When weighing the cost and service benefits of a dedicated truck fleet, consider alternative designs. In many cases, a design with the potential to bring drivers back home on a regular basis will be the best option.
Although many companies use dedicated truck fleets to transport their goods, few give adequate thought to which type of configuration they choose for those fleets. But they should consider the options carefully, because this decision can have a significant impact on cost and service.
The term "dedicated fleet," also known as "dedicated contract carriage," refers to tractors, trailers, drivers, and other resources exclusively devoted to serving a set of facilities or lanes in a transportation network. They usually are owned or leased by a motor carrier or logistics service provider that is hired by the shipper to manage its fleet operations. Traditional alternatives to a dedicated fleet include operating a private fleet, hiring common carriers, or contracting with third-party logistics providers for transportation services.
Article Figures
[Figure 1] Suitability of dedicated fleet with one-way flowsEnlarge this image
[Figure 2] Suitability of dedicated fleet with bi-directional flowsEnlarge this image
For some shippers, dedicated contract carriage offers significant benefits. For one thing, dedicated transportation is an effective way to guarantee capacity. For another, experience has shown that it can reduce transportation costs and has the potential to improve on-time delivery performance by 5 to 10 percent. Moreover, shippers with dedicated contract carriage arrangements can more easily negotiate fuel surcharges and reduce their regulatory liability than can shippers with private fleets. Finally, dedicated fleets allow shippers to focus their personnel and financial resources on their core business operations, such as manufacturing, rather than on transportation management.
Dedicated fleets may be broadly classified into two categories: network-based or depot-based. A network-based dedicated fleet balances freight flows between the various nodes across the entire transportation network. In a depot-based fleet, freight moves revolve around the key truck terminals and destinations. Each has its advantages and disadvantages, and shippers should analyze each of the scenarios in-depth to understand the uncertainties and operational issues before identifying a design for implementation. This article, however, will make the case that depot-based dedicated fleets are the better choice for many shippers because they offer the potential for better service and lower costs.
Suitability of dedicated fleet
Before we look at which type of dedicated fleet works best under which circumstances, it is important to note that a dedicated fleet is not suitable for all circumstances. Figure 1 and 2 illustrate how suitable a dedicated fleet would be for specific types of distribution networks. These assessments are based on research coupled with observations of real dedicated networks. As shown in Figure 1, the cost and service benefits of dedicated fleets in networks with one-way flows are limited. If the service requirements are low, available carrier capacity is high, and average length of haul is high (indicating long-haul freight), there are no benefits to implementing a dedicated fleet other than guaranteeing capacity in the supply chain. In Figure 2, it is clear that dedicated fleets have more to offer when the freight flows are bi-directional. (Note: Both figures assume no seasonality of freight flows in the network. If there are significant seasonal freight volumes, then there may be benefits to contracting short-term dedicated capacity.)
The examples used for Figures 1 and 2 are small and simple enough that it is possible to manually identify dedicated fleet opportunities. For large transportation networks with significant flows, however, it is difficult to identify those opportunities without the help of an analytical model. Such a model attempts to maximize the cost savings resulting from the implementation of a dedicated fleet.
When analyzing the cost impact of a dedicated transportation program, it is helpful for shippers to have historical information about the rates offered by common carriers for various lanes in their networks. They also will need comparative information about the cost of a dedicated fleet program, which may not be easily accessible. When that is the case, shippers may consider using hypothetical costs per mile for loads (say, US $1.25, $1.50, and $1.75) and for moving empty equipment (say, $0.75, $0.85, and $0.95) for the purpose of assessing the opportunity.
The net savings from implementing a dedicated fleet program can be determined based on the following equation (evaluation model):
Net savings = cost of using common carriers - cost of dedicated moves - cost of empty moves
This model identifies lanes that are suitable for dedicated contract carriage and those that are suitable for one-way moves. But the analysis should not stop there; after identifying the opportunities for dedicated moves, it is important to evaluate the implications for the remaining lanes, which will be served by other means. Lanes recommended for delivery by common carriers, for example, might experience a tariff (rate) increase due to a reduction in freight volumes. If those lanes experience significant rate hikes, then it may be justifiable to assign them to dedicated carriers instead. Any such changeable situation requires periodic re-evaluation.
