Sure you have plenty of brainpower. But when it comes to complex logistics or warehousing decisions, an intelligent software "agent" may be able to make the call better, faster or more cost effectively than you can.
In a summer when "The Matrix: Reloaded" reigns at the box office, you probably won't be surprised to know that computers are already making decisions about our lives without any human intervention. Artificial intelligence has become a mundane reality, used in Web services such as Amazon.com's, and to control production lines, city traffic patterns, telephone call routing and even some banking functions. But the logistics and transportation sectors have so far been reluctant to implement so-called smart software, for reasons of money, time and plain old fear.
All that is about to change, according to several experts in logistics technology. "Over the next nine to 12 months you'll see significant pilot projects taking place, at which time the concept will either be proven or disproven," says John Karonis, director of fulfillment technology at Kurt Salmon Associates in Princeton, N.J. "We're confident that it will prove to be a worthwhile endeavor and that we'll then see it rolled out on a much larger scale." Karonis has been working on a project to combine the power of radio-frequency identification (RFID) tags with intelligent software in a way that allows a computer to decide how to fix problems without human intervention every time there's a glitch in the movement of goods.
But what is intelligent software? Dr. Noel Greis, director of The Center for Logistics and Digital Strategy at The Kenan Center, University of North Carolina-Chapel Hill , explains that it's a type of artificial intelligence (AI). AI falls into two broad categories, she says. One is aligned with robotics and artificial vision, the sort of science that holds the promise of an electronic butler who hands you a drink and makes dinner when you get home, or an order-picking machine that would notice if a product was damaged and do something about it. But the other side includes what's known as intelligent software "agents." Also known as "bots," these are software packets that act as autonomous, decision-making entities, capable of coming up with solutions to problems and acting on them automatically.
Intelligent agents can be very simple. A good example is the way Amazon.com offers you a list of books you might like to buy in addition to the one you've just chosen. That's simply an agent that's programmed to think: "If this person orders this book , then I will automatically offer him or her these books, based on choices by other people who ordered the same book ." A more sophisticated agent will keep your personal history of books ordered and suggest new publications that fall within your recorded fields of interest when they become available, also a current feature on Amazon.com. It's only a matter of time, agree Greis and other academics and consultants, before intelligent agents get put to work in the logistics and warehousing industries.
How would they be useful?
First of all, intelligent agents cut out the delays associated with waiting for a human reaction to a glitch in cargo movement. Telecommunications companies such as British Telecom in the U.K. use intelligent software to automatically route calls through the cheapest and most readily available lines. The same could be done with trucks navigating congested roads, or packages moving through a distribution center. Another application that surfaced in the crazy days of the transport dot-com boom was the automated negotiation of spot-market transportation buying. This typically involves fast-paced juggling of rates and availability measured against the performance records of known and unknown carriers. Software that compares apples to apples in the blink of an eye, then accepts or rejects bids could be highly useful. It didn't catch on in a public online auction scenario, but it could work in a private one.
However, Karonis of Kurt Salmon says it's when you combine intelligent software with other technologies—particularly data-collection devices —that things really get exciting. That's because software that makes decisions in real time needs better and more accurate data than is commonly available along the supply chain.
"RFID means more accurate and timely data, but if I don't have a decision engine to do something with that data and I'm just forcing it into the old processes, I'm not going to be able to do anything useful with that data," says Karonis. "By the same token I could deploy intelligent agents to make more intelligent and timely decisions, but if I'm using old data, the value of those decisions is going to be questionable. It's when you put them together you have more accurate, timely data leading to more accurate, timely decisions and that's where the real benefit lies."
Stealth pilots
Combining quick logistics management decisions with real-time data is the way forward for warehousing and supply chain expertise, says Greg Schlegel, former president of APICS -The Educational Society for Resource Management and a senior manager in IBM's ERP/Supply Chain Management Group. Schlegel predict s wide spread deployment of intelligent software to help that happen. "You're getting into neural networks where software can learn and make its own decisions and build learning trees about what to do and what not to do. From there, you get into predictive analysis, the ability to [resolve] problems before they arise. That's the kind of application that logistics and transportation managers are going to deploy."
