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As a tech-enabled 3PL, GlobalTranz is continuously improving and creating digital tools, including predictive analytics and complex algorithms, to deliver instantaneous insights on transportation pricing.
However, in times of extreme market volatility, these tools can fall short.

In these instances, human expertise is needed to support the recommendations from the pricing model. Some digital freight matching brokerages experienced the risks of machine-only cost prediction models firsthand during the height of consumer “panic buying” resulting from the COVID-19 pandemic. Anecdotal reports suggest that their reliance on algorithms without human oversight performed poorly as the transportation market fragmented into “essential” and “non-essential” industries seemingly overnight, creating freight imbalances which their models failed to adequately process.

GlobalTranz has been leveraging a Cost Prediction Model (CPM), which uses a dynamic pricing API to deliver real-time rates on Truckload shipments from any origin to any destination across North America, for a number of years. The CPM predicts lane-level costs based on a wide variety of factors including historical costs and current market conditions. A key component of GlobalTranz’s digital freight matching capabilities, the CPM ensures that the company maximizes supply chain efficiency for customers by securing freight at the optimal price with optimal service.

In this white paper, we will explore how GlobalTranz leveraged its inherent technology skills along with technology partnerships including Microsoft and West Monroe, a national business and technology consulting firm, to effectively insert a user interface (UI) facilitating human oversight of machine generated predictive pricing through a creative and resource-efficient use of both high- and low-code approaches.

Combining High- and Low-Code to Deliver Impactful Predictive Analytics

In this white paper, we will explore:

  • How GlobalTranz built its Cost Prediction Model
  • Utilizing Low Code in concert with High Code development
  • Using Low Code to Develop a User Interface (UI)
  • Leveraging the Power platform to facilitate human oversight of machine generated predictive pricing