Network-based vs. depot-based
Having ascertained that it should use a dedicated fleet, a company should next look at whether it should adopt a network-based model or a depot-based model. Figure 3 summarizes the main characteristics of these two types of dedicated fleets.
A network-based dedicated fleet balances freight flows among its nodes, and thus requires continual movement from one node to another in the network. Ensuring that the sum of inbound flows (the number of truckloads) to any node is equivalent to the sum of outbound flows from the same node makes it possible to execute transportation activities with a high rate of loaded miles. This is illustrated in Figure 4, in which red arrows indicate empty moves in the network. The numbers associated with each lane represent the number of annual loads and the length of haul for that lane, respectively. In order to achieve such a balanced flow in the network, it may be necessary to move tractors, trailers, and drivers without loads.
A major limitation of the network-based dedicated fleet is that drivers are not always able to return to their originating depots on a regular basis. For example, a driver originating from Node A in Figure 4 could follow a number of different routings, such as ABCA, ABDA, ABDEBCA, or even ABDEBCDA. The actual time required to get back to Node A would, of course, depend on the specific lanes assigned to the driver and the length of haul associated with them.
In a large network with destinations scattered across the country, drivers are likely to remain away from their originating depots for long periods. Managers can create schedules that periodically bring drivers home, but enforcing such considerations in this type of network design could negatively affect the dedicated fleet's performance.
Depot-based dedicated solutions, on the other hand, are organized according to the inbound and outbound flows around individual depots in the network. Figure 5 shows an example of a depot-based dedicated network, with Node A being the depot. Nodes C and E receive shipments from Depot A. Nodes B, D, and F ship to Depot A. Depot A is shipping to Node C 400 loads annually with a length of haul of 600 miles on lane AC. Nodes B and D are shipping 300 and 200 loads, respectively, to Depot A. By moving the empty equipment from Node C to Nodes B and D, the backhaul transportation to Node A could be executed in a cost-effective manner. Note that the resource requirement at Node D may be satisfied by moving empty equipment over short distances from Nodes C and E.
Depot-based dedicated fleet programs are suitable for large depots that either have significant inbound and outbound activity or make many local deliveries. In addition, networks with a large number of facilities, including a combination of intra-company moves (that is, between the company's own plants and distribution centers) and outbound customer shipments, could benefit from a depot-based dedicated program.
One of the biggest advantages of a depot-based network is the ability to bring drivers and equipment home on a regular basis. This has a strong, positive impact on the drivers' quality of life and job satisfaction, which understandably translates to increased driver retention and better service.
Case study example
The following case study shows how a company can first analyze whether a dedicated fleet would be suitable for its distribution network and then which type of dedicated fleet to use. This analysis employs data for 108 sites plus 184 lanes with three major depots. More than 50,000 loads were hauled annually, with an average length of haul of 1,100 miles.
Figure 6 shows the correlation between the number of "working units" (each comprising a tractor, a trailer, and a driver) and cost savings for a networkbased dedicated fleet. Figure 7 shows the estimated savings associated with various load ratios. "Load ratio" is defined as the ratio of loaded miles to total miles in the network; "total miles" includes both loaded and empty miles. With a high load ratio, it is possible to justify a dedicated fleet implementation for a small portion of the network. With a smaller load ratio, there will be a greater number of loads but also more empty miles, which is unproductive for the dedicated system.
As indicated in Figure 6, the estimated savings resulting from dedicated operations increases up to a certain point and subsequently diminishes. Figure 7 illustrates a similar relationship between cost savings and load ratio. In other words, there is an "optimum point" at which a dedicated system will maximize the net savings. The planning problem associated with dedicated fleet analysis and design, then, is to determine both the optimal number of working units and the optimal load ratio.
Figure 8 summarizes the results of the shipper's analysis of a network-based dedicated fleet with a load ratio of 99 percent, and Figure 9 does the same for a depot-based solution with a load ratio of 99 percent. (The load ratio was chosen simply for the purpose of discussion.) These analyses assume that the cost per mile for hiring common carriers is US $1.20; the cost per mile for moving a load in a dedicated system is $1; and the cost per mile for moving empty equipment is $0.85. Actual charges vary greatly depending on a variety of factors, but the degree of difference between the three values is typical.
In Figure 9, applying these assumptions for Depot A results in a total savings of US $1,154,000 (= 6,043,000*1.2 - 6,043,000*1 -64,000*0.85). When all three depots are taken into consideration, the scenario offers potential cost savings in excess of US $2.1 million by employing 91 dedicated working units with an average load ratio of 99 percent.
