I think … therefore I might be a material handling robot: interview with Ted Stinson
The days when robots were limited to a set of preprogrammed responses are over. Today’s AI-enabled bots are able to analyze, adapt, and even learn from each other, says Ted Stinson of AI software startup Covariant.
David Maloney has been a journalist for more than 35 years and is currently the group editorial director for DC Velocity and Supply Chain Quarterly magazines. In this role, he is responsible for the editorial content of both brands of Agile Business Media. Dave joined DC Velocity in April of 2004. Prior to that, he was a senior editor for Modern Materials Handling magazine. Dave also has extensive experience as a broadcast journalist. Before writing for supply chain publications, he was a journalist, television producer and director in Pittsburgh. Dave combines a background of reporting on logistics with his video production experience to bring new opportunities to DC Velocity readers, including web videos highlighting top distribution and logistics facilities, webcasts and other cross-media projects. He continues to live and work in the Pittsburgh area.
Artificial intelligence is set to explode across the distribution center, according to Ted Stinson, chief operating officer of Covariant, a Berkeley, California-based startup specializing in innovative software applications. We’ve heard similar predictions for years, but Stinson is confident the time is ripe—and he’s betting his career on it. Stinson left his job as partner with the venture capital firm Amplify Partners 18 months ago to join Covariant, which he had become acquainted with while helping the company develop its first business plan. After getting to know the Covariant team, he says, he decided it was time to chart a new career course and “be part of building an extraordinary company in an industry that will be at the forefront of the next wave of industrialization.”
He may be right. In its short life, Covariant has already made a splash in the supply chain industry, having forged impressive partnerships with Knapp and ABB to integrate its artificial intelligence (AI) software with their robotics systems.
But what exactly is artificial intelligence? How does it work and compare with the way humans think and act? DC Velocity Editorial Director David Maloney recently spoke with Stinson to learn more about this promising, yet complex, technology.
Q: How do you define artificial intelligence, or AI, as it applies to supply chain applications?
A: The concept of artificial intelligence is decades old. At one level, it is meant to represent the idea that systems software is capable of adapting and interacting in the way that you and I do as humans. AI today has evolved into ways that are achieving that goal. It is based on something called deep learning, where for the first time, systems have the ability to consume and analyze extraordinary amounts of data using a new, “neural network” approach to artificial intelligence. It has allowed people to apply and achieve artificial intelligence breakthroughs that simply weren’t possible prior to the last couple of years.
Q: You mention deep learning. And then there is reinforcement learning. Can you explain what they are?
A: The idea of deep learning and reinforcement learning is to learn from experience and to reinforce a behavior through failure or success. These techniques can also be applied to software, having it learn to adapt through trial and error. If you step back and think about that, that’s a huge contrast to the way software has worked historically, where every behavior, every interaction of the software had to be preprogrammed. You would have to specifically write into your code an action you wanted the software program or a robot to take. Now, the nature of these programs is that they enable the software or robot to take a try at something, and then the act of trying is analyzed and adapted to enable the software to learn from success and learn from failure.
Q: The artificial intelligence that you’re developing is going into robotics systems, such as picking robots. Why did you choose this area for your initial deployments?
A: The Covariant ambition is to build a universal AI. We call it the Covariant Brain. It is meant to be essentially the cognitive system for a robot—the “brain” that gives it the ability to see and reason and act on the world around it. We chose to focus on warehouses initially because the logistics market offers such a great opportunity to deploy these capabilities to automate jobs that are tedious and, thus, hard to fill. Things like order picking in an e-commerce or grocery or apparel warehouse are great examples. These jobs are clearly repetitive in nature, but at the same time, every pick has a degree of variability and change, which is what makes warehouse environments so challenging.
Q: What are some of the products robots have difficulty recognizing or handling?
A: A robot system essentially has cameras as its eyes. Seeing things in boxes that come in basic colors is relatively easy. They were among the first things that were “solvable,” but what’s beyond that? What about objects with subtle variations in their shapes, things that are flexible like apparel? A human can look inside a bin or tote containing different types of white T-shirts and pretty easily identify one white T-shirt from another. But to a robot, each T-shirt looks a little different when they are all folded and placed in the bin slightly differently. All those subtle variations represent a change—small, but fundamental.