So far, most of the work on getting logistics software to act intelligently is being done on university campuses. The Massachusetts Institute of Technology in Cambridge, the Robotics Institute at Carnegie Mellon University in Pittsburgh, The Center for Logistics and Digital Strategy at the University of North Carolina-Chapel Hill, and the Department of Computer Science and Engineering at the University of Minnesota have all been working on intelligent logistics software in one form or another. In fact, they all have pilot projects under way in the commercial world, but most of the test subjects prefer to remain silent on early adoption. "They're not normally discussing it because they consider it a competitive advantage to be more cost-effective and efficient," says Schlegel. The truth is that adoption rates are low, so far. "There's probably more hype than actual adoption out there right now," says Dr. Steve Smith,a colleague of Dr. Greis's at UNC.
One of the barriers to adoption is agreeing on data exchange standards, says Karl Waldman, president of software vendor OAT Systems in Wa tertown, Mass. In conjunction with MIT's Auto-ID Center, OAT is working with Gillette to take information gathered via RFID tags in retail outlets and feeding that back into the company's warehouse management and replenishment systems. Up-to-the-minute stocking data isn't worth much if it's in a language the replenishment system can't understand. "Standards are a big problem," says Waldman. "CIOs are looking for something standardized so they don't have to integrate it all later." The Auto-ID Center is a joint industry/MIT initiative to help establish and promote those standards, and OAT has developed a data handling framework called Savant that can be integrated into existing systems to foster standardized data exchange.
But problems with the human element also provide a barrier, Waldman says. "A major [obstacle] is education. Everybody 's been using ERP (enterprise resource planning) and WMS (warehouse management systems) for a number of years, and those systems all represent inventory in a very simple way, so there's a lack of understanding about the types of visibility you can get with RFID and auto ID. We have to spend a lot of time educating people. When they understand there's a whole lot more stuff they can do, their eyes light up."
Bytes and pieces
Most companies are still learning how to use logistics management software that falls below the definition of intelligent. Exception alerts are a good example. These will monitor the flow of goods through a warehouse or supply chain and send out automatic alerts when something goes wrong, prompting a management decision from a human being. For example, Optum is helping Lucent Technologies coordinate complex production and delivery functions. "Their whole goal is to get around 80 suppliers for any given order to ship so that the order all comes together in a three-day window for delivery to a job site," explains John Davies, cofounder and vice president of product marketing at Optum, based in White Plains, N.Y. "If one of the key suppliers producing a critical component can't ship it on time, they provide a message to us and we will automatically route messages to all the other suppliers that the date is going to have to be pushed back."
At a high level of automation,this would constitute intelligent software. But, in this case, the software isn't allowed to decide on a new delivery date without consulting a human manager. "We'll send out a new date but we want someone to say: 'Yes, that's the right date,'" Davies says. He says programming intelligent agents to make reliably good decisions according to the myriad possible situations that may occur in a complex supply chain is currently too much effort for too little return."There are too many variables; it's too hard to write the rules," Davies says. "Humans are still good to have involved in the supply chain."
Davies and others agree that there's reluctance among logistics managers to hand over responsibility for crucial decisions to the machines. Optum's software does help automate some order fulfillment decisions for InvaCare, a maker of medical equipment,making last-minute decisions about how to fill orders based on real-time information about what's rolling off the production line and how demand has changed. "But that's a point solution. It's not like two agents getting together and negotiating and going off automatically," Davies says. "InvaCare wouldn't want those agents to expand into ordering supplier materials on the basis of that information."
Point solutions—or fitting an intelligent agent to a single business function such as cross docking—represent an ideal way to start with intelligent software, says Greis. "These are bottom-up technologies. You identify a problem and then develop an application to support it," she says."It's not like installing a huge SAP system. It's more about pulling out a particular part of the operation and having the agents work on it." Greis says this can be cheap compared to putting in a huge mainframe system. "The applications that we've done are designed to be overlays on existing systems and as inexpensive as you need to have them be," she reports.
