top.
So while a manufacturing IT shop might lose 40% of capacity to unplanned work, a financial services IT shop can lose 40% to unplanned work plus another 25% to compliance—and suddenly you're wondering how anything gets done.
The benchmark here is about efficiency within that constraint: can you automate enough of the compliance workflow to keep unplanned work below 30%?
Construction is project-based, which creates a fundamentally different ERP usage pattern.
Job cost reporting, field connectivity, and project-specific configurations mean that IT isn't supporting a steady-state operation—it's supporting a portfolio of moving targets.
Every new project means new cost codes, new reporting structures, sometimes new integrations with subcontractor systems.
The benchmark that matters most? Time to provision and deprovision project-specific environments.
If it takes your team two weeks to set up a new project in the system, you're creating a bottleneck that the business feels on every bid.
And in construction, the margin between winning and losing a project is often measured in days, not weeks.
How to Use These Benchmarks
Here's what to do.
Take the three metrics you can most easily pull from your current systems—ticket aging, unplanned work ratio, and change success rate—and compare them to the industry benchmarks above. If you're within range, good. You've got a foundation.
If you're significantly off—and most organizations are—then you've identified where your execution drag lives.
Fair enough, you might say. But what do you do with that information?
Start by quantifying it.
If your unplanned work ratio is 40% and the benchmark is 20%, that's 20 percentage points of your team's capacity being consumed by work that shouldn't exist. Multiply that by your fully loaded team cost. That number—that's what execution drag is costing you every year. And it's probably bigger than you think.
The benchmarks aren't a report card. They're a diagnostic tool. Use them to find the gaps, then work backward to the root causes. That's where the real capacity recovery begins.
