Toyota’s chance to score twice on a single play

Developers of autonomous driving software should realize that there is a precursor market for their technology in the material handling world—specifically for forklifts.

While the industry chatter could lead you to believe automated forklifts are available and capable today, experience behind the scenes reveals that most forklifts are not automated and most automated forklifts can only be trusted with the cleanest, clearest, least variable pallet handling tasks.

Automotive technology is rapidly advancing far beyond the limitations of current warehouse offerings. While Waymo’s automation software is making guesses about the future path of a bicyclist and driving through construction zones, automated forklifts struggle to figure out how to get forks in a pallet if the pallet is not presented with mechanical precision.

Forklifts are designed to move pallets; pallets are designed to provide a common handling interface for a virtually unlimited range of goods. In theory this should make pallets an excellent aid to automated handling. But reality gets messy.

Pallets can sag under load, to a degree that it becomes difficult to insert and remove the broader, thicker forks of most horizontal-transport lift trucks. Pallets are loaded unevenly, double-stacked, wedged together, and sometimes broken. Commonly used stretch wrap (think industrial grade version of kitchen plastic wrap) sometimes covers the fork pockets in a way that would make some sensors believe the pockets are blocked; human operators know to punch right through.

But all these challenges, and others like them, could be easily overcome with judicious use of sensors and the kind of learning algorithms, commonly called Artificial Intelligence (AI), which do best when the same basic task is performed again and again. Repetition allows the algorithm to iteratively refine its definition of what the core task is, which variations are acceptable, and which factors lead to failure—and forklift failures can be expensive.

It makes sense that major players in automated vehicles are not focusing on forklifts. The forklift market is vanishingly small compared to the automotive market. The Industrial Truck Association reports about 200,000 lift trucks shipped in North America in 2015. Automation technology replaces the operator, and it is “widely accepted that the operator makes up about 70% of the total cost of ownership of any lift truck,” according to trade publication Modern Materials Handling. Glassdoor.com reports that operators earn $30,000 annually on average. Assume that benefits will double the business cost to $60,000.

Not every forklift is doing a job that makes sense to automate; some are used only occasionally, or for moving a wide variety of non-standard and even unpalletized loads. Let’s assume that it only makes sense to automate half of the forklifts, or 100,000 units per year. If on average and operator costs are about $60,000 each, that’s a $6 billion annual market opportunity.

That’s not even on the radar of the automotive market, which the Wall Street Journal reports at $570 billion in 2015 sales for the USA. And that’s for the car itself, not for paying the operator.

But trying get the material handling market alone to pay off the R&D of autonomous driving is applying the wrong analysis to the opportunity. Automating forklifts is a secondary market – or more accurately, a precursor market – providing an opportunity to teach AI about the behavior of pedestrians and human drivers in a structured, controlled-access facility. Although some adaptations of the AI would be required for pallet engagement and lifting loads to the 40 foot heights of modern warehouses, these are minor tweaks on the core task of avoiding collisions and predicting human behavior.

Much like public roads, most warehouses have clear rules about where people should be walking and where vehicles may travel. But, also as can happen on the road, these rules are often subject to temporary exceptions. This provides an excellent opportunity to extensively test basic maneuvering and collision together with anticipating human behavior in a context where it is only semi-predictable. Warehouses are for the most part lacking significant grades (seeing “over” a hill is a problem for a scrupulously safe automated car) and they lack weather, which means that a warehouse environment is not a perfect test environment in all respects. At the same time, removing these complexities means a lower technical hurdle to get the automation truly ready for full hands-off.

Today only Toyota Industries has a stake in both the automotive and forklift markets. Toyota is lagging in the autonomous driving segment, but starting to push harder. Toyota can leverage the same investment twice, sending its automotive technology over to its forklift division – or it can wait for a start-up that originally aimed at road navigation to pivot to forklifts, as happened with fuel cells and lithium ion batteries.