Reflections from objects are also challenging. Even the way the objects get stacked and placed in the tote can present problems. A human can move around to get a better view of the objects in a box. But with a robot, getting a clearer view of what’s inside the box presents a fundamental challenge.
Q: So, we’ve talked about the recognition problems. What about the problems robots have in picking a variety of different objects?
A: Before the advent of modern AI, it simply was not possible for a robot to handle many different types of objects. Think of an apparel company, for example, that changes its products every season, so you have tens of thousands of items that change on a three- to four-month basis.
Beyond the changing product mix, there’s the challenge of gathering objects that are difficult to pick up. One of our partners, ABB, is one of the world’s largest robotic manufacturers. When it set out to find a partner for AI about a year ago, it developed a series of 26 different tests that essentially mimic real-world conditions. One of the tests was picking rubber ducks. As you can imagine, there are relatively few ways that a typical robotic end effecter can pick up a rubber duck successfully. So, the ability to understand the process—to learn from trying to pick up a rubber duck—is an example of something that is very simple for you and me to do, but extraordinarily hard for a robot to do and requires the ability to try to learn and adapt its behavior.
Q: I would imagine too that apparel can be very difficult to pick, especially if you have a vacuum end effecter that may not be able to grab cloth easily.
A: It is, to be sure. Picking apparel is one of the challenges we’ve put a lot of effort into solving. When you pick up a piece of clothing, it changes shape, so it’s different from, say, a box. In order to pick apparel successfully, the robot needs to understand that when it picks it up an object, that object is going to change shape, and that the new shape, whatever it is, will then have a very material impact on how the robot completes its task.
Q: Just as you’d find with a claw machine at a game arcade, objects in a box may also be too close to the sides for the robot to grab it. In that case, would the robot have to figure out how to manipulate or move the object to gain better access?
A: Yes. A key aspect of how we approach our systems is there is literally infinite variability in how objects can be sitting in a bin. So, you have to have a system that can both deal with all of the known conceivable ways that these objects can be positioned, then also adapt on the fly. We have developed a couple of different proprietary techniques. There are ways in which, for new scenes, new types of environments, the robot can adapt to that environment. It goes back to the core capability of trying, learning, and adapting its behavior until it succeeds.
Q: Could you address how a robot must also learn to adjust its approach based on the product, such as when picking a fragile item?
A: The example of how much pressure a robot should exert in picking up an object is something that could only be solved in a data-driven way. When it comes to the amount of pressure that could theoretically be applied, the possibilities are literally infinite. There is no way you could write a traditional software program that accounts for every possible level of pressure. You have to have a system that can learn and adapt in order to be able to assess the right amount of pressure to apply, just as for a human, there are an incredible number of instinctual judgments that have to be made in handling an object. It is through this deep-learning based approach that we are able to essentially mimic that sort of judgment and adaptability on the fly.
Q: What goes into training the robots to handle products they haven’t encountered before? Do you have to physically manipulate a robot to train it?
A: This is actually part of the secret sauce, which is that the system does this on its own. It is able, through trial and error, to learn and adapt to things it has never seen before and do so without any human intervention or human manipulation.
The initial work of adapting the AI to a customer’s operation is built into the preliminary process that we and our partners go through with customers. The Covariant Brain essentially comes out of the box “smart,” so implementations can be surprisingly fast. Most of the time, it is quick enough that it has no material impact on an operation’s throughput.
In the very beginning, the system might encounter new things that take an extra pick or two before it learns and adapts. But it generalizes these learnings and adapts relatively quickly, so that before long, it is, for the most part, operating at the performance levels that you’re looking for in the system. That accelerated learning process is one of the key benefits unlocked by modern AI systems.
Q: So basically, when you deploy a system, you’re actually using the knowledge from other systems that have been deployed before it? The robots can draw on the experience of previous robots?
A: Yes. This is really an important concept in envisioning operations—the idea that each robot deployed after the first one learns from those around it. So collectively over time, they accelerate the learning amongst themselves. That is one of the key value propositions. So, you could have a decanting robot that is learning from the experiences of an order picking robot. That ability to share learnings is unlocked by this underlying modern AI deep-learning based approach.