IBM's Schlegel says logistics is simply taking time to catch up with other industries that are already exploring the benefits of intelligent software. "Artificial intelligence is being [used] in a big way in banks and financial institutions. They were the first to use neural networks and network systems," Schlegel says. He says banks have a lot to gain from automating computer operations and taking out "touch points" where a human has to enter information, since their business is mostly about data processing and protocols. After the financial services industry, manufacturing became the second group to adopt intelligent software. "They're star ting to embrace the use of message alerts for their supply chains internally," says Schlegel. "Now, the third industry is logistics. They're not embracing it yet, but they're talking about how to leverage it."
"Any time you have complexity in a business process, you can use agents to support a human's decision-making capability," says Greis. "Whether it's logistics or warehousing, it's about figuring out what decisions people have to make and asking whether an agent can make that decision better, faster or in a more cost-effective way."
Economic activity in the logistics industry expanded in January, growing at its fastest clip in more than two years, according to the latest Logistics Managers’ Index (LMI) report, released this week.
The LMI jumped nearly five points from December to a reading of 62, reflecting continued steady growth in the U.S. economy along with faster-than-expected inventory growth across the sector as retailers, wholesalers, and manufacturers attempted to manage the uncertainty of tariffs and a changing regulatory environment. The January reading represented the fastest rate of expansion since June 2022, the LMI researchers said.
An LMI reading above 50 indicates growth across warehousing and transportation markets, and a reading below 50 indicates contraction. The LMI has remained in the mid- to high 50s range for most of the past year, indicating moderate, consistent growth in logistics markets.
Inventory levels rose 8.5 points from December, driven by downstream retailers stocking up ahead of the Trump administration’s potential tariffs on imports from Mexico, Canada, and China. Those increases led to higher costs throughout the industry: inventory costs, warehousing prices, and transportation prices all expanded to readings above 70, indicating strong growth. This occurred alongside slowing growth in warehousing and transportation capacity, suggesting that prices are up due to demand rather than other factors, such as inflation, according to the LMI researchers.
The LMI is a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
As commodities go, furniture presents its share of manufacturing and distribution challenges. For one thing, it's bulky. Second, its main components—wood and cloth—are easily damaged in transit. Third, much of it is manufactured overseas, making for some very long supply chains with all the associated risks. And finally, completed pieces can sit on the showroom floor for weeks or months, tying up inventory dollars and valuable retail space.
In other words, the furniture market is ripe for disruption. And John "Jay" Rogers wants to be the catalyst. In 2022, he cofounded a company that takes a whole new approach to furniture manufacturing—one that leverages the power of 3D printing and robotics. Rogers serves as CEO of that company, Haddy, which essentially aims to transform how furniture—and all elements of the "built environment"—are designed, manufactured, distributed, and, ultimately, recycled.
Rogers graduated from Princeton University and went to work for a medical device startup in China before moving to a hedge fund company, where he became a Chartered Financial Analyst (CFA). After that, he joined the U.S. Marine Corps, serving eight years in the infantry. Following two combat tours, he earned an MBA from the Harvard Business School and became a consultant for McKinsey & Co.
During this time, he founded Local Motors, a next-generation vehicle manufacturer that launched the world's first 3D-printed car, the Strati, in 2014. In 2021, he brought the technology to the furniture industry to launch Haddy. The father of four boys, Rogers is also a director of the RBR Foundation, a philanthropic organization focused on education and health care.
Rogers spoke recently with DC Velocity Group Editorial Director David Maloney on an episode of the "Logistics Matters" podcast.
Q: Could you tell us about Haddy and how this unique company came to be?
A: Absolutely. We have believed in the future of distributed digital manufacturing for a long time. The world has gone from being heavily globalized to one where lengthy supply chains are a liability—thanks to factors like the growing risk of terrorist attacks and the threat of tariffs. At the same time, there are more capabilities to produce things locally. Haddy is an outgrowth of those general trends.
