Before big data, shippers managed freight rates and strategic transportation sourcing with simple tools. For modes such as truckload, air freight, ocean, etc., most shippers maintained a huge Excel spreadsheet containing all carriers and rates for given lanes. The spreadsheet could take days to update when a sourcing event occurred and made it difficult to compare historical data.

For example, less-than-truckload shippers utilized a common rate base then would have all carriers offer state-to-state discounts and floor charges. After comparing the bids, they awarded the lane(s) to the carrier that offered the biggest discount. Often the sourcing events contained very little detailed data, making it difficult for the carrier to accurately determine the best rate to bid. This caused the carrier to either overbid on the discount, which would cause the carrier to come back a few months later with a rate increase, or it would cause the carrier to underbid the discount causing the shipper to pay too much.

Big Data Agility Offers Faster Transportation Sourcing and Freight Rate Management

Big data agility has turned these slow historical processes on their heads and, combined with the new tools available, shippers are now able to achieve quicker and easier rate management where rates for a given lane are stored in an easy-to-use system. This system will now maintain the historical data for quicker comparisons as well as the ability to update rates in hours instead of days with the provided tools.

Supply chain big data creates a more collaborative approach to transportation sourcing, allowing shippers to tell carriers the exact discounts that should be provided in order for the carrier to receive the exact freight mix they want. The detailed data that can be shared with the carrier removes all of the risk from the pricing, allowing the shipper to receive the lowest rate possible and the carrier to maintain a uniform profit level.

Big data puts the shipper in lockstep with the carrier by leveraging the carrier’s rate base (for LTL) which lets the shipper see which lanes a carrier operates most efficiently and at the lowest cost. Carriers can then haul the freight that fits their needs with no risk of hauling freight in the lanes that they are not efficient in.

The Role of Small Data in a Big Data World

Small data isn’t talked about too much but it does play an important role in today’s freight management, allowing the shipper to comprehend what is happening in their supply chain.

A great example of small data is the measurement of non-optimal freight when a shipper may have just finished a sourcing event and has provided their new pricing to their various locations. By re-rating each freight invoice against the new pricing as it is received, displaying the invoice where the optimal carrier wasn’t used, and the associated optimal cost allows the shipper to take corrective action and figure out why a given location isn’t routing the freight based on the new carrier pricing that is in effect.

Big data has to be converted to small data. Too many times we see companies focused on collecting all the data to create the largest dataset possible, only to find that they aren’t getting any actionable information. Small data contains the specific attributes that make it easy for a procurement manager to not only understand where the cost savings opportunities are, but to take the required action to achieve those savings.

Why Big Data Makes Smarter Supply Chain Decisions

Big data provides all the data points that are required to rate for the different modes. Small parcel requires each individual box’s weight, length, width and height; LTL requires class and weight; truckload requires miles, etc. Each mode is unique and can be impossible to compare without the proper data points.

Big data provides the transparency required to remove bias to create stronger supplier and carrier relationships. Having big data drive the decision making is a more objective approach that provides the carrier with assurance that they will receive the amount of freight they thought they would. It also allows the shipper to receive sustainable rates from a carrier, and enables the shipper and the carrier to collaborate on service requirements providing a better experience for the shipper’s customer.

RateLinx Enhances Freight Rate Management and Sourcing Capabilities

The RateLinx Enterprise Solution has the unique ability to capture supply-chain big data and make rate management easy for shippers and carriers. The ShipLinx Transportation Management System (TMS) uses an effective date for all carrier rates. Shippers have instant and easy access to historical rates to do analysis and view trends. The RateLinx Enterprise Solution integrates the ShipLinx TMS data with Freight Audit data and cleanses and standardizes it in real-time to provide the most accurate and relevant view of your supply chain.

By having the ShipLinx TMS re-analyze standardized datasets, it is able to convert the big data into easy-to-understand small data to show potential savings, as well as displaying the savings that have been achieved through the strategic use of the tools. All of this is available through the use of the RateLinx Dashboard and the potential savings KPI’s:

  • Rate Shopping shows the savings potential that exists by simply rate-shopping your current carrier mix.
  • Mode Optimization shows the savings potential that exists by comparing LTL versus small parcel’s hundredweight rates.
  • Multi-Stop shows the savings potential that exists by converting LTL shipments to multi-stop truckload shipments.
  • Inbound Portal shows the savings potential that exists by leveraging the RateLinx Inbound Portal to optimize your inbound freight.

Leveraging big data for rate management and strategic sourcing poses many technical challenges, but our software tools make it easier to do so for you. If you would like to learn more about how we can help, feel free to send me and email with your questions.