reduction demonstrated by simulations
on a new decision method based on simulation data
Airbus is one of the world’s leading aircraft and space vehicle manufacturers with facilities in more than two-dozen countries and final assembly operations in 7 countries and is a leader in designing, manufacturing, and delivering aerospace products, services, and solutions to customers on a worldwide scale.
The riveting machines in Airbus’ final major plants are strategic assets worth several million dollars each and the failure of any individual machine can jeopardize the production planning of the final aircraft. Airbus seeks to maximize the useful lifetime of 6 riveting machines in one plant while reducing their total expenditure on the assets.
Forecasting the future by mirroring the past is not enough. Industrial systems rarely display the same behavior that they have in the past. Cosmo Tech’s digital twin simulation provides visibility on how your system could behave in the future. You can play with different scenarios, see the impact of your decisions and get clear alignment between stakeholders on the best way forward.
Airbus leveraged a Cosmo Tech Prescriptive Simulation Twin that allowed them to identify the optimal replacement strategy for the assembly machines.
From the twin, Airbus teams could define and calibrate the aging model for the machines, configure the different operational, capital, and maintenance downtime costs. For various age replacement of the machines, Airbus could simulate the behavior of their 6 machines and see the financial impact on the production lines in terms of repair costs and production losses related to more significant breakdowns.
They could perform sensitivity analysis to determine the most critical elements in their strategy and share with key stakeholders how it created opportunities for the future.
Airbus identified an optimal asset replacement strategy for its riveting machines that would reduce their total expenditure by 15% over the lifetime of the machine. The different teams engaged in asset management at Airbus were able to align around a data-informed and simulation-driven action plan. Stakeholders could understand how and why the replacement strategy was derived.
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