December 15, 2023, ©. Leeham News: We are discussing the different phases of a new airliner program. After covering Conceptual, Preliminary, and Detailed design, the manufacturing of prototypes, and their roles in flight tests, we now look at the production phase.
Last week, we discussed why production costs vary over time and why they can be up to 500% higher for the first units than for units past 400 to 500 aircraft produced. Now we go deeper into the reasons behind this and what can be done to improve the situation.
We explained last week that the initial fitting together of manufactured parts is one root cause of why early production has a higher cost than when such problems have been corrected, and the assembly runs smoother. This is part of the easier problem areas to address.
Modern 3D design and simulation tools allow designers, production engineers, and shop floor mechanics to visualize the parts and their assembly on large screens or through VR 3D headsets. Cross-functional teaming in the design reviews can catch problems before they appear on the shop floor.
By keeping all models and data (BOMs, instructions, 3D drawings, etc.) digital and using interactive screens in production where mechanics find all the needed information on how to assemble items (Figures 2 and 3), the learning process goes faster.
In Figure 2, two mechanics on the SAAB Final Assembly Line (FAL) for the Gripen E fighter learn how to do the next work steps, reading the information directly from the 3D design database system and not from paper information.
It creates a tighter feedback loop to design/production engineering as problems can be fed back immediately as the assembly is done, Figure 3.
I’ve deliberately used examples from the SAAB Gripen E production, as it’s one of the first instances (if not the first) in aeronautical production where an end-to-end digital chain was introduced all the way to the assembly mechanic, from design to shop floor.
This know-how was the reason Boeing teamed with SAAB for the USAF training project T-X/T-7A Red Hawk and learnings from this project will now be used in other parts of Boeing.
I took the SAAB example for more reasons than the Digital Chain/Digital Twin. The example also addresses the more difficult part of improving the effectiveness of initial production, the mental part.
I have worked in Swedish, US, German, and French work cultures, and they are all different. The largest difference is between the Swedish soft, cooperative culture, where workforce and management cooperate to achieve results and the top-down culture of US companies.
Swedes learn to work and function in groups from Kindergarten, and how to tone down their personality in favor of group function. In the US, on the other hand, you learn how to articulate and portray yourself so you stand out and get recognized. The focus is on the individual.
It’s no accident that so many useful production improvement methods come from Japan, where the person and work culture has similarities with Sweden, and Swedish SAAB is picked up by Boeing as the catalyst for its design-to-production change program.
Production of a product of the complexity of an airliner is INCREDIBLY complex. It’s why top-down initiatives such as aggressive targets and the reliance on KPIs (simple metrics that define success or not) as part of hot-shot manager’s promises: “I will fix the production issues within XX months by applying my XYZ method I’ve used in the past” leaves a management team that cash in their bonuses and move on and a shop floor that has not really changed although the KPIs says it has.
The reality is that only a change process that combines modern technology that can deliver instant, easy-to-understand information on what’s the state of things to learn-ready cross-functional teams can deliver real and lasting results.
It also means that the change process is a marathon that progresses in small but ever-present steps in the minds of real, hands-on individuals, not on the KPI screens of managers.
The improvement of the learning curve requires long-time commitments from the teams and their management. There are no shortcuts and no “quick fixes.”
Part of productivity gains in production comes from the automation of work-hour-intensive processes. The building of an airliner is work-hour intensive, especially the assembly of structural parts. It’s known as the “drill and fill” problem.
Present aircraft materials cannot be welded together (thermoplastic composites are introduced to change this); it’s why the parts are either riveted or bolted (often called fastened) together. The drilling, separation of parts for deburring, assembly, and inserting and tightening of the bolt+nut/rivet require several labor steps.
As we have 60,000 rivets in a Boeing 777, the manual drilling and filling of these 60,000 holes is a target for automation. But aeronautical automation is hard.
I have visited more than 20 aeronautical production lines and seen few semiautomated assembly stations and no fully automated ones, including the brand new Airbus single-aisle Final Assembly Line (FAL) in Toulouse.
The complexity of achieving improvement and failure to do so can be exemplified by the FAUB (Fuselage Automated Upright Build) project to “drill and fill” Boeing 777 sections with Robots at Boeing’s Everett site, Figure 4.
The FAUB project was eventually abandoned as a team of mechanics with hand tools could do a better job, where they easily adapted to any fit or other issues.
But I could equally well have put the picture of Airbus’ automation of the A320/321 FAL number four in Hamburg, which also failed to deliver what was expected. It’s probably why the newer A320/321 FAL in Toulouse only had limited automation (fuselage crawling drillers but nothing more exciting).
What has been learned in all these projects is the incredible flexibility and efficiency of a trained and experienced mechanic. If he/she is, in addition, motivated and well supported by real-time information systems like in Figures 2 and 3, it’s a proven way to increase efficiency and gain an improved learning curve.
Sensible step-wise automation adds to these improvements but requires a well-functioning team around it so as not to spoil the initial learning curve when the complicated automation shall be trimmed in.