The depot-based solution has another advantage: workforce stability. Unlike a network-based dedicated fleet, a depot-based solution stations a predictable number of drivers at predetermined locations. This is a distinct advantage when one considers that, when the economy is strong, it's not uncommon for U.S.-based long-haul motor carriers to see a 100-percent or higher annual turnover among drivers. Adopting a depot-based solution that regularly brings drivers home helps to attract and retain drivers for the long term. Moreover, it almost goes without saying that satisfied drivers will provide the best service to customers.
Lower driver turnover offers cost benefits, too. Assuming a typical cost of US $10,000 per year to recruit and train a driver and 100-percent annual turnover of long-haul drivers, the network-based dedicated fleet implementation presented in Figure 8 will incur an estimated US $1 million recruitment and training cost. That brings the net savings for the network- based dedicated fleet solution with a 99-percent load ratio down to US $1.4 ($2.4 - $1) million. The depot-based dedicated fleet solution shown in Figure 9, with an assumed 50-percent turnover of drivers (a typical percentage), will require recruiting and training 46 drivers during the year. After deducting the lower recruitment and training costs, the depot-based solution can be expected to produce a savings opportunity of US $1.65 ($2.1 - $0.46) million. In short, the network-based dedicated fleet implementation might offer larger net savings, but when the driver turnover factor and associated costs are considered, a depot-based solution has merit.
Implications for carriers and shippers
The methodology for evaluating dedicated fleet opportunities presented earlier will benefit both carriers and shippers. First, it will give carriers the ability to evaluate the scale and scope of potential dedicated fleet opportunities in a shipper's network. This helps them to effectively respond to shippers' requests for proposal (RFPs). Second, carriers can use it to assess the dedicated fleet opportunity from the shipper's perspective by using market-average, common-carrier rates and the dedicated fleet cost coefficients they have proposed to the shippers. In addition, this analysis will guide carriers in proposing appropriate, lane-based pricing strategies in the shippers' networks. Third, this methodology will help carriers participating in optimization-based transportation procurement. (Optimization-based transportation procurement employs sophisticated analytical methods to determine which carriers and modes should be used on a set of lanes in order to minimize systemwide transportation costs.) Specifically, carriers could identify bundles of lanes and associated pricing, which is a key input to the optimization-based transportation procurement process. Finally, carriers could evaluate the trade-offs between implementing a depot-based dedicated fleet and a network-based dedicated fleet.
From a shipper's perspective, the proposed methodology can be used in two distinct ways. First, shippers interested in implementing their own private fleets could use this methodology to determine the scale and scope of private fleet opportunities in their networks. They can do so by using the rates paid to common carriers and the estimated cost of establishing and operating a private fleet as inputs. Second, shippers can use the methodology to negotiate rates with dedicated carriers. For example, shippers could estimate the cost benefits of a dedicated fleet on various lanes and negotiate the rates accordingly.
Transportation and logistics managers should consider a depot-based dedicated fleet as a way to reduce their operating costs and improve service in their networks. Equally important, implementing a depot-based dedicated fleet has significant potential to enhance the quality of life for drivers, and thus improves driver retention. The creation of a stable workforce of drivers thereby ensures shippers a consistent, high quality of service to their customers at a reasonable cost.
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.
Economic activity in the logistics industry expanded in November, continuing a steady growth pattern that began earlier this year and signaling a return to seasonality after several years of fluctuating conditions, according to the latest Logistics Managers’ Index report (LMI), released today.
The November LMI registered 58.4, down slightly from October’s reading of 58.9, which was the highest level in two years. The LMI is a monthly gauge of business conditions across warehousing and logistics markets; a reading above 50 indicates growth and a reading below 50 indicates contraction.
“The overall index has been very consistent in the past three months, with readings of 58.6, 58.9, and 58.4,” LMI analyst Zac Rogers, associate professor of supply chain management at Colorado State University, wrote in the November LMI report. “This plateau is slightly higher than a similar plateau of consistency earlier in the year when May to August saw four readings between 55.3 and 56.4. Seasonally speaking, it is consistent that this later year run of readings would be the highest all year.”
