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CONTACTER LANNER
Complete the form below for a quick response from a Lanner representative or call us on +44 (0)121 709 6520 or +1 713 532 8008. If you require support, please visit my.lanner.com
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Safran Aircraft Engines is a world-class designer, developer, producer and marketer of aircraft engines. With a legacy stretching back over 100 years, it’s the world’s number one producer of mainline commercial jet engines and Europe’s leading producer of military engines.
Lanner has a long-standing relationship helping Safran leverage predictive digital twins. A recent project involved developing a flexible model to help improve operational performance at their primary facility south of Paris.
Safran had launched a major initiative to improve operational performance at the facility. Designed to help Safran reach its ambitions, the project focused on five key areas:
“Based on our past experiences of predictive simulation, we knew Lanner could help us drive three areas we were prioritising: industrial and operational organisation, machining processes and shop floor digitalisation,” explained Safran’s Cyril Brun.
“We wanted to use a predictive digital twin to help us balance lead times and quality,” added Safran’s Pascal Bonpapa. “There are complex equipment configurations and machining processes. The margin for deviation is minuscule when you’re manufacturing mission-critical aircraft parts. The overarching question was: how can we organise and optimise production processes to drive improvements in First Time Yield (parts that never have a quality problem) with lead times that deliver on our commercial targets?”
To help answer this, we developed a model for five production lines that play a critical role in engine production processes.
From the outset, Safran wanted a single model for all five lines. This would give them a future-proof solution that teams could use for a wide range of current and future scenarios.
“Although the five lines produce different part families, they have similar processes,” explained Eric Gaury, Lanner’s account lead. “A single model would allow Safran to simulate these complex, non-linear production processes in the most flexible and user-friendly way.”
Although we developed a single model, it needed the specificity to address two key elements: quality and lead time.
We followed our proven implementation methodology to develop the model. This started with a detailed scoping exercise involving stakeholders from all five teams. We got under the skin of the process nuances for each line, as well as the specific and varied KPIs they wanted to optimise.
“We really appreciated the time Lanner took to learn our processes and our unique language – they truly understood our operations and objectives,” said Pascal. “Importantly, they aren’t just digital twin technology experts; they have strong production backgrounds, too, which meant they truly helped us understand the art of the possible with predictive simulation.”
We developed the model using our WITNESS Horizon predictive simulation software – with an Excel interface so it would be familiar and easy for Safran users. Once the model was ready, we conducted a training session where we took Safran users through modelling exercises and documentation, so they could hit the ground running with their analysis.
Safran started by using the model to analyse two re-engineering opportunities:
For each of these, Safran ran multiple scenarios to test ideas from across teams. The simulation results have provided an evidence base for millions of euros of investment.
“The model is an effective tool for facilitating teamwork and validating ideas. We know there’s no single ‘right’ answer to these complex questions. The model gives us an objective way to analyse all the possible scenarios, understand how complex processes affect each other and determine how we can best speed up lead times without compromising quality,” explained Pascal.
Safran continues to use the model to inform ongoing industrial and operational organisation, machining processes and shop floor digitalisation.
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