Shelf Engine
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IoT and data science will boost foodtech in the post-pandemic era
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.
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Even as e-grocery usage has skyrocketed in our coronavirus-catalyzed world, brick-and-mortar grocery stores have soldiered on. While strict in-store safety guidelines may gradually ease up, the shopping experience will still be low-touch and socially distanced for the foreseeable future.
This begs the question: With even greater challenges than pre-pandemic, how can grocers ensure their stores continue to operate profitably?
Just as micro-fulfillment centers (MFCs), dark stores and other fulfillment solutions have been helping e-grocers optimize profitability, a variety of old and new technologies can help brick-and-mortar stores remain relevant and continue churning out cash.
Today, we present three “must-dos” for post-pandemic retail grocers: rely on the data, rely on the biology and rely on the hardware.
Rely on the data
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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.
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%.
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Shelf Engine has a plan to reduce food waste at grocery stores, and $12 million in new cash to do it
For the first few months it was operating, Shelf Engine, the Seattle-based company that optimizes the process of stocking store shelves for supermarkets and groceries, didn’t have a name.
Co-founders Stefan Kalb and Bede Jordan were on a ski trip outside of Salt Lake City about four years ago when they began discussing what, exactly, could be done about the problem of food waste in the U.S.
Kalb is a serial entrepreneur whose first business was a food distribution company called Molly’s, which was sold to a company called HomeGrown back in 2019.
A graduate of Western Washington University with a degree in actuarial science, Kalb says he started his food company to make a difference in the world. While Molly’s did, indeed, promote healthy eating, the problem that Kalb and Bede, a former Microsoft engineer, are tackling at Shelf Engine may have even more of an impact.
Food waste isn’t just bad for its inefficiency in the face of a massive problem in the U.S. with food insecurity for citizens, it’s also bad for the environment.
Shelf Engine proposes to tackle the problem by providing demand forecasting for perishable food items. The idea is to wring inefficiencies out of the ordering system. Typically about a third of food gets thrown out of the bakery section and other highly perishable goods stocked on store shelves. Shelf Engine guarantees sales for the store, and any items that remain unsold the company will pay for.
Image: OstapenkoOlena/iStock
Shelf Engine gets information about how much sales a store typically sees for particular items and can then predict how much demand for a particular product there will be. The company makes money off of the arbitrage between how much it pays for goods from vendors and how much it sells to grocers.
It allows groceries to lower the food waste and have a broader variety of products on shelves for customers.
Shelf Engine initially went to market with a product that it was hoping to sell to groceries, but found more traction by becoming a marketplace and perfecting its models on how much of a particular item needs to go on store shelves.
The next item on the agenda for Bede and Kalb is to get insights into secondary sources like imperfect produce resellers or other grocery stores that work as an outlet.
The business model is already showing results at around 400 stores in the Northwest, according to Kalb, and it now has another $12 million in financing to go to market.
The funds came from Garry Tan’s Initialized and GGV (and GGV managing director Hans Tung has a seat on the company’s board). Other investors in the company include Foundation Capital, Bain Capital, 1984 and Correlation Ventures .
Kalb said the money from the round will be used to scale up the engineering team and its sales and acquisition process.
The investment in Shelf Engine is part of a wave of new technology applications coming to the grocery store, as Sunny Dhillon, a partner at Signia Ventures, wrote in a piece for TechCrunch’s Extra Crunch (membership required).
“Grocery margins will always be razor thin, and the difference between a profitable and unprofitable grocer is often just cents on the dollar,” Dhillon wrote. “Thus, as the adoption of e-grocery becomes more commonplace, retailers must not only optimize their fulfillment operations (e.g. MFCs), but also the logistics of delivery to a customer’s doorstep to ensure speed and quality (e.g. darkstores).”
Beyond Dhillon’s version of a delivery-only grocery network with mobile fulfillment centers and dark stores, there’s a lot of room for chains with existing real estate and bespoke shopping options to increase their margins on perishable goods, as well.