Out on the open road, the big opportunity is replacing paid drivers, typically truck drivers, for the same reason as in forklifts: other than fuel, drivers are virtually the entire cost. Also, they’re getting harder to find. The chance to automate both tasks is the opportunity to dominate the logistics segment like Amazon, Google, or Apple in their respective domains.

Automation cannot and will not entirely do away with the need for humans in logistics for some time to come; they will need to work as caretakers, facilitators, and troubleshooters alongside the automatic equipment. No prior automation, from the industrial loom to the horseless carriage, ever arrived on the scene fully capable without human attention. But the well-proven preference people have for lower prices, and the increasing difficulty finding people to work in logistics jobs, means automation is inevitable, and the question is only who will be most successful in providing it.

Some businesses find themselves in the lucky position of having a marketable by-product on the way toward the product they set out to deliver. Gasoline started as waste from oil refining. Amazon’s cloud service started as a repackaging of a tool developed for internal needs. Now the opportunity exists for one of the companies on the path to autonomous driving to get some early revenue and real-world feedback — an off-highway alternative market for autonomous driving tech. Will Toyota seize their advantage? Or will it take a start-up to put things together in an unconventional yet effective combination?

Google Can’t Handle Reality – Yet

How hard can it be to make a fully-automated warehouse?

Well, so far it has stumped Amazon and Google.

To be fair, it depends on what exactly you’ll be doing in the warehouse. There are lights-out warehouses in use today. But widespread adoption remains elusive, always just about to take over the industry thanks to the latest technology somebody is selling. Despite what Business Insider called an “army of robots” in Walmart warehouses, there is still an army of people, too. Amazon is in a similar situation, with a growing human workforce despite acquiring Kiva Systems.

Google is also trying to solve automation of material handling. Just look at the effort they have put into it so far:

  1. At the end of 2013, Google had Andy Rubin in charge of a collection of seven robotics companies. Rubin was credited with the success of the Android mobile operating system. For robotics, he saw “clear opportunities” in “both manufacturing and logistics,” intending to “sell products sooner rather than later” according to the NY Times. But Rubin left a little less than a year later, with Google brushing off questions about competing with Amazon.
  2. Nevertheless, in 2015 Google filed for patent, granted about a year later, pertaining to automated vehicles working in a warehouse.
  3. Another patent granted in 2016 deals with coordination between flying and wheeled delivery robots.
  4. Google had a keynote presentation at the 2014 annual conference of the Material Handling Industry trade association.
  5. In March 2016, Google unveiled efforts to train a robotic picking arm. Not coincidentally, Amazon has for three years running been sponsoring a robotics challenge for picking.
  6. Speaking of training a learning algorithm to pick parts, Google Glass is now used in factories and warehouses.
  7. Google also has a long-running and slowly growing delivery service, Google Express. Partnering with retailers like Target and Walmart helps the traditional retailers compete with Amazon.

 

While the evidence suggests Google is gearing up to enter warehousing, material handling and logistics, as yet no specific products or services have been announced. If the business vision is to provide an open platform logistics service, a low-key approach makes sense; the last thing existing retailers need in their battle against Amazon is another Amazon under a Google brand.

Also, much as is the case with their autonomous car division Waymo, Google has nothing distinctive to offer until their warehouse robots are really autonomous. There are a plethora of warehouse automation products in the market today, and the only thing they all have in common is a failure to fully replace human workers.

Google’s failure to bring any warehouse solutions to market could be taken by incumbents as sign that they are safe – the challenge is just too great for Google to handle. Or it could just be a matter of time before Google Search is returning real, tangible results in the warehouses of retailers around the world, competing with Amazon’s “walled garden” with an “open platform” that resembles the competitive position Google took to compete with Apple’s iPhone. Incumbent players in the warehousing solutions industry should look at the long list of phone manufacturers who did not survive the clash of the titans, and adjust their strategies accordingly.