Daub hired IBM to design a computer system that would pore over massive amounts of information — the formulas of existing fragrances, consumer data, regulatory information, on and on — and then suggest new formulations for particular markets.
The system is called Philyra, after the Greek goddess of fragrance.
Evocative name aside, it can’t smell a thing, so it can’t replace human perfumers.
But it gives them a head start on creating something novel.
Daub is pleased with progress so far.
Two fragrances aimed at young customers in Brazil are due to go on sale there in June.
Only a few of the company’s 70 fragrance designers have been using the system, but Daub expects to eventually roll it out to all of them.
However, he’s careful to point out that getting this far took nearly two years — and it required investments that still will take a while to recoup.
Philyra’s initial suggestions were horrible: it kept suggesting shampoo recipes.
After all, it looked at sales data, and shampoo far outsells perfume and cologne.
Getting it on track took a lot of training by Symrise’s perfumers.
Plus, the company is still wrestling with costly IT upgrades that have been necessary to pump data into Philyra from disparate record-keeping systems while keeping some of the information confidential from the perfumers themselves.
“It’s kind of a steep learning curve,” Daub says.
“We are nowhere near having A.
firmly and completely established in our enterprise system.
” The perfume business is hardly the only one to adopt machine learning without seeing rapid change.
Despite what you might hear about A.
sweeping the world, people in a wide range of industries say the technology is tricky to deploy.
It can be costly.
And the initial payoff is often modest.
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