IoT and data science will boost foodtech in the post-pandemic era

Published August 7, 2020

Sunny Dhillon
Contributor

Sunny Dhillon is an early-stage investor at Signia Ventures in San Francisco where he invests in retail tech, e-commerce infrastructure and logistics, alongside consumer and enterprise software startups.

Rely on the data

Image Credits: Pixabay/Pexels (opens in a new window)

The hallmark of shopping in a store is the consistent availability and wide selection of fresh items — often more so than online. But as the number of in-store customers continues to fluctuate, planning inventory and minimizing waste has become ever more so a challenge for grocery store managers. Grocers on average throw out more than 12% of their on-shelf produce, which eats into already razor-thin margins.

While e-grocers are automating and optimizing their fulfillment operations, brick-and-mortar grocers can automate and optimize their inventory planning mechanisms. To do this, they must leverage their existing troves of customer, business and external data to glean valuable insights for store managers.

Eden Technologies of Walmart is a pioneering example. Spun out of a company hackathon project, the internal tool has been deployed at over 43 distribution centers nationwide and promises to save Walmart over $2 billion in the coming years. For instance, if a batch of produce intended for a store hundreds of miles away is deemed soon-to-ripen, the tool can help divert it to the nearest store instead, using FDA standards and over 1 million images to drive its analysis.

From farm to phone: A paradigm shift in grocery

Similarly, ventures such as Afresh Technologies and Shelf Engine have built platforms to leverage years of historical customer and sales data, as well as seasonality and other external factors, to help store managers determine how much to order and when. The results have been nothing but positive — Shelf Engine customers have increased gross margins by over 25% and Afresh customers have reduced food waste by up to 45%.

Standard Cognition raises another $5.5M to create a cashier-less checkout experience

Published July 17, 2018

As Amazon looks to increasingly expand its cashier-less grocery stories — called Amazon Go – across different regions, there’s at least one startup hoping to end up everywhere else beyond Amazon’s empire.

Standard Cognition aims to help businesses create that kind of checkout experience based on machine vision, using image recognition to figure out that a specific person is picking up and walking out the door with a bag of Cheetos. The company said it’s raised an additional $5.5 million in a round in what the company is calling a seed round extension from CRV. The play here is, like many startups, to create something that a massive company is going after — like image recognition for cashier-less checkouts — for the long tail businesses rather than locking them into a single ecosystem.

Standard Cognition works with security cameras that have a bit more power than typical cameras to identify people that walk into a store. Those customers use an app, and the camera identifies everything they are carrying and bills them as they exit the store. The company has said it works to anonymize that data, so there isn’t any kind of product tracking that might chase you around the Internet that you might find on other platforms.

“The platform is built at this point – we are now focused on releasing the platform to each retail partner that signs on with us,” Michael Suswal, Co-founder and COO said. “Most of the surprises coming our way come from learning about how each retailer prefers to run their operations and store experiences. They are all a little different and require us to be flexible with how we deploy.”

It’s a toolkit that makes sense for both larger and smaller retailers, especially as the actual technology to install cameras or other devices that can get high-quality video or have more processing power goes down over time. Baking that into smaller retailers or mom-and-pop stores could help them get more foot traffic or make it easier to keep tabs on what kind of inventory is most popular or selling out more quickly. It offers an opportunity to have an added layer of data about how their store works, which could be increasingly important over time as something like Amazon looks to start taking over the grocery experience with stores like Amazon Go or its massive acquisition of Whole Foods.

“While we save no personal data in the cloud, and the system is built for privacy (no facial recognition among other safety features that come with being a non-cloud solution), we do use the internet for a couple of things,” Suswal said. “One of those things is to update our models and push them fleet wide. This is not a data push. It is light and allows us to make updates to models and add new features. We refer to it as the Tesla model, inspired by the way a driver can have a new feature when they wake up in the morning. We are also able to offer cross-store analytics to the retailer using the cloud, but no personal data is ever stored there.”

It’s thanks to advances in machine learning — and the frameworks and hardware that support it — that have made this kind of technology easier to build for smaller companies. Already there are other companies that look to be third-party providers for popular applications like voice recognition (think SoundHound) or machine vision (think Clarifai). All of those aim to be an option outside of whatever options larger companies might have like Alexa. It also means there is probably going to be a land grab and that there will be other interpretations of what the cashier-less checkout experience looks like, but Standard Cognition is hoping it’ll be able to get into enough stores to be an actual challenger to Amazon Go.