The trucking industry faces a range of challenges these days, particularly when it comes to load planning—a resource-intensive task that often results in suboptimal decisions, unnecessary empty miles, late deliveries, and inefficient asset utilization. What’s more, delays in decision-making due to a lack of real-time insights can hinder operational efficiency, making cost management a constant struggle.
Truckload carrier Paper Transport Inc. (PTI) experienced this firsthand when the company sought to expand its over the-road (OTR), intermodal, and brokerage offerings to include dedicated fleet services for high-volume shippers—adding a layer of complexity to the business. The additional personnel required for such a move would be extremely costly, leading PTI to investigate technology solutions that could help close the gap.
Enter Freight Science and its intelligent decision-recommendation and automation platform.
PTI implemented Freight Science’s artificial intelligence (AI)-driven load planning optimization solution earlier this year, giving the carrier a high-tech advantage as it launched the new service.
“As PTI tried to diversify … we found that we needed a technological solution that would allow us to process [information] faster,” explains Jared Stedl, chief commercial officer for PTI, emphasizing the high volume of outbound shipments and unique freight characteristics of its targeted dedicated-fleet customers.
The Freight Science platform allowed PTI to apply its signature high-quality service to those needs, all while handling the daily challenges of managing drivers and navigating route disruptions.
STREAMLINING PROCESSES
Dedicated fleets face challenges that evolve from day to day and minute to minute, including truck breakdowns, drivers calling in sick, and rescheduled appointment times. PTI needed a tool that allowed for a real-time view of the fleet, ultimately enabling its team to adjust truck and driver allocation to meet those challenges.
The Freight Science solution filled the bill. The platform uses advanced analytics and algorithms to give carriers better visibility into operations while automating the decision-making process. By combining streaming data, a carrier’s transportation management system (TMS), machine learning, and decision science, the solution allows carriers to deploy their fleets more efficiently while accurately forecasting future needs, according to Freight Science.
In PTI’s case, Freight Science’s software integrates with the carrier’s TMS, real-time electronic logging device (ELD) data, and other external data, feeding an AI model that generates an optimized load plan for the planner.
“We’re an integrated data analytics company for trucking companies,” explains Matt Foster, Freight Science’s president and CEO. “We’re talking about AI.”
The benefits of the real-time data are difficult to overstate.
“We’ve been able to execute in the toughest of situations because we’ve got real, live data on how long each event is actually going to take and a system to aid and even automate the decision-making process,” says Chad Borley, PTI’s operations manager. “From what traffic patterns we are battling in the morning and evening with rush hour and things like that, to the impact of additional miles to a route, or even location-specific dwell times, it’s been a huge differentiator for us.”
REALIZING RESULTS
A case in point: the collapse of Baltimore’s Francis Scott Key Bridge in March. PTI was scheduled to go live with a new dedicated account in the area just days after the collapse, which would mean rerouting and the potential for longer transit times. Instead of recalculating based on assumptions or latent data, PTI was able to reroute freight based on real-time information and analytics to give the customer timely updates.
“With the bridge going out, that changed our ability to make as many turns a day as the customer would expect,” Stedl explains. “But one of the things Freight Science could do [was to] quickly [assess] how much of an impact that traffic would have [and] what the turns [would] be based on what’s happening on the ground.
“So we were able to go back to the customer and readjust expectations in a real way that made sense, using data. Now expectations can be reset¾we’re not asking for forgiveness when there’s no reason for it.”
The system’s advanced algorithms make load planning more cost-effective and scalable as well. The platform allows PTI to monitor trucks, trailers, and driver hours in real time, recommending additional loads with remaining driver hours that would otherwise be wasted.
And they’re doing it all with much less. Stedl says tasks that used to require five people and hours of work can now be accomplished by one person in mere minutes, improving productivity and profitability while reducing labor and operational costs.
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."
Even worse, many managers are overconfident in their data. The majority (91%) of supply chain managers believe they are equipped to drive accurate supply chain visibility, but the reality is that only a third (33%) consistently obtain accurate, real-time inventory data.
