Strategic Shipping Solutions
Q: How is predictive modeling reshaping logistics?
A: Predictive modeling is changing the dynamic that exists between the carrier and the shipper. It's creating more of a collaborative environment, where the carrier and shipper can now talk openly about their goals with the transparency required to create and maintain a great relationship. The shipper now has the levers to pull that will instantly apply a new pricing proposal from a carrier, and provide instant feedback to the carrier during the rate discussions. Predictive modeling is also allowing the shipper to explore completely different strategies and understand the outcomes before investing a large amount of money to make the change.
Q: How can shippers harness their data to transform freight from a tactical necessity to a strategic advantage?
A: I see this as the biggest challenge facing shippers. It may seem obvious, but shippers have to start thinking more strategically and less tactically about freight. For example, shippers will perform an RFP with their carriers every year with the goal of lowering their rates each time. Instead, shippers should be evaluating their shipping profile to uncover the inefficiencies or lack of compliance that is currently costing them money. The shipper's rates may be the best rates in the market, but if they aren't using them correctly, how good are those rates? The data should be used to continually measure and monitor the health of their freight spend.
Q: How are shippers missing the mark when it comes to embracing big data analytics?
A: Big data is all about looking forward, not backward. I see shippers confusing big data with business intelligence, or descriptive analytics, which is looking backward to confirm the decisions that have been made. Running an analysis to show how much money has been saved three months after implementing a change is not taking advantage of big data analytics. Big data is defined using the three V's (volume, velocity, and variety). Measuring change in real time takes advantage of the velocity element to assess both the volume and variety of data. This allows you to leverage the prescriptive nature of big data by having real-time visibility and transparency around any non-compliance that is preventing you from reaching your savings goal. You can now make any necessary changes in a timely manner. Non-compliance is corrected, and you're on track for realizing the savings.