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."
Congestion on U.S. highways is costing the trucking industry big, according to research from the American Transportation Research Institute (ATRI), released today.
The group found that traffic congestion on U.S. highways added $108.8 billion in costs to the trucking industry in 2022, a record high. The information comes from ATRI’s Cost of Congestion study, which is part of the organization’s ongoing highway performance measurement research.
Total hours of congestion fell slightly compared to 2021 due to softening freight market conditions, but the cost of operating a truck increased at a much higher rate, according to the research. As a result, the overall cost of congestion increased by 15% year-over-year—a level equivalent to more than 430,000 commercial truck drivers sitting idle for one work year and an average cost of $7,588 for every registered combination truck.
The analysis also identified metropolitan delays and related impacts, showing that the top 10 most-congested states each experienced added costs of more than $8 billion. That list was led by Texas, at $9.17 billion in added costs; California, at $8.77 billion; and Florida, $8.44 billion. Rounding out the top 10 list were New York, Georgia, New Jersey, Illinois, Pennsylvania, Louisiana, and Tennessee. Combined, the top 10 states account for more than half of the trucking industry’s congestion costs nationwide—52%, according to the research.
The metro areas with the highest congestion costs include New York City, $6.68 billion; Miami, $3.2 billion; and Chicago, $3.14 billion.
ATRI’s analysis also found that the trucking industry wasted more than 6.4 billion gallons of diesel fuel in 2022 due to congestion, resulting in additional fuel costs of $32.1 billion.
ATRI used a combination of data sources, including its truck GPS database and Operational Costs study benchmarks, to calculate the impacts of trucking delays on major U.S. roadways.
There’s a photo from 1971 that John Kent, professor of supply chain management at the University of Arkansas, likes to show. It’s of a shaggy-haired 18-year-old named Glenn Cowan grinning at three-time world table tennis champion Zhuang Zedong, while holding a silk tapestry Zhuang had just given him. Cowan was a member of the U.S. table tennis team who participated in the 1971 World Table Tennis Championships in Nagoya, Japan. Story has it that one morning, he overslept and missed his bus to the tournament and had to hitch a ride with the Chinese national team and met and connected with Zhuang.
Cowan and Zhuang’s interaction led to an invitation for the U.S. team to visit China. At the time, the two countries were just beginning to emerge from a 20-year period of decidedly frosty relations, strict travel bans, and trade restrictions. The highly publicized trip signaled a willingness on both sides to renew relations and launched the term “pingpong diplomacy.”
Kent, who is a senior fellow at the George H. W. Bush Foundation for U.S.-China Relations, believes the photograph is a good reminder that some 50-odd years ago, the economies of the United States and China were not as tightly interwoven as they are today. At the time, the Nixon administration was looking to form closer political and economic ties between the two countries in hopes of reducing chances of future conflict (and to weaken alliances among Communist countries).
The signals coming out of Washington and Beijing are now, of course, much different than they were in the early 1970s. Instead of advocating for better relations, political rhetoric focuses on the need for the U.S. to “decouple” from China. Both Republicans and Democrats have warned that the U.S. economy is too dependent on goods manufactured in China. They see this dependency as a threat to economic strength, American jobs, supply chain resiliency, and national security.
Supply chain professionals, however, know that extricating ourselves from our reliance on Chinese manufacturing is easier said than done. Many pundits push for a “China + 1” strategy, where companies diversify their manufacturing and sourcing options beyond China. But in reality, that “plus one” is often a Chinese company operating in a different country or a non-Chinese manufacturer that is still heavily dependent on material or subcomponents made in China.
This is the problem when supply chain decisions are made on a global scale without input from supply chain professionals. In an article in the Arkansas Democrat-Gazette, Kent argues that, “The discussions on supply chains mainly take place between government officials who typically bring many other competing issues and agendas to the table. Corporate entities—the individuals and companies directly impacted by supply chains—tend to be under-represented in the conversation.”
Kent is a proponent of what he calls “supply chain diplomacy,” where experts from academia and industry from the U.S. and China work collaboratively to create better, more efficient global supply chains. Take, for example, the “Peace Beans” project that Kent is involved with. This project, jointly formed by Zhejiang University and the Bush China Foundation, proposes balancing supply chains by exporting soybeans from Arkansas to tofu producers in China’s Yunnan province, and, in return, importing coffee beans grown in Yunnan to coffee roasters in Arkansas. Kent believes the operation could even use the same transportation equipment.
The benefits of working collaboratively—instead of continuing to build friction in the supply chain through tariffs and adversarial relationships—are numerous, according to Kent and his colleagues. They believe it would be much better if the two major world economies worked together on issues like global inflation, climate change, and artificial intelligence.
And such relations could play a significant role in strengthening world peace, particularly in light of ongoing tensions over Taiwan. Because, as Kent writes, “The 19th-century idea that ‘When goods don’t cross borders, soldiers will’ is as true today as ever. Perhaps more so.”