Q: Is this learning done in real time?
A: As you find with people, learning happens at different levels. There is some learning that happens in the moment and other learning that happens upon what I call “reflection.” Both are aspects of how the systems improve over time.
Q: You have industry partnerships with Knapp and ABB. Could you talk about how you’re working with these companies to deploy AI robotic solutions?
A: Covariant is an AI company. Our expertise is in building software. We partner with companies that have strong domain knowledge and strong robotics capabilities. Knapp and ABB are examples of both. Knapp has been one of the pioneers in the robotics field. It was first to market with an order picking solution. It has a long history of investing to bring this capability to market, and it recognized the limitations of traditional software when it comes to solving robotic picking problems.
Knapp concluded that we had developed something that was essentially the key to unlocking the adaptability and learning capability of robotics systems used in material handling applications. We entered into a partnership last year and have now brought two different use cases to market, with several more on the horizon. Overall, I am really excited about the partnership.
Q: Could you elaborate a little on those deployments and the kinds of products being handled?
A: Obeta is a German hardware supplier, and we’re seeking to introduce robotic systems into its operations. The system that is deployed at Obeta is the first system that we and Knapp have publicly announced. What’s notable about this deployment is that it’s a system that has achieved autonomy, which we think is a really important notion.
At the end of the day, what you want as an operator is the ability to have a robotic system that performs at the same level as your traditional manual processes. No asterisks, no exceptions. It just works in the same way. Hopefully, over time, it actually works better. That for us has been the benchmark.
Q: You are obviously looking at use cases for these technologies. Are there particular applications you’re looking at?
A: Within materials handling, we’re just getting started. We and Knapp have brought to market the order picking solution. We have half a dozen or more use cases that we and Knapp and other partners are in the process of formalizing. Our goal for supply chain and material handling leaders is to show that we will deliver a roadmap for a set of use cases and stations that are going to provide substantial coverage across a modern warehouse operation. One thrust of what we are looking to do is expand within that environment.
Q: So, this might involve other types of robotic systems beyond the robotic arm?
A: Absolutely. We haven’t talked a lot about that publicly, but we tend to think of a warehouse itself as one big robot. The conveyance systems and various mechanical devices are all systems that ultimately can be optimized through AI in terms of throughput, density, and performance. Our vision is to be able to take and leverage the underlying models and the Covariant Brain to unlock those gains over time.
Q: Obviously, we’re going through some very unusual times right now with the Covid-19 crisis. I would guess that automation is top of mind for many people because of the need for social distancing and eliminating human touches. Has that changed how you go to market?
A: Our focus from a commercial perspective through Covid-19 is to be there with our partners and for our customers to help them figure out where to get started. The question I get from every supply chain leader I talk to is not “Should I deploy robotics,” but “How do I deploy it and where do I start?” Our energy has been trying to guide our prospective customers through the choices and develop a framework that lets them start in the right place and then develop a roadmap for where to go next.
Q: Have we reached the point where the technology has finally caught up with the promise of AI?
A: The technology is ready. It is here today, and at the same time, it is still at the first stage of the journey. But we’re certainly at the point where I would encourage folks to invest the time to understand the different offerings. Now is the time in the evolution of these technologies for supply chain leaders to be looking at them closely and figuring out how they might use them to enhance their operations. I really encourage folks to look past the marketing and the demos in controlled environments. Ultimately, we need to be solving the challenges in the real world. I am really optimistic but also want to encourage people to be smart shoppers as they go through and look at these different capabilities.
The logistics process automation provider Vanderlande has agreed to acquire Siemens Logistics for $325 million, saying its specialty in providing value-added baggage and cargo handling and digital solutions for airport operations will complement Netherlands-based Vanderlande’s business in the warehousing, airports, and parcel sectors.
According to Vanderlande, the global logistics landscape is undergoing significant change, with increasing demand for efficient, automated systems. Vanderlande, which has a strong presence in airport logistics, said it recognizes the evolving trends in the sector and sees tremendous potential for sustained growth. With passenger travel on the rise and airports investing heavily in modernization, the long-term market outlook for airport automation is highly positive.
