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?