How the Routing & Staggering Engine Works

A set of planning and order-creation capabilities built to make seasonal and promotional execution more consistent, explainable, and scalable -- standardizing a decision planners previously made by hand into a repeatable, self-service workflow. ICC Flow Builder was ultimately handed off at the enterprise level for direct integration into Walmart's ordering systems, moving it from a standalone planning aid into a connected step in the order-creation pipeline.

250+

business partners reached

$5M+

supply chain savings

17%

on-time delivery improvement

$100M+

delivery risk mitigated

6.6K

annual planning hours saved

Workflow, at a Glance

Flow diagram: item, store, and case data enters a routing check for full-truckload volume. Full loads ship direct. Partial loads are consolidated, assigned to one of four lead-time-based waves, and smoothed across multiple order dates. Both paths produce a ready-to-execute routing and date plan. An automated data refresh continuously feeds current lead-time and routing data into the routing check and wave-assignment steps.

A simplified view of the routing and staggering logic. Exact thresholds and data sources are intentionally generalized for this public one-pager.

How It Works, Behind the Scenes

In general terms, the tool checks whether a shipment's volume is large enough to fill a truck straight to its destination. If so, it ships direct. If not, the remaining volume is consolidated, grouped into one of four lead-time-based waves, and its order dates are smoothed across several days so volume doesn't spike on any single day. That smoothing step runs on a greedy, constraint-based scheduling algorithm: it sorts shipments by target date and assigns each one to the earliest day that still has capacity, so no single day gets overloaded. An automated data refresh runs continuously in the background, keeping the lead-time and routing data behind these decisions current -- so the logic never depends on a static, aging spreadsheet.

Technology Behind the Tool

JavaScript
Python (Flask)
SQL-based data warehouse integration
Automated twice-daily data pipeline
Greedy, constraint-based scheduling algorithm
Automated test coverage (unit + end-to-end)
Enterprise single sign-on
Containerized cloud deployment

Capabilities Demonstrated

Voice of customer
MVP validation
Product operations
Workflow automation
Requirements definition
Full-stack tool direction
Cross-functional partnership with engineering
Operational transformation