To meet that growing demand, the proposed transaction will significantly enhance customer value by providing accelerated access to advanced technologies, improving global presence for better local service, and creating further customer value through synergies in technology development, Vanderlande said.
In a statement, Nuremberg, Germany-based Siemens Logistics said that merging with Vanderlande would “have no operational impact on ongoing or new projects,” but that it would offer its current customers and employees significant development and value-add potential.
"As a distinguished provider of solutions for airport logistics, Siemens Logistics enjoys a first-class reputation in the baggage and air-cargo handling areas. Together with Vanderlande and our committed global teams, we look forward to bringing fresh impetus to the airport industry and to supporting our customers' business with future-oriented technologies," Michael Schneider, CEO of Siemens Logistics, said in a release.
The initiative is the culmination of the companies’ close working relationship for the past five years and represents their unified strength. “We recognized that going to market under a cadre of names was not helping our customers understand our complete turn-key services and approach,” Scott Lee, CEO of Systems in Motion, said in a release. “Operating as one voice, and one company, Systems in Motion will move forward to continue offering superior industrial automation.”
Systems in Motion provides material handling systems for warehousing, fulfillment, distribution, and manufacturing companies. The firm plans to complete a rebranded web site in January of 2025.
I recently came across a report showing that 86% of CEOs around the world see resiliency problems in their supply chains, and that business leaders are spending more time than ever tackling supply chain-related challenges. Initially I was surprised, thinking that the lessons learned from the Covid-19 pandemic surely prepared industry leaders for just about anything, helping to bake risk and resiliency planning into corporate strategies for companies of all sizes.
But then I thought about the growing number of issues that can affect supply chains today—more frequent severe weather events, accelerating cybersecurity threats, and the tangle of emerging demands and regulations around decarbonization, to name just a few. The level of potential problems seems to be increasing at lightning speed, making it difficult, if not impossible, to plan for every imaginable scenario.
What is it Mike Tyson said? Everyone has a plan until they get punched in the mouth.
It has never been more important to be able to pivot and adjust to challenges that can throw you off your game. The report I referenced—the “2024 Supply Chain Barometer” from procurement, supply chain, and sustainability consulting firm Proxima—makes the case for just that. The company surveyed 3,000 CEOs from the United Kingdom, Europe, and the United States and found that the growing complexities in global supply chains necessitate a laser-sharp focus on this area of the business. One example: Rightshoring, which is the process of moving business operations to the best location, means companies are redesigning and reconfiguring their supply chains like never before. The study found that large numbers of CEOs are grappling with the various subsets of rightshoring: 44% said they are planning to or have already undertaken onshoring, for instance; 41% said they are planning to or have undertaken nearshoring; 41% said they are planning to or have undertaken friendshoring; and 35% said they are planning to or have undertaken offshoring.
But that’s not all. CEOs are also struggling to deal with the rise of artificial intelligence (AI) and its application to business processes, the potential for abuse and labor rights issues in their supply chains, and a growing number of barriers to their companies’ decarbonization efforts. For instance:
Nearly all of those surveyed (99%) said they are either using or considering the use of AI in their supply chains, with 82% saying they are planning new initiatives this year;
More than 60% said they are concerned about the potential for human or labor rights issues in their supply chains;
And virtually all (99%) said they face barriers to decarbonization, with 30% pointing to the complexity of the work required as the biggest barrier.
Those are big issues to contend with, so it’s no surprise that 96% of the CEOs Proxima surveyed said they are dedicating equal (41%) or more time (55%) to supply chain issues this year than last year. And changing economic conditions are adding to the complexity, according to the report.
“As inflation fell throughout last year, there were glimmers of markets stabilizing,” the authors wrote. “The reality, though, has been that global market dynamics are shifting. With no clear-set position for them to land in, CEOs must continue to navigate their organizations through an ever-changing landscape. Just 4% of CEOs foresee the amount of time spent on supply chain-related topics decreasing in the year ahead.”
Simon Geale, executive vice president and chief procurement officer at Proxima, added some perspective.
“It’s fair to say that the complexities of global supply chains continue to have CEOs around the world scratching their heads,” he wrote. “The results of this year’s Barometer show that business leaders are spending more and more time tackling supply chain challenges, reflecting the multiple challenges to address.”