Hyster-Yale Materials Handling today announced its plans to fulfill the domestic manufacturing requirements of the Build America, Buy America (BABA) Act for certain portions of its lineup of forklift trucks and container handling equipment.
That means the Greenville, North Carolina-based company now plans to expand its existing American manufacturing with a targeted set of high-capacity models, including electric options, that align with the needs of infrastructure projects subject to BABA requirements. The company’s plans include determining the optimal production location in the United States, strategically expanding sourcing agreements to meet local material requirements, and further developing electric power options for high-capacity equipment.
As a part of the 2021 Infrastructure Investment and Jobs Act, the BABA Act aims to increase the use of American-made materials in federally funded infrastructure projects across the U.S., Hyster-Yale says. It was enacted as part of a broader effort to boost domestic manufacturing and economic growth, and mandates that federal dollars allocated to infrastructure – such as roads, bridges, ports and public transit systems – must prioritize materials produced in the USA, including critical items like steel, iron and various construction materials.
Hyster-Yale’s footprint in the U.S. is spread across 10 locations, including three manufacturing facilities.
“Our leadership is fully invested in meeting the needs of businesses that require BABA-compliant material handling solutions,” Tony Salgado, Hyster-Yale’s chief operating officer, said in a release. “We are working to partner with our key domestic suppliers, as well as identifying how best to leverage our own American manufacturing footprint to deliver a competitive solution for our customers and stakeholders. But beyond mere compliance, and in line with the many areas of our business where we are evolving to better support our customers, our commitment remains steadfast. We are dedicated to delivering industry-leading standards in design, durability and performance — qualities that have become synonymous with our brands worldwide and that our customers have come to rely on and expect.”
In a separate move, the U.S. Environmental Protection Agency (EPA) also gave its approval for the state to advance its Heavy-Duty Omnibus Rule, which is crafted to significantly reduce smog-forming nitrogen oxide (NOx) emissions from new heavy-duty, diesel-powered trucks.
Both rules are intended to deliver health benefits to California citizens affected by vehicle pollution, according to the environmental group Earthjustice. If the state gets federal approval for the final steps to become law, the rules mean that cars on the road in California will largely be zero-emissions a generation from now in the 2050s, accounting for the average vehicle lifespan of vehicles with internal combustion engine (ICE) power sold before that 2035 date.
“This might read like checking a bureaucratic box, but EPA’s approval is a critical step forward in protecting our lungs from pollution and our wallets from the expenses of combustion fuels,” Paul Cort, director of Earthjustice’s Right To Zero campaign, said in a release. “The gradual shift in car sales to zero-emissions models will cut smog and household costs while growing California’s clean energy workforce. Cutting truck pollution will help clear our skies of smog. EPA should now approve the remaining authorization requests from California to allow the state to clean its air and protect its residents.”
However, the truck drivers' industry group Owner-Operator Independent Drivers Association (OOIDA) pushed back against the federal decision allowing the Omnibus Low-NOx rule to advance. "The Omnibus Low-NOx waiver for California calls into question the policymaking process under the Biden administration's EPA. Purposefully injecting uncertainty into a $588 billion American industry is bad for our economy and makes no meaningful progress towards purported environmental goals," (OOIDA) President Todd Spencer said in a release. "EPA's credibility outside of radical environmental circles would have been better served by working with regulated industries rather than ramming through last-minute special interest favors. We look forward to working with the Trump administration's EPA in good faith towards achievable environmental outcomes.”
Editor's note:This article was revised on December 18 to add reaction from OOIDA.
A Canadian startup that provides AI-powered logistics solutions has gained $5.5 million in seed funding to support its concept of creating a digital platform for global trade, according to Toronto-based Starboard.
The round was led by Eclipse, with participation from previous backers Garuda Ventures and Everywhere Ventures. The firm says it will use its new backing to expand its engineering team in Toronto and accelerate its AI-driven product development to simplify supply chain complexities.
According to Starboard, the logistics industry is under immense pressure to adapt to the growing complexity of global trade, which has hit recent hurdles such as the strike at U.S. east and gulf coast ports. That situation calls for innovative solutions to streamline operations and reduce costs for operators.
As a potential solution, Starboard offers its flagship product, which it defines as an AI-based transportation management system (TMS) and rate management system that helps mid-sized freight forwarders operate more efficiently and win more business. More broadly, Starboard says it is building the virtual infrastructure for global trade, allowing freight companies to leverage AI and machine learning to optimize operations such as processing shipments in real time, reconciling invoices, and following up on payments.
"This investment is a pivotal step in our mission to unlock the power of AI for our customers," said Sumeet Trehan, Co-Founder and CEO of Starboard. "Global trade has long been plagued by inefficiencies that drive up costs and reduce competitiveness. Our platform is designed to empower SMB freight forwarders—the backbone of more than $20 trillion in global trade and $1 trillion in logistics spend—with the tools they need to thrive in this complex ecosystem."