Predictive analytics are among the most iconic ways freight managers can derive more value and streamline logistics through freight data. On the surface, they provide an assumed future for day-to-day freight operations based on current conditions, including weather. But predictive data analytics are only as valuable as the quality, integrity and actionability of data. Thus, it’s imperative for advanced shippers, brokers and carriers to start using predictive analytics to strengthen ROI of new tools and resources.

Why not using predictive insights leaves money on the table

Predictive data analytics tell what will happen based on today. The value of foresight is undisputed. If an organization can proactively identify what is likely to happen, it becomes possible to intervene and ensure the best outcome. Failure to do so will inevitably result in lost opportunities to recapture excess revenue.

Predictive analytics is relatively new in the scope of modern supply chain management. According to Supply & Demand Chain Executive, “Even though the use of analytics has only emerged over the last 10 years, the ability to leverage this data to forecast or predict jobs and outcomes based on several factors is quickly evolving as the Next Big Thing for data use. Today’s transportation fleets are leveraging this advanced business intelligence to help drive costs out of the supply chain.”

Predictive capabilities are not solely applicable to individual load tendering. Predictive data analytics can be deployed to improve annual truckload RFP processes, understand how transportation analytics flow seamlessly into the next stage of data analysis and derive more value.


Predictive analytics generate the meat of forecasts based on current conditions

A common misconception of predictive capabilities goes back to their infancy in terms of the full supply chain. Traditional supply chains were incredibly linear and rather stale. Freight moved in each, prescribed path from manufacturer to retailer, right down the street and within a few miles of one another. However, the growing complexity of global supply chains created additional opportunities for risk. Thousands, if not millions of conditions, affect individual loads. 

If a single load results in an added expense of $30 due to a weather delay, it may not seem like much. Over the course of 1,000 shipments, reflecting approximately three booked loads per day for brokers, that is waste in excess of $30,000. Depending on pay rates at your organization, it could also amount to one or more FTE savings opportunities for broker revenue. That’s where the real benefit of predictive data analytics comes into play.

Predictive analytics generate the meat of forecasts based on what has happened in the supply chain and what is expected to occur. They must be incredibly adaptive and capable of responding to volatile freight markets as well. Of course, that’s only the beginning. Once predictive analyses complete a reporting cycle, it is up to prescriptive data analytics to identify the next best freight management action. With that in mind, it becomes necessary to know what’s needed after making those changes.

Best practices in using predictive capabilities in logistics

Earlier this week it was explained how prescriptive analytics is the actionable step of the analytics cycle. And they could be applied to any process, such as freight spend or tender market share forecasting. They literally prescribe what an organization should do to achieve an optimum result. But the supply chain would fall apart if an organization only did what a computer said and did not follow through to ensure it worked. That is where the waters between predictive and prescriptive capabilities begin to get more complex. As a result, it’s best to follow three best practices in using predictive capabilities and logistics:

  • Increase the sample size of your dataset to generate the most comprehensive insights.
  • Recognize how one form of one analytics turns into another.

Wait, what? If analytics show what to do, doesn’t the process end there?

Not really. Predictive analytics relies on the sum of everything to generate the possible outcomes that may occur. In that respect, predictive analytics begin to combine with descriptive and diagnostic capabilities to identify what is happening after making improvements and generating additional forecasts. The flux of real-time freight data continuously results in movements between the four types of analytics. And over time, they amount to a more robust, agile and enduring transportation strategy.

Gain a better market understanding with predictive analytics-driven management

Predictive data analytics are essential to navigating freight market volatility. And that’s something to revere in the modern logistics network. Start by realizing the true value of predictive analytics as a tool for continuous improvement. So what are you waiting for? Become a better shipper, broker or carrier by gaining access to the most sweeping and insightful analytics in the freight market by clicking the button below to request a FreightWaves SONAR demo today. 

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