Automating Load-Level Decisions Between the Asset & Brokerage Divisions to Maximize Network Potential
Thought Leadership
Thought Leadership

Automating Load-Level Decisions Between the Asset & Brokerage Divisions to Maximize Network Potential

July 28, 2023

By Optimal Dynamics

Every trucking or logistics company with assets faces daily decisions of accepting or rejecting loads. If there is a brokerage division, load planners must also decide whether to send rejected fleet loads to their brokerage instead. Within these decisions is a tremendous potential to optimize the network, and businesses not utilizing solutions built and automated for this decision-making process may be missing out on profit, efficiency, and growth potential.


In this article, we discuss automation's role in enhancing these load-level decisions.


The Problem: Load-Level Decision Challenges in Freight Operations

Take the example of a load planner considering Load A or Load B for a particular asset. Load A is 40 miles away, and Load B is 50 miles away. The load planner also sees an available Load C for pickup in the same city as Load B’s delivery point and on the same day. Load C might entice the load planner to choose Load B, but how can they be sure it is the most optimal decision? The load planner would need to consider the next load if they chose Load A to compare the two options and decide. If they then start to look at the hypotheticals of the following load in the sequence, suddenly, a simple Load A or Load B decision has become a complex puzzle with variables to the nth degree.


The load planner also needs to consider whether assigning one or more of the loads in question to the brokerage division is more advantageous. Perhaps after all the driver assignment options are considered, certain loads are not a good fit for the fleet, but they can still turn a profit in brokerage.


Clearly, load-level decisions are a source of complexity for load planners of logistics companies that have both assets and a brokerage division. These decision-making processes present several obstacles and challenges.


Manual Processes

Traditional, manual processes for making load-level decisions are time-consuming, error-prone, and not scalable, especially for businesses that need to manage thousands of loads.


Data Overload

There is no practical way to manually and thoroughly handle the large amounts of data that should factor into each decision. With too much data to analyze, this leads to less-than-optimal choices.


Balancing Profitability and Service Levels

Deciding which loads to accept/reject or whether to assign them to the brokerage division involves balancing the complex factors of maximizing profitability and maintaining high service levels.


Real-Time Decision-Making

The dynamic nature of load availability requires decisions to be made in real time. If the process of assessing each load, available resources, and network constraints takes too long, the company could lose out on valuable load opportunities.


Uncertainty and Variability

Factors like changing demand and routes, variable transportation costs, and the possibility for new information to emerge add a layer of uncertainty that can complicate decision-making.


Unless logistics companies have a solution to manage these challenges, they face inefficiencies, reduced profitability, and other impacts.


Reduced Efficiency

Inefficient decision-making processes can lead to suboptimal use of resources — for example, assigning loads to assets that are not a good fit — leading to poor asset utilization or rejecting loads that the brokerage division could have profitably handled.


Lower Profit Margins

Poor load-level decisions directly and immediately impact profitability if they inadvertently cause load planners to accept low-profit loads or reject high-profit ones. From another angle, inefficiencies within asset utilization can lead to higher operational costs, cutting profit margins.


Decreased Customer Satisfaction

The inability to make quick, accurate load-level decisions can affect service levels and cause delays or unfulfilled orders, ultimately impacting customer satisfaction.


Limited Scalability

Manual decision-making cannot scale, which means operations cannot handle larger loads without requiring more resources.


Inability to Adapt to Change

Without the ability to quickly analyze data and adapt to changing conditions, companies may struggle to take advantage of new opportunities, both at the load and business levels.


Benefits of Automating Load-Level Decisions

Fortunately, there is a way to avoid these challenges and negative impacts — through advanced technology designed to automate and optimize load decisions. With this technology, companies can discover the following benefits.


Efficiency and Speed

Optimized decisions assisted by automation can significantly speed up processes while enabling real-time responses to changing conditions.



This type of system contributes to fewer errors, handling large amounts of data and complex algorithms more accurately than manual processes.



Automated solutions can easily scale to handle larger volumes of loads, making them ideal for growing logistics companies.