Adoption of the technologies used in 3D printing has been decidedly uneven, although we do hear about applications like tissue bioprinting and food printing as well as the printing of trays for dental aligners. At Haddy, we saw an opportunity to take advantage of large-scale structural printing to approach the furniture and furnishings industry. The technology and software that make this possible are already here.
Q: Furniture is a very mature market. Why did you see this as a market that was ripe for disruption?
A:The furniture market has actually been disrupted many times in the last 200 years. The manufacturing of furniture for U.S. consumption originally took place in England. It then moved to Boston and from there to New Amsterdam, the Midwest, and North Carolina. Eventually, it went to Taiwan, then China, and now Vietnam, Indonesia, and Thailand. And each of those moves brought some type of disruption.
Other disruptions have been based on design. You can look at things like the advent of glue-laminated wood with Herman Miller, MillerKnoll, and the Eames [furniture design and manufacturing] movement. And you can look at changes in the way manufacturing is powered—the move from manual operations to machine-driven operations powered by steam and electricity. So the furniture industry has been continuously disrupted, sometimes by labor markets and sometimes by machines and methods.
What's happening now is that we're seeing changes in the way that labor is applied in furniture manufacturing. Furniture has traditionally been put together by human hands. But today, we have an opportunity to reassign those hands to processes that take place around the edges of furniture production. The hands are now directing robotics through programming and design; they're not actually making the furniture.
And so, we see this mature market as being one that's been continuously disrupted during the last 200 years. And this disruption now has a lot to do with changing the way that labor interacts with the making of furniture.
Q: How do your 3D printers actually create the furniture?
A:All 3D printing is not the same. The 3D printers we use are so-called "hybrid" systems. When we say hybrid, what we mean is that they're not just printers—they are holders, printers, polishers, and cutters, and they also do milling and things like that. We measure things and then print things, which is the additive portion. Then we can do subtractive and polishing work—re-measuring, moving, and printing parts again. And so, these hybrid systems are the actual makers of the furniture.
Q: What types of products are you making?
A: We've started with hardline or case goods, as they're sometimes known, for both residential and commercial use—cabinets, wall bookshelves, freestanding bookshelves, tables, rigid chairs, planters, and the like. Basically, we've been concentrating on products that don't have upholstery.
It's not that upholstery isn't necessary in furniture, as it is used in many pieces. But right now, we have found that digital furniture manufacturing becomes analog again when you have to factor in the sewing process. And so, to move quickly and fully leverage the advantages of digital manufacturing, we're sticking to the hardline groups, except for a couple of pieces that we have debuted that have 3D-printed cushions, which are super cool.
Q: Of course, 3D printers create objects in layers. What types of materials are you running through your 3D printers to create this furniture?
A: We use recycled materials, primarily polymer composites—a bio-compostable polymer or a synthetic polymer. We look for either recycled or bio-compostable [materials], which we then reinforce with fibers and fillers, and that's what makes them composites. To create the bio-compostables, we marry them with bio-fibers, such as hemp or bamboo. For synthetic materials, we marry them with things like glass or carbon fibers.
Q: Does producing goods via 3D printing allow you to customize products easily?
A: Absolutely. The real problem in the furniture and furnishings industries is that when you tool up to make something with a jig, a fixture, or a mold, you tend to be less creative because you now feel you have to make and sell a lot of that item to justify the investment.
One of the great promises of 3D printing is that it doesn't have a mold and doesn't require tooling. It exists in the digital realm before it becomes physical, and so customization is part and parcel of the process.
I would also add that people aren't necessarily looking for one-off furniture. Just because we can customize doesn't mean we're telling customers that once we've delivered a product, we break the digital mold, so to speak. We still feel that people like styles and trends created by designers, but the customization really allows enterprise clients—like businesses, retailers, and architects—to think more freely.
