Innovating Category Management – The Role of Robots in the Future of Category Management

The robot’s journey to establish its presence in retail stores is a decade in the making. It is a slow progression caused by human factor perception and a pandemic, as well as the fact that it’s a traditionally slow industry to adopt and a complex problem to solve. The main challenge is showing a viable return on investment. Retailers’ ROI can only be achieved when the solution can autonomously collect data accurately and repeatedly on a large scale.

All these factors contributed to the slow adoption of robots in retail stores. Although some retailers are exiting pilots with a firm NO, we are starting to see more regional adoption grow, with most large retailers still on the sidelines.  

Robots have a long-term role in retail, but they will take a new form, expand to address more complex use cases, and have a clearer ROI. 

Expansion 

Only some of the original players’ solutions remain active after a few recent exits.  

Simbe Robotics impressively secured a timely, sizeable second round of investments from Eclipse. This is positive news, especially if they plan to use Eclipse’s expertise with expanded robotics capabilities. Simbe has been growing its presence with regional and independent grocers. Currently, I estimate they have the most banner presence globally. The shape of the Simbe Tally robot is still differentiating in size, form factor, and visual appeal compared to existing and newer solutions in the market, which makes its presence in crowded retail aisles less intrusive.      

Badger Technologies is expanding on its multipurpose robots supported by its parent, Jabil. Currently present in several regional retailers globally, Badger recently announced that it is replacing its spill-detection deployed robots with new multipurpose robots. The company created this new generation of robots for spill detection, shelf data collection, and security. Badger showed value by first developing a spill detection solution and building its scaling operational muscle early on and then moving to insight and security with a multifunctional robot.  

Badger rollouts will have the most robots deployed at a single retailer after Bossa Nova’s exit from Walmart with its 400+ deployments. I also estimate that Badger has the most store count deployments of data collection robots globally.  

Zippedi maintains its steady and conservative approach with a low-cost model. Building on its presence in South America, the company is expanding globally, currently in a large U.S. retailer pilot and several in Europe and beyond.  

Brain Corp expanded the use of its floor-sweeping machines by adding cameras for data collection. The company collects data by performing additional passes post-floor sweeping for better image quality. Brain is launching a robot in 2024 dedicated to collecting shelf inventory data. Additionally, the company has a new robot available for product delivery and trash recycling pickup from the aisle. It can also perform mobile promotions while navigating store aisles. It’s too early to tell about the ROI provided by this new robot.  

Almost all remaining players have a global footprint with a store count hovering around 100+ stores in various retailer banners among the different robot functions. Few have achieved the century mark of store count in an individual banner.   

Regrettable exits 

Following Bossa Nova, Zebra Technologies’ early exit from robotic data collection was a setback. With its global presence and solution footprint, Zebra had the potential to bring an end-to-end robotic-based solution to retailers faster. Unfortunately, I assume the company could not build a solution that delivered accurately, repeatedly at scale and showed enough ROI. It could also be a calculated opportunistic move to hold and reenter via acquisition or simply bank on its investment in Focal Systems as the long-term solution with fixed cameras.  

Fixed cameras 

The robotic solution was always meant to be transitional and short term in its current form. Eventually, fixed cameras will be able to scale and relieve the robots of their current duties.  

Focal Systems is one of the fixed camera solutions leading the way and starting to show signs of scale capabilities. There is news of them scaling across Walmart Canada, and I look forward to learning more about success stories driven by near real-time shelf data from this deployment.  

Or not so fixed 

A new hybrid solution, a movable fixed camera on a track inside a tube built by Spacee, combines the benefits of robots and fixed cameras and provides a viable alternative with the potential to go the distance. One camera on a track is able to cover a whole side of an aisle.

This new creation minimizes challenges brought by robots navigating a store and overcomes obstacles encountered by fixed cameras or handheld devices. It builds on the strength of robots and fixed cameras while eliminating their drawbacks.  

Handheld capture 

The number of providers offering handheld image capture applications for smartphones and tablets is increasing rapidly, creating a crowded market with erratic messaging about capabilities. Promising solutions are available from Clobotics, Infilect, Pensa Systems, and Retech Labs. 

With the advancements in computer vision and services provided by companies like Google and Microsoft, along with upgraded camera hardware available on mobile devices and the inclusion of lidar capabilities, it has become easier to digitize the store. These technologies make it more accessible and possible to gain store insights rapidly. 

A few solutions are starting to differentiate themselves, expand store count, and gain market share and recognition in the industry. The secret sauce in collecting these insights efficiently is the feedback given during the image capture process that minimizes human error and increases image quality.  

Best solution 

What is the go-to solution? There is still room for consolidation through partnerships and acquisitions, especially for handheld solutions; the field is too crowded as is.  

For robots, delivering a commercial-ready solution takes time and effort. If companies do not manage their runway efficiently from day one, they cannot go the distance without significant backing.  

The best solution for retailers will depend on size, store environment, store count, and high-priority problems to solve. Each of the solutions excels in various differentiating areas. The table below shows the differentiation of multiple approaches:  

Function	Handheld	Robots	Fixed Cameras	Semi-Fixed Cameras
Stock detection (OOS, low, medium, in stock)	Y	Y	Y	Y
Planogram compliance	Y	Y	Y	Y
Price tag	Y	Y	Y	Y
Shelf tag/Product description	Limited	Y	Limited	Limited
Barcode detection	Limited	Y	Limited	Limited
Video capture	Y	Y	N	Y
Overstock	Y	N	Y	Y
Product image	Medium	Medium	High	High
Shopper disruption	Medium	High	Low/NA	Low/NA
Cost	Low	High	Medium	Low
Install/Implementation cost	NA	Medium	High	Low
Maintenance needs	None	High	Medium	Low
Human dependency	High	Medium	Medium	Low
Scan frequency per day	Labor Availability	1-3	Unlimited	Unlimited
Near real time	Labor Availability	N	Y	Y
Safety concerns	NA	Medium	NA	NA

Second act 

The present-day window on robots with their current configuration is narrowing in retail. Despite a decade-long development, retailers have yet to achieve the desired scale. The next phase and next version of the robots will have longevity. This will occur when robots grow arms that will enable them to pick products off the shelf to fulfill orders and possibly restock shelves. This is an even bigger ask, but ultimately, it is the most desirable outcome. I have seen several advancements in this direction from current solutions and newcomers to this area. It’s an even more difficult use case, and I do not expect an overnight outcome. It is, however, the ultimate use case for robots in retail stores.  

Whole store, all stores  

Using robots in whole stores and all stores continues to be elusive, but it is inevitable. Once we start seeing the benefits of chain-wide rollouts, it will be contagious and drive faster adoption, whether robots, cameras, or a combination. The good news is that we progressed, and some of these approaches will slowly become the standard. 

Future certainty 

The future is awaiting near real-time shelf data collection at scale for the whole store in all stores. This data type is decades in the making and has built up increased anticipation. Near real-time space insights are the single critical data point in delivering autonomous, smart end-to-end category management.  

Smart category management will make half of the current solutions redundant, and the other half will be smarter and open the door to many new solutions. The impact will be long-lasting, and disruption will be felt positively for years. The future is in the making — are you ready?