And in turn, that gap also hinders supply chain managers’ ability to address challenges such as counterfeit goods, shrink and theft, misload and delivery errors, meeting sustainability requirements, and effectively implementing AI within their organization’s supply chain. Those results came from Seattle-based Impinj’s “Supply Chain Integrity Outlook 2025” report, which was based on a survey of 1,000 US supply chain managers.
“Supply chain managers continue to face data blind spots that prevent them from ensuring secure, reliable, and adaptable supply chains,” Impinj Chief Revenue Officer Jeff Dossett said in a release. “It’s essential that organizations address the data accuracy gap by putting technology in place to surface accurate data that fuels the real-time, actionable insights and visibility needed to ensure supply chain resilience.”
In additional findings, the study showed that over half (52%) of supply chain managers face challenges responding to rapid peaks in customer demand driven by social media- and influencer-driven trends. Nearly half (47%) of supply chain managers also report that changes in customer demand due to growth in social media storefronts (49%) and the rise of the thrift movement (47%) are among the top challenges for their organization’s supply chain.
The survey also identified the most significant supply chain integrity challenges and priorities for several sectors:
in retail: 65% of supply chain managers agree it’s a challenge for their organization to reduce the amount of counterfeit goods entering the supply chain
also in retail: 60% of retail supply chain managers surveyed also agree that reducing rates of shrink and theft is a challenge for their organization, and 99% are investing in measures to mitigate these concerns
in the food, grocery, and restaurant sector, 82% of supply chain managers report challenges reducing shrink, which is primarily due to shoplifting (45%), food spoilage (37%), and food waste (35%)
in transportation and logistics, 74% of surveyed supply chain managers are concerned about growing volumes of Load Planning Problems (LPPs), misloads, and delivery errors
When it comes to logistics technology, the pace of innovation has never been faster. In recent years, the market has been inundated by waves of cool new tech tools, all promising to help users enhance their operations and cope with today’s myriad supply chain challenges.
But that ever-expanding array of offerings can make it difficult to separate the wheat from the chaff—technology that’s the real deal versus technology that’s just “vaporware,” meaning products that don’t live up to their hype and may even still be in the conceptual stage.
One way to cut through the confusion is to check out the entries for the “3 V’s of Supply Chain Innovation Awards,” an annual competition held by the Council of Supply Chain Management Professionals (CSCMP). This competition, which is hosted by DC Velocity’s sister publication, Supply Chain Xchange, and supply chain visionary and 3 V’s framework creator Art Mesher, recognizes companies that have parlayed the 3 V’s—“embracing variability, harnessing visibility, and competing with velocity”—into business success and advanced the practice of supply chain management. Awards are presented in two categories: the “Business Innovation Award,” which recognizes more established businesses, and the “Best Overall Innovative Startup/Early Stage Award,” which recognizes newer companies.
The judging for this year’s competition—the second annual contest—took place at CSCMP’s EDGE Supply Chain Conference & Exhibition in September, where the three finalists for each award presented their innovations via a fast-paced “elevator pitch.” (To watch a video of the presentations, visit the Supply Chain Xchange website.)
What follows is a brief look at the six companies that made the competition’s final round and the latest updates on their achievements:
Arkestro: This San Francisco-based firm offers a predictive procurement orchestration solution that uses machine learning (ML) and behavioral science to revolutionize sourcing, eliminating the need for outdated manual tools like pivot tables and for labor-intensive negotiations. Instead, procurement teams can process quotes and secure optimal supplier agreements at a speed and accuracy that would be impossible to achieve manually, the firm says.
The company recently joined the Amazon Web Services (AWS) Partner Network (APN), which it says will help it reach its goal of elevating procurement from a cost center to a strategic growth engine.
AutoScheduler.AI: This Austin, Texas-based company offers a predictive warehouse optimization platform that integrates with a user’s existing warehouse management system (WMS) and “accelerates” its ability to resolve problems like dock schedule conflicts, inefficient workforce allocation, poor on-time/in-full (OTIF) performance, and excessive intra-campus moves.
“We’re here to make the warehouse sexy,” the firm says on its website. “With our deep background in building machine learning solutions, everything delivered by the AutoScheduler team is designed to provide value by learning your challenges, environment, and best practices.” Privately funded up until this summer, the company recently secured venture capital funding that it will use to accelerate its growth and enhance its technologies.