Customization is most useful in allowing people to "iterate" quickly. Our designers can do something digitally first without having to build a tool, which frees them to be more creative. Plus, because our material is fully recyclable, if we print something for the first time and find it doesn't work, we can just recycle it. So there's really no penalty for a failed first printing—in fact, those failures bring their own rewards in the form of lessons we can apply in future digital and physical iterations.
Q: You currently produce your furniture in an automated microfactory in Florida, with plans to set up several more. Could you talk a little about what your microfactory looks like and how you distribute the finished goods?
A: Our microfactory is a 30,000-square-foot box that mainly contains the robots that make our furniture along with shipping docks. But we don't intend for our microfactories to be storage warehouses and trans-shipment facilities like the kind you'd typically see in the furniture industry—all of the trappings of a global supply chain. Instead, a microfactory is meant to be a site where you print the product, put it on a dock, and then ship it out. So a microfactory is essentially an enabler of regional manufacturing and distribution.
Q: Do you manufacture your products on a print-to-order basis as opposed to a print-to-stock model?
A: No. We may someday get to the point where we receive an order digitally, print it, and then send it out on a truck the next day. But right now, we aren't set up to do a mini-delivery to one customer out of a microfactory.
We are an enterprise company that partners with architects, designers, builders, and retailers, who then distribute our furnishings to their customers. We are not trying to go direct-to-consumer at this stage. It's not the way a microfactory is set up to distribute goods.
Q: You've mentioned your company's use of recycled materials. Could you talk a little bit about other ways you're looking to reduce waste and help support a circular economy?
A: Yes. Sustainability and a circular economy are really something that you have to plan for. In our case, our plans call for moving toward a distributed digital manufacturing model, where we establish microfactories in various regions around the world to serve customers within a 10-hour driving radius of the factory. That is a pretty large area, so we could cover the United States with just four or five microfactories.
That also means that we can credibly build our recycling network as part of our microfactory setup. As I mentioned, we use recycled polymer stock in our production, so we're keeping that material out of a landfill. And then we tell our enterprise customers that while the furniture they're buying is extremely durable, when they're ready to run a special and offer customers a credit for turning in their used furniture, we'll buy back the material. Buying back that material actually reduces our costs because it's already been composited and created and recaptured. So our microfactory network is well designed for circularity in concert with our enterprise customers.
Generative AI (GenAI) is being deployed by 72% of supply chain organizations, but most are experiencing just middling results for productivity and ROI, according to a survey by Gartner, Inc.
That’s because productivity gains from the use of GenAI for individual, desk-based workers are not translating to greater team-level productivity. Additionally, the deployment of GenAI tools is increasing anxiety among many employees, providing a dampening effect on their productivity, Gartner found.
To solve those problems, chief supply chain officers (CSCOs) deploying GenAI need to shift from a sole focus on efficiency to a strategy that incorporates full organizational productivity. This strategy must better incorporate frontline workers, assuage growing employee anxieties from the use of GenAI tools, and focus on use-cases that promote creativity and innovation, rather than only on saving time.
"Early GenAI deployments within supply chain reveal a productivity paradox," Sam Berndt, Senior Director in Gartner’s Supply Chain practice, said in the report. "While its use has enhanced individual productivity for desk-based roles, these gains are not cascading through the rest of the function and are actually making the overall working environment worse for many employees. CSCOs need to retool their deployment strategies to address these negative outcomes.”
As part of the research, Gartner surveyed 265 global respondents in August 2024 to assess the impact of GenAI in supply chain organizations. In addition to the survey, Gartner conducted 75 qualitative interviews with supply chain leaders to gain deeper insights into the deployment and impact of GenAI on productivity, ROI, and employee experience, focusing on both desk-based and frontline workers.
Gartner’s data showed an increase in productivity from GenAI for desk-based workers, with GenAI tools saving 4.11 hours of time weekly for these employees. The time saved also correlated to increased output and higher quality work. However, these gains decreased when assessing team-level productivity. The amount of time saved declined to 1.5 hours per team member weekly, and there was no correlation to either improved output or higher quality of work.
