UPS ORION route optimization (INFORMS Franz Edelman 2016) — at full deployment ~$300-400M/yr savings, 100M fewer miles, 10M fewer gallons fuel

Claim: UPS ORION route optimization — built on telematics, ~250M address data points, and map/GPS data (public and proprietary) — was estimated at full deployment to save $300-400M annually, ~100M fewer miles/yr, ~10M gallons fuel/yr, ~100,000 metric tons CO₂/yr. Project cost ~$250M; had already saved >$320M by Dec 2015; 6-8 mile/day reduction per driver.

Source: INFORMS 2016 Franz Edelman Award (https://www.informs.org/Impact); corroborated by bsr.org.

Confidence: Verified.

Caveat: UPS is a large public company, used here as a mechanism illustration of route-optimization-on-mixed-public-and-proprietary-data, not as an SMB.

Why this matters for Candid: Establishes the mechanism. For an Ontario SMB the same principle scales down: a delivery business that optimises routes against free map data + its own scheduling history is operating in the same shape, even if the savings are five orders of magnitude smaller. See R2 — Build only on data you already own — transaction history, CRM, scheduling, no-show patterns; that is the only category with native defensibility.