April 2014 | Sponsored | Thought Leaders

Big Data Tools Enable Predictive and Prescriptive Analytics

Tags: Logistics I.T., Transportation Management Systems (TMS), Transportation Management

Shannon Vaillancourt is President, RateLinx, 262-565-6150

Q: What is different in transportation technology today compared to five years ago?

A: Two words: big data. It has become significantly less expensive in the past five years to store and analyze large amounts of data. Due to these decreased costs, companies can now afford to use data to gain additional insight into their transportation patterns.

Q: How can technology be used to mitigate rising transportation costs?

A: Big data tools must be used to reduce the overall perceived risk that carriers have with a company's freight. This can be accomplished in three ways:

  1. Providing each carrier with a larger dataset of freight moves.
  2. Modeling the dataset to obtain the resultant carrier mix.
  3. Utilizing a Transportation Management System (TMS) to make the carrier selection.

The larger dataset will allow each carrier to have visibility to the exact freight lanes, as well as the frequency or seasonality of the volume in each lane. Modeling the dataset with the carrier's proposed rates and business operational rules allows shippers to take into account their service requirements. Today's consumers and trading partners are more sophisticated, which adds complexity to the carrier awards. A more granular lane strategy between shippers and carriers helps mitigate risks and costs for both parties. Then, by using a TMS to make the carrier selection, shippers are able to load the same business rules to achieve the predicted outcome from the modeling.

Q: Having enough supply chain talent is a problem today. How can technology help with this issue?

A: By leveraging the big data tools that are becoming more prevalent, companies can quickly spot trends that would otherwise have gone unnoticed. Many people are under the impression that big data only refers to a large amount of data. The second definition of big data is that the dataset is too difficult to process using traditional data processing applications. When it comes to supply chain operations, many large companies are still dependent on using a spreadsheet to manage a very complex global part of the business.

With big data tools, shippers can move past the business intelligence side of measuring and diagnosing, and move into the predictive and prescriptive side. A big data tool will allow transportation teams to have fewer experienced supply chain staff members, because the data will be more actionable. As big data continues to evolve, the prescriptive side will become more prevalent and powerful as the data being captured will allow the system to self-correct with little to no user intervention.