{"id":17510,"date":"2020-03-08T13:52:59","date_gmt":"2020-03-08T13:52:59","guid":{"rendered":"https:\/\/rbsitsoftwaresolution.com\/blog\/yc-backed-turing-uses-ai-to-help-speed-up-the-formulation-of-new-consumer-packaged-goods.html"},"modified":"2020-03-08T13:52:59","modified_gmt":"2020-03-08T13:52:59","slug":"yc-backed-turing-uses-ai-to-help-speed-up-the-formulation-of-new-consumer-packaged-goods","status":"publish","type":"post","link":"https:\/\/www.rbsitsoftwaresolution.in\/blog\/yc-backed-turing-uses-ai-to-help-speed-up-the-formulation-of-new-consumer-packaged-goods.html","title":{"rendered":"YC-backed Turing uses AI to help speed up the formulation of new consumer packaged goods"},"content":{"rendered":"

One of the more interesting and useful applications of artificial intelligence technology has been in the world of biotechnology and medicine, where now more than 220 startups<\/a> (not to mention universities and bigger pharma companies) are using AI to accelerate drug discovery by using it to play out the many permutations resulting from drug and chemical combinations, DNA and other factors.<\/p>\n

Now, a startup called Turing<\/a> \u2014 which is part of the current cohort at Y Combinator due to present in the next Demo Day on March 22 \u2014 is taking a similar principle but applying it to the world of building (and \u201cdiscovering\u201d) new consumer packaged goods products.<\/p>\n

Using machine learning to simulate different combinations of ingredients plus desired outcomes to figure out optimal formulations for different goods (hence the \u201cTuring\u201d name, a reference to Alan Turing\u2019s mathematical model, referred to as the Turing machine<\/a>), Turing is initially addressing the creation of products in home care (e.g. detergents), beauty and food and beverage.<\/p>\n

Turing\u2019s founders claim that it is able to save companies millions of dollars by reducing\u00a0the average time it takes to formulate and test new products, from an average of 12 to 24 months down to a matter of weeks.<\/p>\n

Specifically, the aim is to reduce all the time it takes to test combinations, giving RD teams more time to be creative.<\/p>\n

\u201cRight now, they are spending more time managing experiments than they are innovating,\u201d Manmit Shrimali, Turing\u2019s co-founder and CEO, said.<\/p>\n

Turing is in theory coming out of stealth today, but in fact it has already amassed an impressive customer list. It is already generating revenues by working with eight brands owned by one of the world\u2019s biggest CPG companies, and it is also being trialed by another major CPG behemoth (Turing is not disclosing their names publicly, but suffice it to say, they and their brands are household names).<\/p>\n

Turing is co-founded by Shrimali and Ajith Govind, two specialists in data science that worked together on a previous startup called Dextro Analytics. Dextro had set out to help businesses use AI and other kinds of business analytics to help with identifying trends and decision making around marketing, business strategy and other operational areas.<\/p>\n

While there, they identified a very specific use case for the same principles that was perhaps even more acute: the research and development divisions of CPG companies, which have (ironically, given their focus on the future) often been behind the curve when it comes to the \u201cdigital transformation\u201d that has swept up a lot of other corporate departments.<\/p>\n

\u201cWe were consulting for product companies and realised that they were struggling,\u201d Shrimali said. Add to that the fact that CPG is precisely the kind of legacy industry that is not natively a tech company but can most definitely benefit from implementing better technology, and that spells out an interesting opportunity for how (and where) to introduce artificial intelligence into the mix.<\/p>\n

RD labs play a specific and critical role in the world of CPG.<\/p>\n

Before eventually being shipped into production, this\u00a0is where products are discovered; tested; tweaked in response to input from customers, marketing, budgetary and manufacturing departments and others; then tested again; then tweaked again; and so on. One of the big clients that Turing works with spends close to $400 million in testing alone.<\/p>\n

But RD is under a lot of pressure these days. While these departments are seeing their budgets getting cut, they continue to have a lot of demands. They are still expected to meet timelines in producing new products (or often more likely, extensions of products) to keep consumers interested. There are a new host of environmental and health concerns around goods with huge lists of unintelligible ingredients, meaning they have to figure out how to simplify and improve the composition of mass-market products. And smaller direct-to-consumer brands are undercutting their larger competitors by getting to market faster with competitive offerings that have met new consumer tastes and preferences.<\/p>\n

\u201cIn the CPG world, everyone was focused on marketing, and RD was a blind spot,\u201d Shrimali said, referring to the extensive investments that CPG companies have made into figuring out how to use digital to track and connect with users, and also how better to distribute their products. \u201cTo address how to use technology better in RD, people need strong domain knowledge, and we are the first in the market to do that.\u201d<\/p>\n

Turing\u2019s focus is to speed up the formulation and testing aspects that go into product creation to cut down on some of the extensive overhead that goes into putting new products into the market.<\/p>\n

Part of the reason why it can take upwards of years to create a new product is because of all the permutations that go into building something and making sure it works as consistently as a consumer would expect it to (which still being consistent in production and coming in within budget).<\/p>\n

\u201cIf just one ingredient is changed in a formulation, it can change everything,\u201d Shrimali noted. And so in the case of something like a laundry detergent, this means running hundreds of tests on hundreds of loads of laundry to make sure that it works as it should.<\/p>\n

The Turing platform brings in historical data from across a number of past permutations and tests to essentially virtualise all of this: It suggests optimal mixes and outcomes from them without the need to run the costly physical tests, and in turn this teaches the Turing platform to address future tests and formulations. Shrimali said that the Turing platform has already saved one of the brands some $7 million in testing costs.<\/p>\n

Turing\u2019s place in working with\u00a0RD gives the company some interesting insights into some of the shifts that the wider industry is undergoing. Currently, Shrimali said one of the biggest priorities for CPG giants include addressing the demand for more traceable, natural and organic formulations.<\/span><\/p>\n

While no single DTC brand will ever fully eat into the market share of any CPG brand, collectively their presence and resonance with consumers is clearly causing a shift. Sometimes that will lead to acquisitions of the smaller brands, but more generally it reflects a change in consumer demands that the CPG companies are trying to meet.\u00a0<\/span><\/p>\n

Longer term, the plan is for Turing to apply its platform to other aspects that are touched by RD beyond the formulations of products. The thinking is that changing consumer preferences will also lead to a demand for better \u201cformulations\u201d for the wider product, including more sustainable production and packaging. And that, in turn, represents two areas into which Turing can expand, introducing potentially other kinds of AI technology (such as computer vision) into the mix to help optimise how companies build their next generation of consumer goods.<\/span><\/p>\n

Article source: http:\/\/feedproxy.google.com\/~r\/techcrunchIt\/~3\/Qzn-4IC5nts\/<\/p>","protected":false},"excerpt":{"rendered":"

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