Bottom-up emulations of real sustainment systems that explicitly model spares, personnel, operations, and maintenance are apowerful way to tie funding decisions to their impact on readiness, but they are not widely used. The simulations require extensivedata to properly model the complex and variable processes involved in a sustainment system, and the raw data used to populate thesimulation are often scattered across multiple organizations. The Navy has encountered challenges in keeping sustaining the desirednumber of F/A-18 Super Hornets in mission capable states. IDA was asked to build an end-to-end model of the Super Hornetsustainment system using the OPUS/SIMLOX suite of tools to investigate the strategic levers that drive readiness. IDA built an Rpackage (honeybee) that aggregates and interprets Navy sustainment data using statistical techniques to create component-levelmetrics. IDA built a second R package (stinger) that uses these metrics to automatically generate the input tables necessary torun OPUS/SIMLOX; the effect of both of these packages is that IDA has lowered the barrier for entry for building these large end-to-end sustainment models. We present a summary of these tools and techniques to the OPUS User community in this briefing.