Figuring out which products are in stock and which stock is likely to run low is a never-ending battle, as shoppers spend an estimated 40 billion hours picking things off shelves. It's also error-prone -- employees regularly misplace an estimated one in 10 items, contributing to global retail revenue losses exceeding $1 trillion. But drones hold the answer to the inventory tracking problem, if you ask serial entrepreneur Richard Schwartz. So strong is he in this conviction that he cofounded Pensa Systems, which develops inventory systems equipped with computer vision algorithms that "understand" what's on store shelves. The Austin startup today announced the close of a $10 million follow-on seed funding round that brings its total raised to $17.2 million, and according to investor and Pensa advisory board member James McCann, the future is looking bright.
Pensa, a company that makes an autonomous mobile perception system to track inventory in stores, has $5 million in additional funding after a newly-announced round. With a total funding of more than $7 million, Pensa is now a contender in a tightening race to build the ultimate autonomous shelf-scanning system for brick-and-mortar retail. Other companies in the space include Simbe and Bossa Nova, which make shelf-scanning robots that can be deployed to work safely alongside customers as they scan shelves and track inventory. The robots roam stores and quickly scan merchandise on shelves using machine vision or RFID readers, helping retailers keep track of inventory more efficiently than employees with scanning guns. Inventory management is a bigger problem than you might think, potentially leading to billions of dollars of lost revenue.
The robot uprising is coming to a store near you. And no, I'm not talking about Skynet building T-1000 Terminators to take over the world (or store in this case). I am talking about how the retail store is drastically changing to become more automated, using artificial intelligence, and relying on non-human assistance for check-outs, price checks, and inventory management. Robots and drones are in stores now and retailers are looking to build upon their capabilities for the foreseeable future. Robots in the warehouse are certainly nothing new.
From driver-assisted vehicles on our city streets to self-driving vehicles on our factory floors, robotic and autonomous systems are becoming commonplace. You may even have one in your home, vacuuming the floors for you while you stay busy with more meaningful work. The truth is, these hands-off systems are just about everywhere anymore. In a sign of the growing adoption of robotic systems, the market-advisory firm ABI Research predicts that, by 2025, more than 4 million commercial robots will be on the job in over 50,000 warehouses, up from just under 4,000 robotic warehouses in 2018.1 And that's just warehouses -- that's not the "everywhere else" where these worker bees are found.
Deep learning techniques do a good job at building models by correlating data points. But many AI researchers believe that more work needs to be done to understand causation and not just correlation. The field of causal deep learning -- useful in determining why something happened -- is still in its infancy, and it is much more difficult to automate than neural networks. Much of AI is about finding hidden patterns in large amounts of data. Soumendra Mohanty, executive vice president and chief data analytics officer at L&T Infotech, a global IT service company, said, "Obviously, this aspect drives us to the'what,' but rarely do we go down the path of understanding the'why.'"