Problem
Ramping up the production of a new aircraft is a complex process, involving continuous evaluation and revision of both the product and production processes. As a result, aircraft lead time can increase dramatically.
Additionally, aircraft are low volume products, with each unit often highly customized. These factors, combined with decreasing product lifecycles, pose a major challenge for aircraft production engineers.
To meet the challenges of increasingly frequent ramp-ups, the Airbus Group joined the European Union ARUM (Adaptive Production Management) project.
The project, aimed mainly at new product ramp-ups in the aircraft and shipbuilding industries, focused on developing tech solutions for risk reduction, decision-making, and planning.
Simulation was chosen as a part of the ARUM solution. It allowed the participants to reproduce a real production environment (based on an Airbus Group case study) and provide a benchmark for testing ARUM solutions.
AnyLogic simulation software was selected because it can combine agent-based and discrete-event modeling methods.
Solution
The simulation model included a part of the Hamburg Airbus A350 assembly line where two different pieces of the fuselage are completed.
This part of the assembly line consists of six assembly stations with 30-35 people working at each, and approximately 300 work orders per station.
The challenge was to simulate the ramp-up process with general productivity increasing over time, and the whole ramp-up period lasting up to two years.
ARUM solution structure.
The agent-based and discrete-event model consisted of three types of elements:
- The flow line, including work stations, each one with its own material and labor resources. The stations were modeled as agents.
- The products (fuselage sections) going through the assembly line. Each section required 200-600 work orders at a work station. Work orders formed tasks that required specific materials and resources. When a section entered a station, it started going through work processes (modeled using the Process Modeling Library), then moved to the next station, and finally, to the assembly line in a different city, which was not modeled.
- The control model included plans that were sometimes affected by disturbances. The controller agent modeled the complex behavior of human managers reacting to disturbance events with control strategies.
The control strategies included open work policy alternatives. This meant that if some of the work could not be done, it could be postponed and completed further along the assembly line, perhaps in another city. In this case, workers from Hamburg would travel to complete the work (the “traveling work” strategy). Alternatively, work on the section could be suspended until the problem was resolved (the “stop and fix” strategy).
The disturbances that occurred during the ramp-up included:
- Unbalanced workload and resource allocation due to the worker learning curve and the fact that the same line produced several different products.
- Design non-conformities and changes, as production often began with a partially prepared product.
- Missing material or material incompatibilities due to late design changes.
Included in the model statistics were: aircraft lead time, the amount of traveling work, and resource utilization rates (labor, materials, and stations).
The project resulted in a model that was easy to understand and reuse. Ultimately, it was integrated into the ARUM solution architecture, including the assembly line visualization component.
Simulation model structure.
Outcome
The model was run to simulate the effects of the disturbance mitigation strategies currently being applied at the Airbus facility, including the “stop-and-fix” and “traveling work” strategies.
Multiple ramp-up scenarios with different sets of production plans were tested and made use of historical assembly disturbance data, including extreme scenarios.
Overall, the model will be used to compare plans suggested by the ARUM suite with current management practices. This will allow the development of optimal disturbance mitigation strategies for both aerospace and shipbuilding manufacturing ramp-ups. A powerful tool for accelerated product lifecycles.