Current Situation
Today’s shipment tracking combines online services of shipments with manual data scrapes of carrier/terminal systems or calls to carrier dispatch and customer service. This causes costly delays, ties up resources, and affects timely decision making.
Goals and Objectives
This use case provides decision support based on predictive analytics and likelihood of meeting predefined service levels. Advancing beyond assigning a binary “on time” or “late” designation to a shipment, contextualizing this designation with a probability of achieving on-time delivery allows teams to assess and manage the risk of a late delivery with their tolerance for a potentially late delivery and the costs associated with intervening to speed it up. Assignment of confidence intervals provides deeper clarity to shipment status, allowing for more focused mitigation efforts, better network planning, increased customer satisfaction, and improved cost controls.
Technology Deployed
IoT, personal devices, cloud, big data/analytics, mobile, APIs, telematics, connectivity services, and live vehicle tracking
Use Case Summary
Live vehicle tracking through onboard devices gives location of asset in real time and estimates the probability that pickup/delivery commitments will be met allowing teams to focus efforts only where needed and manage by exception.