Logistics and parcel delivery
Building a prediction platform for delivery planning at scale
A cloud-scale prediction platform supporting low-latency, high-throughput delivery planning and operational recommendations.
Client
Major global logistics company
Basis
Partner experience, prior role
The insight
“A prediction only creates value if it arrives in time to change a decision.”
Exhibit
Predictions only matter if they arrive inside the planning window
Where the prediction platform sits in the delivery planning loop
- 01 Operational data Shipments, network events, and history
- 02 Feature pipeline Prepared continuously at network scale
- 03 Prediction service Low latency, high throughput
- 04 Delivery planning Predictions consumed inside the plan
- 05 Operational decisions Recommendations acted on in time
↑ Outcomes feed back into operational data
Schematic of the architecture pattern, not a system diagram. Outcomes feed back into the data, so predictions improve as the network operates.
Predictions to drive delivery planning
A major logistics company wanted predictions to drive delivery planning and operational recommendations across its network, at a volume that would make it one of the largest prediction workloads on its cloud platform.
Value depends on arriving in time
Predictions only create value when they arrive in time to change a decision. Delivery planning operates on tight windows, so the platform had to deliver high throughput and low latency at once, and its outputs had to fit directly into the planning process rather than sit in a separate analytics layer.
Engineering for throughput and latency together
We led AI and automation initiatives to design and build the prediction platform on Microsoft Azure, engineering it for the latency and throughput that operational planning required and integrating its outputs with delivery planning workflows.
Predictions at network scale
The platform delivered predictions at network scale and speed, supporting delivery planning and operational recommendations across the company’s operations.
What made the difference
01
Start from the planning window
Latency and throughput requirements were set by when delivery planning needed the answer, not by what was convenient to compute.
02
Engineer for scale
The platform was built on Microsoft Azure to deliver high throughput and low latency at the same time.
03
Embed it in the plan
Predictions fed directly into delivery planning workflows rather than sitting in a separate analytics layer.