Davinci Micro Fulfillment: Located in Bound Brook, New Jersey, Davinci operates a “microfulfillment as a service” platform that helps users expedite inventory turnover while reducing operating expenses by leveraging what it calls the “4 Ps of global distribution”—product, placement, price, and promotion. The firm operates a network of microfulfillment centers across the U.S., offering services that include front-end merchandising and network optimization.
Within the past year, the company raised seed funding to help enhance its technology capabilities.
Flying Ship: Headquartered in Leesburg, Virginia, Flying Ship has designed an unmanned, low-flying “ground-effect maritime craft” that moves freight over the ocean in coastal regions. Although the Flying Ship looks like a small aircraft or large drone, it is classified as a maritime vessel because it does not leave the air cushion over the waves, similar to a hovercraft.
The first-generation models are 30 feet long, electrically powered, and semi-autonomous. They can dock at existing marinas, beaches, and boat ramps to deliver goods, providing service that the company describes as faster than boats and cheaper than air. The firm says the next-generation models will be fully autonomous.
Flying Ship, which was honored with the Best Overall Startup Award in this year’s 3 V’s competition, is currently preparing to fly demo missions with the Air Force Research Laboratory (AFRL).
Perfect Planner: Based in Alpharetta, Georgia, Perfect Planner operates a cloud-based platform that’s designed to streamline the material planning and replenishment process. The technology collects, organizes, and analyzes data from a business’s material requirements planning (MRP) system to create daily “to-do lists” for material planners/buyers, with the “to-dos” ranked in order of criticality. The solution also uses advanced analytics to “understand” and address inventory shortages and surpluses.
Perfect Planner was honored with the Business Innovation Award in this year’s 3 V’s competition.
ProvisionAi: Located in Franklin, Tennessee, ProvisionAi has developed load optimization software that helps consumer packaged goods (CPG) companies move their freight with fewer trucks, thereby cutting their transportation costs. The firm says its flagship offering is an automatic order optimization (AutoO2) system that bolts onto a company’s existing enterprise resource planning (ERP) or WMS platform and guides larger orders through execution, ensuring that what is planned is actually loaded on the truck. The firm’s CEO and founder, Tom Moore, was recognized as a 2024 Rainmaker by this magazine.
Many AI deployments are getting stuck in the planning stages due to a lack of AI skills, governance issues, and insufficient resources, leading 61% of global businesses to scale back their AI investments, according to a study from the analytics and AI provider Qlik.
Philadelphia-based Qlik found a disconnect in the market where 88% of senior decision makers say they feel AI is absolutely essential or very important to achieving success. Despite that support, multiple factors are slowing down or totally blocking those AI projects: a lack of skills to develop AI [23%] or to roll out AI once it’s developed [22%], data governance challenges [23%], budget constraints [21%], and a lack of trusted data for AI to work with [21%].
The numbers come from a survey of 4,200 C-Suite executives and AI decision makers, revealing what is hindering AI progress globally and how to overcome these barriers.
Respondents also said that many stakeholders lack trust in AI technology generally, which holds those projects back. Over a third [37%] of AI decision makers say their senior managers lack trust in AI, 42% feel less senior employees don’t trust the technology., and a fifth [21%] believe their customers don’t trust AI either.
“Business leaders know the value of AI, but they face a multitude of barriers that prevent them from moving from proof of concept to value creating deployment of the technology,” James Fisher, Chief Strategy Officer at Qlik, said in a release. “The first step to creating an AI strategy is to identify a clear use case, with defined goals and measures of success, and use this to identify the skills, resources and data needed to support it at scale. In doing so you start to build trust and win management buy-in to help you succeed.”
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The new "Amazon Nova" AI tools can use basic prompts--like "a dinosaur sitting in a teacup"--to create outputs in text, images, or video.
Benefits for Amazon's customers--who include marketplace retailers and logistics services customers, as well as companies who use its Amazon Web Services (AWS) platform and the e-commerce shoppers who buy goods on the website--will include generative AI (Gen AI) solutions that offer real-world value, the company said.
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.