How to Use Big Data in Freight Transportation


Big data has been a buzzword for some time, and for good reason. There are some pretty radical potential applications of big data, such as accurate demand forecasting or industry-wide real-time visibility. Oracle calls big data the “electricity of the twenty-first century – a new kind of power that transforms everything it touches in business, government and private life.” There are also many ways to use big data in freight transportation.

But how does it apply to freight transportation?

Most transportation data revolves around operating a transportation management system (TMS), which tracks and analyzes freight performance. Implementing a big data initiative can be overwhelming and the return on investment is hard to predict.

With the use of a TMS, there are practical ways to start utilizing data that’s readily available to optimize freight movement and reduce transportation spend.

Transportation Metrics that Matter

There are multiple transportation metrics that a shipper can track, but there are 5 basic metrics that give visibility into transportation performance and cost:

Average Time-in-Transit

Calculated by taking the sum of time-in-transit and dividing it by the number of trips. This metric is useful for finding how long it typically takes your freight to reach its destination. Shipping and production schedules can be altered based on this information to improve service.

Inbound Freight Costs as a Percentage of Purchases

Calculated by dividing total inbound freight costs by total purchase amounts in a given period. Use this metric to judge inbound performance, the effect inbound transportation has on your bottom line, and whether freight paid or freight collect is a better payment method.

Outbound Freight Costs as a Percentage of Sales

Calculated by dividing total outbound freight costs by total net sales. Shippers rather view transportation as a high-cost to control, rather than a strategic asset and competitive advantage. With that in mind, this metric reveals to what degree freight costs are an obstacle to increased revenue, and also gives an idea of overall transportation performance.

Mode Selection Efficiency

Calculated by dividing the number of shipments sent on the optimal mode by the total number of shipments. For this, each lane must have a designated optimal mode. While calculating mode selection efficiency may be time-consuming, it provides valuable data that shows the accuracy and productivity of decision making within a logistics organization. It also reveals opportunities for significant cost savings.

On-time pickups

Calculated by dividing the number of pickups made on-time by the total number of shipments. This metric measures the performance of the freight carriers servicing a shipper. It can help find the best carriers and show how much certain carriers affect logistics operations.

Simply tracking data is not enough to make a difference within an organization and reduce transportation spend. Practical applications of big data in transportation require shippers to set goals, understand the metrics they’re measuring, and most of all, take quick and decisive action on the data available.

Need logistics help? Contact us now with any questions you have!

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