Simulation for Aerospace & Defense
Low Volume, Long Horizon, Constrained by Resources
Aerospace and defense production is the opposite of a bottling line. Units are few, cycles are long, and what runs short is rarely machine capacity. It's fixtures, bays, cranes, transport and qualified people. Throughput depends on how those resources are shared over months and years, so it's a scheduling and contention problem rather than a rate problem.
Who has used it
Boeing · Lockheed Martin · Raytheon · NASA · Electric Boat · SAIC · Los Alamos National Laboratory · Sandia National Laboratories · Westinghouse Savannah River · United States Marine Corps
How many fixtures buy how many rockets?
Delta IV launch vehicles are shipped to Cape Canaveral in parts and assembled near the launch site. Assembly needs specialized fixtures, each one expensive and each one occupied for a long time by a single vehicle. Buy too few and vehicles queue for a fixture; buy too many and capital sits idle between launches.
Boeing used a model to weigh that tradeoff, the number of specialized assembly fixtures against the throughput of assembled rockets.
This is the buffer question again. A fixture is a resource that decouples one stage from the next. Its cost is capital, and the right number depends on variability rather than on averages. The arithmetic that sizes a surge tank in a chemical plant also sizes an assembly fixture at a launch site.
The method is cited in the field
In 2010, Steven E. Saylor and James K. Dailey of Boeing Research & Technology presented BALANCE (the Advanced Logistics Analysis Capabilities Environment) at the Winter Simulation Conference. It builds on the non-item-based approach, citing Phelps, Parsons and Siprelle (2002).
A major aerospace research organization built its own logistics analysis capability on this modeling approach and said so at the field’s flagship conference.
Long horizons and shared resources
Programs measured in decades
Transport and storage resource planning for Department of Energy transuranic waste was assessed over a 35-year horizon. At that timescale the question stops being "can we do it" and becomes "what do we need, when, and what happens if it arrives late." Answering that across scenarios takes a model that can run a whole program quickly.
Facility maintenance as a bid input
Resource requirements for long-term facility maintenance were modeled to support contract proposals. The model showed how much crew, plant and float a multi-year service commitment needs, given failures that arrive unpredictably.
Contention between programs
Bays, cranes, clean rooms and qualified technicians are shared across programs that are planned independently. A schedule that works for each program alone can be infeasible for all of them together, and that only shows up when they're simulated on the same resources.
Deployment
Everything runs on the desktop, with no outbound data flow and no cloud dependency, and it works air-gapped, so your process data never leaves your network. In this sector that's usually the first question asked.
The same mechanisms, documented on other lines
How big should a buffer be?
How much decoupling capacity is worth buying, and why the answer depends on variability rather than on average rates. The fixture-count question in its general form.
Read the guide → Capital vs UtilizationThe bagging lines are tied to bulk production
How many lines to buy, traded against running the existing ones harder. The answer comes from the time split rather than nameplate capacity.
Read the case study →Related: Chemicals, oil & gas · Pharma & life sciences · All case studies
A free line assessment, this fall
Bring one question about your line. We model it on your own data and send back a two-page readout in about five business days.
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