Advanced technology ensures consistent decision-making based on predefined rules and algorithms, eliminating the variability of human judgment.


Data Utilization

Automation can use large datasets and advanced analytics for better informed strategic decisions.


Implementing an automated solution to optimize load-level decisions can transform business operations by maximizing network potential and improving profitability. Companies can better allocate resources by accounting for every factor and constraint systematically. The results are decisions that directly help maximize profits. As an added benefit, automation reduces the need for manual intervention, reducing costs by streamlining decisions. Logistics companies can unlock their network potential and achieve greater profitability.


Optimal Dynamics offers this technology powered by AI, providing artificial decision intelligence for asset-owning logistics and trucking companies.


  • When BCB Transport implemented Optimal Dynamics’ solution for load acceptance and dispatching decisions, their revenue increased by 19.6%.
  • After Standard Logistics began using Optimal Dynamics’ solution, they doubled their fleet size and reduced the time to run a scenario from a week to just an afternoon.


The Solution: Load-Level Decisions that Benefit the Entire Network


Optimal Dynamics offers a unique approach to load-level decision-making. With artificial decision intelligence, Optimal Dynamics’ Execute product analyzes thousands of options and presents the best ones for consideration, saving time and effort for companies. The assigned Load Score helps rank available loads based on factors like asset availability, established business rules, and profitability. Next, Driver-to-Load matching automatically provides a ranked list of options. Users can see which drivers make the best fit while they retain flexibility in the final decision. This approach ensures that decisions align with the company's objectives.


Optimal Dynamics knows it is important for businesses to be more than reactive; they must also be predictive to compete in this industry. With forecasting capabilities, companies can anticipate future load options and receive suggested decisions leading to more desirable outcomes. Optimal Dynamics also has the flexibility to account for new information, like a change in driver availability or the availability of new loads. Decisions are adjusted based on the new needs, allowing companies to optimize even in dynamic market conditions.


Businesses get the benefits of:


  • AI-Powered Planning uses artificial intelligence to simplify and streamline complex processes like network fit, real-time dispatching, and load acceptance.
  • Advanced Algorithms account for a wide range of factors influencing overall profitability, including average profit and network health.
  • Capacity Forecasting generates accurate forecasts of capacity, shipper requests, and driver availability, enabling informed decision-making even when the future is uncertain.
  • Data-Driven Decisions grounded in accurate and complete data reduce the risks of error and bias in decision-making.


Case Study: Successful Implementation of Optimal Dynamics' Solutions

BCB Transport, out of Mansfield, Texas, was experiencing operational challenges from their decision-making capabilities, including load acceptance and dispatching. They partnered with Optimal Dynamics to conduct an onsite training session, "Dispatcher for a Day.” In this session, the implemented solution Execute resulted in 60% more freight being planned in the same amount of time, exceeding their operational efficiency goals.


As a result of more efficient decision-making and optimized decisions, BCB Transport saw a 19.6% revenue increase, even during a period of depressed market volumes and rates. With AI-powered decision-making, BCB Transport was able to optimize load acceptance decisions, leading to better utilization of their assets.


Leverage Artificial Decision Intelligence for Your Load Decisions

Logistics companies understand the potential their load-level decisions have to impact their bottom line, operational efficiency, and competitive advantage. But getting there is not a matter of implementing a better manual process. For the most effective solution, load-level decisions should be optimized through AI and automation.


Optimal Dynamics, offering artificial decision intelligence, provides this through Execute, automating load acceptance and dispatching decisions for improved asset utilization. Companies can make optimized decisions fast, enabling them to scale and grow without sacrifice.


To learn more, schedule a demo with Optimal Dynamics, and get on the path to load-level optimization.

Through load-level decisions, businesses have the potential to optimize their network and increase profits with the assistance of AI and automation.

Discover a Better Way to Plan and Execute

Connect with us to see the power of AI in action on your network.

Demo Request
Please include your first name.
Please include your last name.
Please include your job title.
Please include your company name.
Please include your phone number.
Please enter a valid business email address
Please select the number of trucks.
Please select a platform introduction type.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.