What are the biggest contributors to freight spend? Is it freight pricing models, capacity constraints, lackluster carrier density or something else? Chances are good that it’s a combination of these factors. And while leveraging a freight rate index or ocean import data to lower detention risk, the sheer size of the supply chain makes management difficult at best. It’s simply too complex to figure out the next best step, the next best action, in freight management, such as using intermodal when appropriate. That’s why using prescriptive analytics can add the most value. Let’s take a closer look at why.

Freight management is the story of supply and demand

Freight management is a cycle on a macro-scale. It’s millions of individual transactions that amount to pallets, loads, trucks, rail cars, airplanes and beyond. Why is that relevant? 

It comes down to a simple story of supply and demand. When supply is limited, like trucks have been for much of 2020, and the demand stays high, freight management becomes about more than simple schedules and asking who’s available?

And depending on the size of your supply chain, filling a single order could result in a compounding series of actions that go back to the supplier itself.

As an example, it may be necessary to leverage drop shipping to give a manufacturer the opportunity to fill the order and manage its transportation process. With the volume and complexity of supply chains increasing, it comes as no surprise that freight managers may simply not know everything that’s happening. Obviously, that is a  situation that will inevitably lead to added expenses. On the other hand, using the power of analytics, including descriptive (what happened) analyses, it is possible to gain additional insights into day-to-day activities.


Any plan must consider endless “what if” scenarios

Analytics then become more valuable using diagnostic resources, indicating why something happened in the past. Those analytics then consider the “what if” scenarios and their combinations. When analytics reach this stage of maturity, it becomes a predictive component of supply chain management. Simultaneously, predicting what might happen must consider all the other things that could happen at the current time.

According to a recent Journal of Big Data article, “In typical [supply chain management] problems, it is assumed that capacity, demand, and cost are known parameters. However, this is not the case in reality, as there are uncertainties arising from variations in customers’ demand, supplies transportation, organizational risks and lead times. Demand uncertainties, in particular, have the greatest influence on [supply chain] performance with widespread effects on production scheduling, inventory planning and transportation. In this sense, demand forecasting is a key approach in addressing uncertainties in supply chains.”

Now going back to the examples of what-if scenarios, it is possible to isolate the conditions in which an optimum outcome will occur. Thus, figuring out the optimum steps to take to achieve that outcome is how freight managers arrive at the next best action.

A freight management next best action uses prescriptive analytics

A freight management next best action describes the literal steps needed to achieve an optimum outcome. That is the simple part. The next aspect involves figuring out how those steps turn into meaningful improvements within all operations.

As an example, the next best actions identified through prescriptive analytics can be exceedingly complex. It may be necessary to submit multiple quotes to clients via freight broker software, or advanced shippers may seek to initiate new truckload RFP processes. The exact steps to take are subjective and dependent on the continuous flux of information within the supply chain. That’s why it’s critical to have access to real-time data capabilities in freight management. Without a source of data that is indicative of true market conditions, it is impossible to get a handle on the best steps necessary to achieve the best results.

Quite simply, prescriptive analytics’ next best action is really a series of steps that will reveal what’s needed to reach the best outcome. And the opportunities to apply the next best action are limited only by the imagination and potential applications of logistics managers and professionals. In other words, the next best action has an implication for front-line sales reps in the brokerage, replenishment processes in warehouses, drivers that work for carriers and so on.

Explore your company’s freight management next best action with SONAR data now

The evidence behind prescriptive analytics is clear. Increased use of foresight-capable analytics, prescriptive data insights, amounts to improved day-to-day management. And it begins with capturing and analyzing data in its raw form. Fortunately, the advancement of freight data providers and analytics, including FreightWaves SONAR, have made it easier than ever to put the power of prescribed actions into motion. Click the button below to request your SONAR demo today.

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