Equipment Readiness and Availability

Managing equipment readiness across multiple distributed operating bases presents a persistent challenge in asset-intensive industries: without accurate modeling, organizations either over-provision assets — incurring unnecessary cost — or under-provision and fail operational availability targets. In this case, the organization needed to determine the exact number of aircraft available at any given time across several Main Operating Bases (MOBs), accounting for variability in maintenance durations, inspection frequencies, and the number of assets entering deep-level inspection and repair (DLIR) simultaneously. The absence of a reliable simulation model left planners unable to test the impact of operational changes before committing resources.

To resolve the fleet readiness uncertainty, all maintenance activities performed on the aircraft were catalogued along with their durations and frequencies. A discrete-event simulation model was then constructed using Arena — Rockwell Automation's simulation platform — to create a functional digital twin of fleet operations under ideal sparing conditions. The model incorporated key input variables including the number of MOBs in operation, aircraft distributions and quantities, reductions in activity durations and frequencies, and the number of assets placed in DLIR at a time. By running the simulation against these parameters, planners could evaluate SAR standby aircraft availability against desired performance thresholds before making any physical resource commitments. This approach allowed iterative scenario testing without disrupting live operations.

The simulation produced clear, base-specific staffing requirements and identified meaningful efficiency gains:

• 7 aircraft required at MOB Comox to meet availability targets
• 6 aircraft required at all other MOBs
• 25% reduction in inspection requirements achieved across three or four bases
• SAR standby availability benchmarked against desired levels, confirming the model's validity

Beyond the numbers, the digital twin gave planners a repeatable tool to test future operational changes — including fluctuations in aircraft quantities or maintenance tempo — without requiring physical trials.

• Discrete-event simulation (digital twin) can resolve equipment readiness questions before physical resources are committed, reducing both over- and under-provisioning risk.
• Cataloguing all maintenance activities with accurate durations and frequencies is a prerequisite — garbage-in assumptions will invalidate simulation outputs.
• Testing variability parameters (fleet size, inspection frequency, DLIR batch size) within the model produces differentiated, site-specific requirements rather than one-size-fits-all targets.
• A 25% reduction in inspection requirements demonstrates that simulation-driven analysis often uncovers optimization headroom that intuition-based planning misses.
• Platform-supported simulation tools like Rockwell Automation's Arena lower the barrier for SMEs to implement credible digital twins without building bespoke modeling infrastructure.