|Date||Monday, March 28, 2022|
|Time||10:30 AM - 11:15 AM|
|Location||Emerging Technologies Theater|
Minimizing risk is at the core of all decision-making when it comes to moving freight – how do you know if you’ve made the right call and selected the right carrier? Using the traditional approach, the answer won’t become evident until after the deal is done and the freight delivered. Oftentimes, bias plays a hand in which loads get moved due to an individual’s idea of what a winning shipment may be without any mathematical or quantitative data behind that decision. A specific shipment may be prioritized for the wrong reasons, even with good intent. This is when business intelligence [networks] can utilize predictive and prescriptive analytics to modify behaviors, reconcile substantiated data points and impact future freight models. In this seminar, BlueGrace will address the use of predictive and prescriptive analytics to help understand internal and external risks for 3PLs and shipper customers. In this case, risk is defined as price vs. cost, on-time performance, and load give back. Loads are analyzed for risk by leveraging proprietary pricing data while accounting for shipment and market characteristics. Understanding the range of outcomes helps price, prioritize and execute shipments in the most efficient and cost-effective way. The most effective 3PLs use information gained from business analytics to change behaviors and drive better decision making. Data becomes an integral part of the conversation with customers and carriers alike. An accumulation of historical data can prove certain behaviors lead to specific outcomes, and eliminate anchored bias on load platforms, which allows for the opportunity to make better decisions. Short term losses can lead to long term gains. This methodology assists in selecting the right carrier for each load while increasing velocity, efficiency and employee and carrier experience. Using proprietary intelligence, organizations can leverage a range of pricing options for optimal cost and time savings for shippers, carriers and employers. This allows for more relevant and productive conversations between parties and puts decision-making power into the hands of those closest to marketplace. As such, they can complete faster transactions, with less effort, and the understanding that the lowest price point doesn’t always lead to the biggest benefit.
A Predictive analytics workflow chart shows fours key entry points:
- Minimizing risk at the time of pricing a shipment
-Shipment prioritization based on risk (service the loads with highest risk first)
-Feedback logic using machine learning (looping)
|Raddy Velkov||BlueGrace Logistics|
Seminar sponsored by BlueGrace Logistics.