Perhaps the extra focus on supply chain issues will help organizations improve their ability to roll with the punches and overcome resiliency challenges in the year ahead. Only time will tell.
Investing in artificial intelligence (AI) is a top priority for supply chain leaders as they develop their organization’s technology roadmap, according to data from research and consulting firm Gartner.
AI—including machine learning—and Generative AI (GenAI) ranked as the top two priorities for digital supply chain investments globally among more than 400 supply chain leaders surveyed earlier this year. But key differences apply regionally and by job responsibility, according to the research.
Twenty percent of the survey’s respondents said they are prioritizing investments in traditional AI—which analyzes data, identifies patterns, and makes predictions. Virtual assistants like Siri and Alexa are common examples. Slightly less (17%) said they are prioritizing investments in GenAI, which takes the process a step further by learning patterns and using them to generate text, images, and so forth. OpenAI’s ChatGPT is the most common example.
Despite that overall focus, AI lagged as a priority in Western Europe, where connected industry objectives remain paramount, according to Gartner. The survey also found that business-led roles are much less enthusiastic than their IT counterparts when it comes to prioritizing the technology.
“While enthusiasm for both traditional AI and GenAI remain high on an absolute level within supply chain, the prioritization varies greatly between different roles, geographies, and industries,” Michael Dominy, VP analyst in Gartner’s Supply Chain practice, said in a statement announcing the survey results. “European respondents were more likely to prioritize technologies that align with Industry 4.0 objectives, such as smart manufacturing. In addition to region differences, certain industries prioritize specific use cases, such as robotics or machine learning, which are currently viewed as more pragmatic investments than GenAI.”
The survey also found that:
Twenty-six percent of North American respondents identified AI, including machine learning, as their top priority, compared to 14% of Western Europeans.
Fourteen percent of Western European respondents identified robots in manufacturing as their top choice compared to just 1% of North American respondents.
Geographical variances generally correlated with industry-specific priorities; regions with a higher proportion of manufacturing respondents were less likely to select AI or GenAI as a top digital priority.
Digging deeper into job responsibilities, just 12% of respondents with business-focused roles indicated GenAI as a top priority, compared to 28% of IT roles. The data may indicate that GenAI use cases are perceived as less tangible and directly tied to core supply chain processes, according to Gartner.
“Business-led roles are traditionally more comfortable with prioritizing established technologies, and the survey data suggests that these business-led roles still question whether GenAI can deliver an adequate return on investment,” said Dominy. “However, multiple industries including retail, industrial manufacturers and high-tech manufacturers have already made GenAI their top investment priority.”
Regardless of the elected administration, the future likely holds significant changes for trade, taxes, and regulatory compliance. As a result, it’s crucial that U.S. businesses avoid making decisions contingent on election outcomes, and instead focus on resilience, agility, and growth, according to California-based Propel, which provides a product value management (PVM) platform for manufacturing, medical device, and consumer electronics industries.
“Now is not the time to wait for the dust to settle,” Ross Meyercord, CEO of Propel, said in a release. “Companies should approach this election cycle as an opportunity to thrive in the face of constant change by proactively investing in technology and talent that keeps them nimble. Businesses always need to be prepared for changing tariffs, taxes, or geopolitical tensions that lead to unexpected interruptions – that’s just the new normal.”
In Propel’s analysis, a Trump administration would bring a continuation of corporate tax cuts intended to bolster American manufacturing. However, Trump’s suggestion for spiraling tariffs may benefit certain industries, but would drive up costs for businesses reliant on global supply chains.
In contrast, a Harris administration would likely continue the current push for regulatory reforms that support sectors like AI, digital assets, and manufacturing while protecting consumer rights. Harris would also likely prioritize strategic investments in new technologies and provide tax incentives to promote growth in underserved areas.
And regardless of the new administration, the real challenge will come from a potentially divided Congress, which could impact everything from trade negotiations to tax policies, Propel said.
“The election outcome is less material for businesses,” Meyercord said. “What is important is quickly adapting to shifting policies or disruptions that address ‘what if’ scenarios and having the ability to pivot your strategy. A responsive manufacturing sector will have a significant impact on the broader economy, driving growth and favorably influencing GDP. One thing is clear: the only certainty is change.”