Separately, Rogers said the end-of-year growth reflects the return to a healthy holiday peak, which started when inventory levels expanded in late summer and early fall as retailers began stocking up to meet consumer demand. Pandemic-driven shifts in consumer buying behavior, inflation, and economic uncertainty contributed to volatile peak season conditions over the past four years, with the LMI swinging from record-high growth in late 2020 and 2021 to slower growth in 2022 and contraction in 2023.
“The LMI contracted at this time a year ago, so basically [there was] no peak season,” Rogers said, citing inflation as a drag on demand. “To have a normal November … [really] for the first time in five years, justifies what we’ve seen all these companies doing—building up inventory in a sustainable, seasonal way.
“Based on what we’re seeing, a lot of supply chains called it right and were ready for healthy holiday season, so far.”
The LMI has remained in the mid to high 50s range since January—with the exception of April, when the index dipped to 52.9—signaling strong and consistent demand for warehousing and transportation services.
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).
Specifically, 48% of respondents identified rising tariffs and trade barriers as their top concern, followed by supply chain disruptions at 45% and geopolitical instability at 41%. Moreover, tariffs and trade barriers ranked as the priority issue regardless of company size, as respondents at companies with less than 250 employees, 251-500, 501-1,000, 1,001-50,000 and 50,000+ employees all cited it as the most significant issue they are currently facing.
“Evolving tariffs and trade policies are one of a number of complex issues requiring organizations to build more resilience into their supply chains through compliance, technology and strategic planning,” Jackson Wood, Director, Industry Strategy at Descartes, said in a release. “With the potential for the incoming U.S. administration to impose new and additional tariffs on a wide variety of goods and countries of origin, U.S. importers may need to significantly re-engineer their sourcing strategies to mitigate potentially higher costs.”
Grocers and retailers are struggling to get their systems back online just before the winter holiday peak, following a software hack that hit the supply chain software provider Blue Yonder this week.
The ransomware attack is snarling inventory distribution patterns because of its impact on systems such as the employee scheduling system for coffee stalwart Starbucks, according to a published report. Scottsdale, Arizona-based Blue Yonder provides a wide range of supply chain software, including warehouse management system (WMS), transportation management system (TMS), order management and commerce, network and control tower, returns management, and others.
Blue Yonder today acknowledged the disruptions, saying they were the result of a ransomware incident affecting its managed services hosted environment. The company has established a dedicated cybersecurity incident update webpage to communicate its recovery progress, but it had not been updated for nearly two days as of Tuesday afternoon. “Since learning of the incident, the Blue Yonder team has been working diligently together with external cybersecurity firms to make progress in their recovery process. We have implemented several defensive and forensic protocols,” a Blue Yonder spokesperson said in an email.
The timing of the attack suggests that hackers may have targeted Blue Yonder in a calculated attack based on the upcoming Thanksgiving break, since many U.S. organizations downsize their security staffing on holidays and weekends, according to a statement from Dan Lattimer, VP of Semperis, a New Jersey-based computer and network security firm.
“While details on the specifics of the Blue Yonder attack are scant, it is yet another reminder how damaging supply chain disruptions become when suppliers are taken offline. Kudos to Blue Yonder for dealing with this cyberattack head on but we still don’t know how far reaching the business disruptions will be in the UK, U.S. and other countries,” Lattimer said. “Now is time for organizations to fight back against threat actors. Deciding whether or not to pay a ransom is a personal decision that each company has to make, but paying emboldens threat actors and throws more fuel onto an already burning inferno. Simply, it doesn’t pay-to-pay,” he said.
The incident closely followed an unrelated cybersecurity issue at the grocery giant Ahold Delhaize, which has been recovering from impacts to the Stop & Shop chain that it across the U.S. Northeast region. In a statement apologizing to customers for the inconvenience of the cybersecurity issue, Netherlands-based Ahold Delhaize said its top priority is the security of its customers, associates and partners, and that the company’s internal IT security staff was working with external cybersecurity experts and law enforcement to speed recovery. “Our teams are taking steps to assess and mitigate the issue. This includes taking some systems offline to help protect them. This issue and subsequent mitigating actions have affected certain Ahold Delhaize USA brands and services including a number of pharmacies and certain e-commerce operations,” the company said.
Editor's note:This article was revised on November 27 to indicate that the cybersecurity issue at Ahold Delhaize was unrelated to the Blue Yonder hack.
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."