A trucking company’s bidding processes can be the difference between profit and loss in an industry that regularly deals with tight margins. The strategy used to decide which lanes to bid on and at what rates will directly impact revenue and margins. However, this process is often far from straightforward. It can be a lengthy process for analysts to find order and strategy from the unlimited number of lane combinations. This is where an AI-powered platform can support trucking companies with a better-informed and time-saving approach to bid analysis, eliminating the need for manual human analysis and guesswork. AI accounts for market impacts and dependencies to improve network balance, ultimately helping trucking companies make optimized decisions on the lanes they choose to bid on.
Bidding often happens in a yearly RFP process, but it may also be more sporadic when new business opportunities present themselves. The RFP process brings its own share of challenges for trucking companies, driven by the fact that many shippers are going through the same process at the same time. They send their RFPs, and trucking companies often have a limited time frame to assess their network capabilities and market rates. The shippers, meanwhile, are also conducting optimization processes on their end and communicating back to the carriers. Ultimately, this can make it difficult for the carrier to have an efficient bid analysis process while also having confidence that their bid strategy and decisions are the most optimal for their business.
Trucking companies need a way to quickly identify the lanes that work best with their current and future network and can give them the rate per mile they are looking for. The challenge comes from the fact that bidding happens at the lane level, with forecasted volumes, and at this point, shippers have no way of setting in stone any load-level specifics. Trucking companies need a way to take the hypotheticals of bidding and work out possible load-level details to help them evaluate their options.
With the typical approach to bid analysis, the difficulty is being unable to simulate the effects on the network and, therefore, needing help understanding the impacts on profit margins, network balance, service coverage, and more. To fully understand profitability, bid analysis must consider all load factors within the context of the broader network, like load sequencing and empty miles. These load-level factors have the potential to impact the conclusions significantly, and yet they are easily left out of manual data interpretation and decision-making.
Essentially, trucking companies must be able to translate lane-level options into load-level activity to truly understand the effects on their network and profits. To achieve this, they need simulation capabilities as well as their historical data to serve as a reference guide, translating the hypotheticals into concrete, reliable examples of load coverage, profits, etc. This would allow companies to view how analyzed bids impact their existing network and create future, simulated scenarios of their network to accommodate those impacts.
Optimal Dynamics uses AI to do just this—to take bid files and help trucking companies see their options more tangibly by automatically analyzing bids and providing recommended lane decisions.
Our powerful AI engines take lane-level volume from shippers and breaks it down into transactional loads. They then use historical load data to form accurately forecasted load volumes from the bid files. From here, AI runs multiple simulations to work the forecasted loads into the current network, and the result is a recommendation for each lane in the bid file—whether it should be accepted or rejected for the network.
To help trucking companies understand this recommendation, we created the Lane Score. The Lane Score is a score of 0 to 100 assigned to each bid lane as an objective rating to understand a lane's value. It indicates the lane's viability, helping users prioritize their bids. Rather than manually evaluating bid lanes, users can save time and resources to quickly identify the most promising opportunities.
This Lane Score, within Bid by Optimal Dynamics™, is calculated from configurable, customer-defined metrics like number of loads covered and the service coverage. These metrics help define what it means for a lane to be acceptable (a Lane Score of 70–100), tolerable (11–69), or unacceptable (0–10).
Users can sort by Lane Score, change any decisions (from accepted to rejected or vice versa), adjust the volume accepted, or adjust the rate per mile. With control over these details, they can rerun simulations, paying attention to whether there are any changes to Lane Scores to further confirm the simulated decisions are the best choices for the network.
This type of AI-powered solution completely handles the heavy lifting of bid analysis in a way that is not otherwise possible. Users gain clarity on each option, including the impacts on revenue and how well each accepted lane would be covered by assets versus brokerage. Bid Manager provides detailed insights and gives trucking companies a way to approach the bid analysis process faster and more efficiently than ever. Users stay in complete control over making any adjustments and can rerun simulations to accurately represent the effects of their final decisions.
Optimal Dynamics takes the guesswork out of the bidding process. Using our forecasting engine, a layer of the users’ historical data, plus thousands of AI simulations, users get data and insights to inform their bid responses. While Bid by Optimal Dynamics™ provides a clear recommendation for each lane, users also get a valuable tool that enables them to explore the effects of their lane decisions even deeper.
To learn more about Load Scoring for better decision-making, schedule a demo with Optimal Dynamics today.