[WHITE PAPER] How to measure freight data analytics value and why backtesting proves it

Jason VanoverFreight Market Blog, White Papers

Volatility has become commonplace in transportation management. According to Dan Weinberger of Supply Chain Brain, “In a time of increasingly unpredictable consumer demand, traditional supply chains are struggling to keep up. In response, businesses need to weed out outdated components and obsolete practices, while moving toward a more agile system.

Organizations that are still employing traditional methods will struggle to streamline end-to-end supply chain processes. Obsolete practices will only increase the complexity and difficulty of the task.” 

Part of that need to identify obsolete practices and find a better path forward depends on the ability to maximize the value of new technologies, including analytics. 

In other words, supply chain leaders need to validate and measure the success (read “accuracy”) of data source analytics and how well they stack up to the observed market conditions. In fact, that’s where the value of FreightWaves Scientific Indices and Intermodal Contract Savings Indices is most apparent.

To help supply chain leaders understand the necessity of backtesting in data analytics, particularly predictive freight analytics, this white paper will explore:

  • The possible backlash resulting from outdated data or inaccurate data insights.
  • The impeccable value of accuracy in all predictive analytics insights.
  • A few tips to measure the validity and accuracy of analytics metrics.

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