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Shelf Engine uses machine learning to stop food waste from eating into store margins
Shelf Engine’s team
While running Molly’s, the Seattle-based ready meal wholesaler he founded, Stefan Kalb was upset about its 28 percent food wastage rate. Feeling that the amount was “astronomical,” he began researching how to lower it — and was shocked to discovered Molly’s was actually outperforming the industry average. Confronted by the sheer amount of food wasted by American retailers, Kalb and Bede Jordan, then a Microsoft engineer, began working on an order prediction engine.
The project quickly brought Molly’s percentage of wasted food down to the mid-teens. “It was one of the most fulfilling things I’ve ever done in my career,” Kalb told TechCrunch in an interview. Driven by its success, Kalb and Jordan launched Shelf Engine in 2016 to make the technology available to other companies. Currently participating in Y Combinator, the startup has already raised $800,000 in seed funding from Initialized Capital, the venture capital firm founded by Alexis Ohanian and Gerry Tan, and is now used at more than 180 retail points by clients including WeWork, Bartell Drugs, Natural Grocers and StockBox.
Shelf Engine’s order prediction engine analyzes historical order and sales data and makes recommendations about how much retailers should order to minimize waste and increase margins. The more retailers use Shelf Engine, the more accurate its machine learning model becomes. The system also helps suppliers, because many operate on guaranteed sales, or scan-based trading, which means they agree to take back and refund the purchase price of any products that don’t sell by their expiration date. While running Molly’s, Kalb learned what a huge pain point this is for suppliers. To alleviate that, Shelf Engine itself buys back unsold inventory from the retailers it works with, taking the risk away from their suppliers.
Kalb, Shelf Engine’s CEO, claims the startup’s customers are able to increase their gross margins by 25 percent and reduce food waste from an industry average of 30 percent to about 16-18 percent for items that expire within one to five days. (For items with a shelf life of up to 45 days, the longest that Shelf Engine manages, it can reduce waste to as little as 3-4 percent).
The food industry operates on notoriously tight margins, and Shelf Engine wants to relieve some of the pressure. Running Molly’s, which supplies corporate campuses, including Microsoft, Boeing and Amazon, gave Kalb a firsthand look at the paradox faced by retail managers. Even though a lot of food is wasted, items are also frequently out of stock at stores, annoying customers. Then there is the social and environmental impact of food waste — not only does it raise prices, food rotting in landfills is a major contributor to methane emissions.
A store manager may need to make ordering decisions about thousands of products, leaving little time for analysis. Though there are enterprise resource planning software products for food retail, Kalb says that during store visits he realized a surprisingly high number still rely on Excel spreadsheets or pen and paper to manage reoccurring orders. The process is also highly subjective, with managers ordering products based on their personal preferences, a customer’s suggestion or what they’ve noticed does well at other stores. Sometimes retailers get stuck in a cycle of overcorrecting, because if customers complain about missing out on something, managers order more inventory, only to end up with wastage, then scaling back their next order and so on.
“Americans want selection at all times, we get furious when a product is sold out, but it’s a really hard decision to make about how much challah bread to stock on a Monday,” says Kalb. “Yet we are doing that ad hoc.”
When retailers use Shelf Engine’s prediction engine, it decides how many units they need and then submits those orders to their suppliers. After products reach their sell-by dates, the retailer reports back to Shelf Engine, which only charges them for units they sold, but still pays suppliers for the full order. As time passes, Shelf Engine can make more granular predictions (for example, how precipitation correlates with the sale of specific items like juice or bread).
In addition to providing the impetus for the creation of Shelf Engine, Molly’s also helped Kalb and Jordan, its CTO, build the startup’s distribution network. Kalb says Shelf Engine has benefited from the network effect, because when a retailer signs up, their suppliers will often mention it to other retailers that they serve. Kalb says the startup is currently hiring more engineers and salespeople to help Shelf Engine leverage that and spread through the food retail industry.
“It’s a world I got to know and I came into the world fascinated with healthy food and making delicious grab-and-go meals,” says Kalb. “It turned into a fascination with this crazy market, which is so massive and still has so many opportunities to be maximized.”
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