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Project
From Routes to Results: Tracking Strategy Implementation and Savings

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Project Overview
C.H. Robinson (CHR) has a network modeling team that recommends network changes for shippers—closing or consolidating warehouses, shifting routing patterns, and tuning the truckload/LTL mix.

Measuring how those long‑horizon decisions perform after go‑live is hard: demand shifts, freight markets move, and local execution can drift from the original plan.

CHR worked with a student team at Northwestern to start to tackle this problem.

The student team framed this as a repeatable analytics problem: build tools that normalize for volume changes, compare “before vs. after” performance, and highlight where implementation is on track—and where it isn’t.

Solution
The team delivered three modular, Python-based tools designed to be re‑run with updated data and shared via Jupyter notebooks/CSV:

  • Savings Model — normalizes spend by volume and computes total and per‑pound cost across supplier→DC, DC→customer, and end‑to‑end legs to track realized savings over time.
  • End‑to‑End Reallocation Model — estimates cost impact for destinations affected by warehouse closures/reassignments (e.g., by ZIP prefix), surfacing regional cost hotspots.
  • Shipment Method Tracker — checks alignment to routing guidance (e.g., TL vs. LTL by weight/distance bands) and flags where mode choices could be improved.

Across a multi‑year data set, the tools showed where network‑wide savings were achieved, where specific regions experienced cost pressure after reallocation, and where mode selection adherence could unlock additional value. Together they provide a practical “monitor and improve” loop for strategy implementation.

Client Perspective

“We gave Amrita, Luis, Gabi, and Misha a challenging mandate—to measure the long‑term success of network design recommendations. They got up to speed quickly, clarified the problem, and brought forward pragmatic ideas we can take into the next phase. This was a strong first step toward a robust solution.”
— Ryan Bischoff, C.H. Robinson

Deliverables

  • Three reusable Python models (with documentation) for savings tracking, reallocation impact, and shipment‑method adherence.
  • Clean input templates and example outputs (CSV) so CHR teams can refresh analyses as new data arrives.
  • A short playbook outlining how to operationalize the tools in quarterly network reviews and implementation check‑ins.

 

Student Team: Amrita Natarajan, Luis Diaz, Gabi Pedrazzani, Misha Melnichuk
Faculty Advisor: Izzy Grosof
Client Team: Ryan Bischoff, Daniel Montoliu, Jenny Makarov, Ryan Hammett

For more information on C.H. Robinson, see their website.