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.
The move delivers on its August announcement of a fleet renewal plan that will allow the company to proceed on its path to decarbonization, according to a statement from Anda Cristescu, Head of Chartering & Newbuilding at Maersk.
The first vessels will be delivered in 2028, and the last delivery will take place in 2030, enabling a total capacity to haul 300,000 twenty foot equivalent units (TEU) using lower emissions fuel. The new vessels will be built in sizes from 9,000 to 17,000 TEU each, allowing them to fill various roles and functions within the company’s future network.
In the meantime, the company will also proceed with its plan to charter a range of methanol and liquified gas dual-fuel vessels totaling 500,000 TEU capacity, replacing existing capacity. Maersk has now finalized these charter contracts across several tonnage providers, the company said.
The shipyards now contracted to build the vessels are: Yangzijiang Shipbuilding and New Times Shipbuilding—both in China—and Hanwha Ocean in South Korea.
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."
A growing number of organizations are identifying ways to use GenAI to streamline their operations and accelerate innovation, using that new automation and efficiency to cut costs, carry out tasks faster and more accurately, and foster the creation of new products and services for additional revenue streams. That was the conclusion from ISG’s “2024 ISG Provider Lens global Generative AI Services” report.
The most rapid development of enterprise GenAI projects today is happening on text-based applications, primarily due to relatively simple interfaces, rapid ROI, and broad usefulness. Companies have been especially aggressive in implementing chatbots powered by large language models (LLMs), which can provide personalized assistance, customer support, and automated communication on a massive scale, ISG said.
However, most organizations have yet to tap GenAI’s potential for applications based on images, audio, video and data, the report says. Multimodal GenAI is still evolving toward mainstream adoption, but use cases are rapidly emerging, and with ongoing advances in neural networks and deep learning, they are expected to become highly integrated and sophisticated soon.
Future GenAI projects will also be more customized, as the sector sees a major shift from fine-tuning of LLMs to smaller models that serve specific industries, such as healthcare, finance, and manufacturing, ISG says. Enterprises and service providers increasingly recognize that customized, domain-specific AI models offer significant advantages in terms of cost, scalability, and performance. Customized GenAI can also deliver on demands like the need for privacy and security, specialization of tasks, and integration of AI into existing operations.
Commercial fleet operators are steadily increasing their use of GPS fleet tracking, in-cab video solutions, and predictive analytics, driven by rising costs, evolving regulations, and competitive pressures, according to an industry report from Verizon Connect.
Those conclusions come from the company’s fifth annual “Fleet Technology Trends Report,” conducted in partnership with Bobit Business Media, and based on responses from 543 fleet management professionals.
The study showed that for five consecutive years, at least four out of five respondents have reported using at least one form of fleet technology, said Atlanta-based Verizon Connect, which provides fleet and mobile workforce management software platforms, embedded OEM hardware, and a connected vehicle device called Hum by Verizon.
The most commonly used of those technologies is GPS fleet tracking, with 69% of fleets across industries reporting its use, the survey showed. Of those users, 72% find it extremely or very beneficial, citing improved efficiency (62%) and a reduction in harsh driving/speeding events (49%).
Respondents also reported a focus on safety, with 57% of respondents citing improved driver safety as a key benefit of GPS fleet tracking. And 68% of users said in-cab video solutions are extremely or very beneficial. Together, those technologies help reduce distracted driving incidents, improve coaching sessions, and help reduce accident and insurance costs, Verizon Connect said.
Looking at the future, fleet management software is evolving to meet emerging challenges, including sustainability and electrification, the company said. "The findings from this year's Fleet Technology Trends Report highlight a strong commitment across industries to embracing fleet technology, with GPS tracking and in-cab video solutions consistently delivering measurable results,” Peter Mitchell, General Manager, Verizon Connect, said in a release. “As fleets face rising costs and increased regulatory pressures, these technologies are proving to be indispensable in helping organizations optimize their operations, reduce expenses, and navigate the path toward a more sustainable future.”
Progress in generative AI (GenAI) is poised to impact business procurement processes through advancements in three areas—agentic reasoning, multimodality, and AI agents—according to Gartner Inc.
Those functions will redefine how procurement operates and significantly impact the agendas of chief procurement officers (CPOs). And 72% of procurement leaders are already prioritizing the integration of GenAI into their strategies, thus highlighting the recognition of its potential to drive significant improvements in efficiency and effectiveness, Gartner found in a survey conducted in July, 2024, with 258 global respondents.
Gartner defined the new functions as follows:
Agentic reasoning in GenAI allows for advanced decision-making processes that mimic human-like cognition. This capability will enable procurement functions to leverage GenAI to analyze complex scenarios and make informed decisions with greater accuracy and speed.
Multimodality refers to the ability of GenAI to process and integrate multiple forms of data, such as text, images, and audio. This will make GenAI more intuitively consumable to users and enhance procurement's ability to gather and analyze diverse information sources, leading to more comprehensive insights and better-informed strategies.
AI agents are autonomous systems that can perform tasks and make decisions on behalf of human operators. In procurement, these agents will automate procurement tasks and activities, freeing up human resources to focus on strategic initiatives, complex problem-solving and edge cases.
As CPOs look to maximize the value of GenAI in procurement, the study recommended three starting points: double down on data governance, develop and incorporate privacy standards into contracts, and increase procurement thresholds.
“These advancements will usher procurement into an era where the distance between ideas, insights, and actions will shorten rapidly,” Ryan Polk, senior director analyst in Gartner’s Supply Chain practice, said in a release. "Procurement leaders who build their foundation now through a focus on data quality, privacy and risk management have the potential to reap new levels of productivity and strategic value from the technology."