Optimal Dynamics Uses AI for Better Truckload Bidding, Dispatch Decisions – Fleet Management

Optimal Dynamics is using artificial intelligence to help truckload carriers make better decisions and has added features to help optimize EDI tender acceptance, bidding workflow, and dispatching.

Leveraging the company’s powerful optimization engine, these new solutions provide recommendations that reach further throughout the complex workflows in modern logistics.

Optimized EDI Tender Acceptance Recommendations

With the many decisions dispatchers and planners face, increasing efficiency and automation at each stage is crucial.

For years, Execute by Optimal Dynamics has provided optimized recommendations on whether to assign loads to either asset or brokerage to best balance truckload networks.

Today, the company is applying its decision intelligence to the question of whether to accept or reject a tendered load. It provides recommendations that assess unique business rules and factors such as available capacity, existing commitments, driver coverage, and profitability.

Optimized Bidding Workflow

Finding the optimal rate to bid requires analyzing numerous pricing options. This process can often be tedious and time-consuming, leading to slower RFP responses.

The new Bid Rate Analysis tool streamlines the experience for bulk lane rate adjustments and analysis directly from uploaded bid files. Now, users can easily run multiple bid analyses at different rates after applying bulk rate changes, for a more efficient, informed, and responsive bidding workflow. 

Analysts also will have greater insight into how bid lanes affect their complete shipper network. The Network Impact Analysis provides a comprehensive list of customers that would be impacted in the future, assuming all Optimal Dynamics recommended lanes are won.

Dispatch Outcome Explainability

Just as important as knowing what decisions to make is understanding why those are the best decisions to make. With embedded KPIs providing clear visibility into metrics such as revenue per driver, empty mileage percentage, and current availability, fleet managers can quickly gain a detailed view of future business performance based on Optimal Dynamics recommendations and act quickly on those recommendations.