Christian Sorensen
Global Head of Marketing & Sales

In a recent Maritime Executive article, our own Christian Ove Sørensen, global head of marketing and sales at Portix Logistic Software (PLS), questioned the ability to predict pricing by way of utilizing data analytics. Imagine a shipper knowing exactly when to book a container or pallet for transportation to achieve optimum pricing.

 

"Can you predict with some kind of accuracy what freight rates will be one to two weeks out?" he asks. "Some customers tell me that this will never fly, that it's not a rational market. But some are interested to start exploring this."

Despite the naysayers, it is possible to build such a model by considering such inputs as fuel prices, carrier utilization and of course economic data such as GDP growth. In turn, by studying and linking these inputs to how predictive decisions have been achieved in other industries could further prove beneficial.

 

The Inputs

Identifying the right inputs is, of course, necessary but also the time of year, or seasonality, plays a big role. The ebb and flow of historical pricing, as well as carrier utilization is important to consider – the traditional peak season and before and after annual events such as the Chinese New Year when a high number of Asian manufacturing facilities close for a few weeks are examples of seasonality considerations.

A similar model can be found in the just-in-time business model that has been adopted by such industries as automotive and high-tech. Inventory is typically kept at a minimum with levels of such determined by data analytics. Key inputs to determine the optimum inventory levels required are based on such points as historical inventory levels, manufacturing orders and forecasts and, once again, the time of year.

 

The Model

Even though the inputs are logical, one may never achieve 100% accuracy. As the case for just-in-time models, one-off incidents often occur and as many are aware, the logistics market itself is often described as irrational – a labor strike at a port, a natural disaster at a manufacturing facility, mergers & acquisitions, new entrants and more plays into the ever-changing market.

Freight forwarders are perhaps best to embrace these types of analytics when working with customers. Well-versed in modes of transportation, customs brokerage, and trade-lanes, freight forwarders are able to view ‘the big picture’ and are often utilized to determine the best mode, or modes, of transportation along with the ‘right’ trade-lane to achieve not only the time requirements as dictated by the customer but also at the best price.

In addition, the experience, the years of relationships and the know-how that may only be found by simply talking to forwarders could possibly be harnessed into a data-driven model. This may be difficult to accumulate due to the perception that forwarders believe this differentiate themselves. However, the more data that can be gathered the more accurate such a model can be. It can also show how and why a forwarder may know better than anyone else how markets are performing and potential outcomes.

 

Predictive Pricing and PLS

A number of technology companies are already working towards building a model to predict pricing, however, with its partnership with WNS and years of experience in not only technology but also within freight forwarding, PLS is well- positioned to create such model. Interested in learning more? Contact us today to see how your company can be involved in this endeavor.