Additional negative organizational impacts of GenAI deployments include:
Frontline workers have failed to make similar productivity gains as their desk-based counterparts, despite recording a similar amount of time savings from the use of GenAI tools.
Employees report higher levels of anxiety as they are exposed to a growing number of GenAI tools at work, with the average supply chain employee now utilizing 3.6 GenAI tools on average.
Higher anxiety among employees correlates to lower levels of overall productivity.
“In their pursuit of efficiency and time savings, CSCOs may be inadvertently creating a productivity ‘doom loop,’ whereby they continuously pilot new GenAI tools, increasing employee anxiety, which leads to lower levels of productivity,” said Berndt. “Rather than introducing even more GenAI tools into the work environment, CSCOs need to reexamine their overall strategy.”
According to Gartner, three ways to better boost organizational productivity through GenAI are: find creativity-based GenAI use cases to unlock benefits beyond mere time savings; train employees how to make use of the time they are saving from the use GenAI tools; and shift the focus from measuring automation to measuring innovation.
According to Arvato, it made the move in order to better serve the U.S. e-commerce sector, which has experienced high growth rates in recent years and is expected to grow year-on-year by 5% within the next five years.
The two acquisitions follow Arvato’s purchase three months ago of ATC Computer Transport & Logistics, an Irish firm that specializes in high-security transport and technical services in the data center industry. Following the latest deals, Arvato will have a total U.S. network of 16 warehouses with about seven million square feet of space.
Terms of the deal were not disclosed.
Carbel is a Florida-based 3PL with a strong focus on fashion and retail. It offers custom warehousing, distribution, storage, and transportation services, operating out of six facilities in the U.S., with a footprint of 1.6 million square feet of warehouse space in Florida (2), Pennsylvania (2), California, and New York.
Florida-based United Customs Services offers import and export solutions, specializing in remote location filing across the U.S., customs clearance, and trade compliance. CTPAT-certified since 2007, United Customs Services says it is known for simplifying global trade processes that help streamline operations for clients in international markets.
“With deep expertise in retail and apparel logistics services, Carbel and United Customs Services are the perfect partners to strengthen our ability to provide even more tailored solutions to our clients. Our combined knowledge and our joint commitment to excellence will drive our growth within the US and open new opportunities,” Arvato CEO Frank Schirrmeister said in a release.
And many of them will have a budget to do it, since 51% of supply chain professionals with existing innovation budgets saw an increase earmarked for 2025, suggesting an even greater emphasis on investing in new technologies to meet rising demand, Kenco said in its “2025 Supply Chain Innovation” survey.
One of the biggest targets for innovation spending will artificial intelligence, as supply chain leaders look to use AI to automate time-consuming tasks. The survey showed that 41% are making AI a key part of their innovation strategy, with a third already leveraging it for data visibility, 29% for quality control, and 26% for labor optimization.
Still, lingering concerns around how to effectively and securely implement AI are leading some companies to sidestep the technology altogether. More than a third – 35% – said they’re largely prevented from using AI because of company policy, leaving an opportunity to streamline operations on the table.
“Avoiding AI entirely is no longer an option. Implementing it strategically can give supply chain-focused companies a serious competitive advantage,” Kristi Montgomery, Vice President, Innovation, Research & Development at Kenco, said in a release. “Now’s the time for organizations to explore and experiment with the tech, especially for automating data-heavy operations such as demand planning, shipping, and receiving to optimize your operations and unlock true efficiency.”
Among the survey’s other top findings:
there was essentially three-way tie for which physical automation tools professionals are looking to adopt in the coming year: robotics (43%), sensors and automatic identification (40%), and 3D printing (40%).
professionals tend to select a proven developer for providing supply chain innovation, but many also pick start-ups. Forty-five percent said they work with a mix of new and established developers, compared to 39% who work with established technologies only.
there’s room to grow in partnering with 3PLs for innovation: only 13% said their 3PL identified a need for innovation, and just 8% partnered with a 3PL to bring a technology to life.