When a fleet, a factory, or a force falls short of its readiness target, the instinct is to blame the people doing the work. The instinct is usually wrong. Naval serviceability sits 15.4 points below its 75% target; aerospace sits 36.1 points below its 85% target. In both cases, the biggest contributors to the gap were not physical — they were data and process: maintenance demand that was not visible early enough, material that was not planned against actual usage, obsolescence that was not flagged until it became a failure. A fleet that cannot see its own downtime cannot fix it. The first move is not to hire — it is to decompose the gap into its drivers.
The finding: the gap is consistent, and the diagnosis is consistent
Across fleets, the readiness gap is large, measurable, and — critically — misdiagnosed the same way every time:
| Fleet | Actual | Target | Gap |
|---|---|---|---|
| Naval | 59.6% | 75% | 15.4 points |
| Aerospace | 48.9% | 85% | 36.1 points |
The gap is not abstract. Under a performance-based contract, serviceability is the thing the contractor gets paid for. The naval gap alone is worth C$104–260 million in at-risk sustainment fees over the life of a CAD 5.2 billion contract.
And the pressure is rising: a 40–50% surge in parts and maintenance volume is forecast at the next asset induction, landing on material-planning processes already rated 3 out of 5.
The framework: the readiness decomposition
The readiness decomposition separates the data gap from the physical gap. It is the difference between “we don’t have enough people” and “we don’t know where the downtime is coming from.”
Three drivers dominate, and none of them is a technician shortage:
1. Demand visibility. Maintenance demand that is not visible early enough becomes a surprise, not a plan.
2. Material planning. Requirements planned against history rather than actual usage mean parts arrive after the failure, not before it.
3. Obsolescence. Components that are not flagged until they fail turn a scheduled replacement into an emergency.
The uncomfortable truth: you can hire a thousand more mechanics, and if they are all waiting for parts the system did not know to order, the ships still do not sail. The labour crunch is real — only 1 in 13 armed-forces applicants was recruited between 2022 and 2025 — but it is not the binding constraint.
The trap: paying for activity instead of outcome
The deeper problem is the incentive. When a contractor is paid for maintenance activity, it delivers a lot of activity and not much readiness. When it is paid for fleet availability, the system reorganizes itself around the thing that actually matters.
The shift is performance-based logistics: align the incentive with the outcome — a ship that is ready — rather than the activity — a task completed.
The decision agenda: what to do differently
- Decompose before you hire. Separate the data gap from the physical gap. If you cannot decompose the gap into its drivers, you are not ready to close it.
- Fix the information flow first. Demand forecast from usage data, material planned against actual consumption, obsolescence flagged before failure.
- Pay for the outcome, not the activity. The incentive structure, not the headcount, is the lever.
What could break this read
- The gap is partly physical. Aging hulls and genuine parts scarcity are real. The data-and-process decomposition explains most of the gap, but not all of it — and data fixes have a ceiling.
- The target keeps moving. If the readiness bar keeps rising faster than the fleet can recover, no amount of data hygiene closes the gap. At some point the constraint is the target, not the system.
The checklist
- Do I know where the downtime is actually coming from — or just the total?
- Is maintenance demand forecast from usage, or discovered at failure?
- Is material planned against actual consumption, or against history?
- Am I paying for activity, or for availability?
The rule: decompose the gap before you hire. The constraint is rarely the people — it is the system that tells them what to work on.
Sources
- 15Rock naval in-service support assessment (2026) — serviceability 45.73% (FY2023-24) to 59.6% (FY2024-25) against a 75% target; 15.4-point gap; C$104–260M at-risk fees on a CAD 5.2B contract.
- Public defence-policy analysis (2026) — aerospace serviceability 48.9% vs 85% target (36.1-point gap).
- Same assessments — 40–50% parts and maintenance volume surge at 2026 induction; 1 in 13 armed-forces applicants recruited 2022-25.
- Canada’s Defence Industrial Strategy (Feb 2026) — maritime serviceability target 75%, land 80%, aerospace 85%.
Figures are externally reported or calculated estimates. Client and program names are withheld.
Frequently Asked Questions
What causes fleet readiness gaps in aerospace and defence sustainment?
Across multi-platform aerospace and defence diagnostics, fleet readiness deficits are rarely driven by a single shortage of technicians; instead, they decompose into cascading planning and data friction: parts-availability delays at depot intake, unplanned cannibalization across tail or hull numbers, outdated technical data packages, and sequential maintenance scheduling.
How do you run an aerospace and defence readiness diagnostic?
A rigorous readiness diagnostic decomposes mission-capable (MC) and non-mission-capable (NMC) rates into three mutually exclusive buckets: supply-driven downtime (NMCS), maintenance-capacity downtime (NMCM), and administrative/engineering hold time, then traces the top 20% of LRU or sub-assembly drivers back to vendor lead times and depot work-scope creep.
Why do digital twin and predictive maintenance rollouts stall in defence sustainment?
Predictive algorithms fail when underlying maintenance logs, serial-number configuration trees, and work-order closeout codes are inconsistent across legacy ERP and depot systems. Cleaning and standardizing the reliability data taxonomy across platforms must precede predictive modelling.