LLM use case: better understand customer expectations for a player in the travel sector
Eleven helped a world leader in travel booking to better understand customer expectations and pain points.
Reduction of raw material requirements
Streamlining of sprinkler network production processes to reduce steel requirements and reduce unusable scrap while optimizing team time spent on network generation
Thanks to their knowledge of reinforcement learning and generative design methods, Eleven’s consultants have helped a major player in sprinkler installations (fixed sprinkler installations) to considerably reduce the steel requirements for the production of networks at the client’s premises, in particular by limiting the amount of unusable raw material scrap and therefore overproduction at the suppliers’.
In addition to resource savings, this new “data augmented” production model has saved over 17,000 hours of work time for the engineers thanks to improved operational efficiency, and a 20% increase in projects won thanks to a faster, more accurate and more appropriate response to calls for tender.
For more information: contact Stéphane Maviel or read this article
Reduction in the amount of steel needed to produce sprinkler systems
3,5ml
saved per year in engineering time
20%
more projects per year for the customer
Reduced unusable scrap and overproduction at suppliers
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