A briefing from Enmovil for
Procter & Gamble

We can help you with smarter logistics.

Fill every truck. Plan every mile with AI. Scale it across eight thousand lanes.

(Your Canadian retailer pilot on slashing empty return miles is awesome. This is how you replicate it across the network.)

The scale you run. The goal you committed to.

Two numbers that have to meet.

TODAY · NORTH AMERICA
8,000 lanes
  • 800,000 shipments a year
  • 80 carriers in rotation
  • 110 plants, 200 distribution centers globally
  • 3.9M tonnes carbon dioxide equivalent per year, upstream freight
BY 2030 · PUBLICLY COMMITTED
-50% intensity
  • Upstream freight emissions, halved
  • ~4% on the board as of the last disclosure
  • 46 points to go, five years left
  • Today's planning cadence can't close this gap

How do you close 46 points of emissions intensity across 8,000 lanes, without hiring a 1,000-person planning org?

By better asset utilization. We can help. Here's how.

The operating layer · Caddie, Enmovil's agentic AI.

Six agents. One continuous loop. Works with Kinaxis, Maersk, your private fleet. No rip and replace.

Caddie takes the plan you already have, turns it into minute by minute dispatch decisions, and writes the outcome back to your system of record. A shipment enters at Pairing, exits at Settlement, autonomously, in minutes. Then the next shipment enters. The loop runs 24/7.

Pairing
Finds retailer backhauls for your outbound loads.
Modal Intelligence
Picks road-pair vs rail vs solo per load.
Onboarding
Integrates new retailers in weeks, not months.
Dispatch
Renegotiates pairs in real time when schedules slip.
Dock & Yard
Sequences truck arrivals so dock capacity holds.
Settlement
Splits cost with retailer, attributes emissions per load.
Foundational economics

This is the math that unlocks hidden margin. Then compounds toward your 2030 commitment.

Every number ties to a primary public source. ATRI for deadhead and per-mile cost. EPA SmartWay for diesel emissions. AAR for rail vs road. P&G's own disclosed pilot for the anchor. Pull the sliders. See what one lever (collaborative freight) delivers on its own. Then compound with planning, warehousing, and modal shift on the same platform.

Full methodology, formulas, assumptions, and an Excel-ready model are maintained separately. Every assumption is vettable.

Open the calculator in a new tab for a full-width view.

Start at Iowa City

Five retailer DCs inside one dispatch radius. A $150M expansion in the ground. The template for forty more plants.

Pair economics are a density problem. Iowa City has the density. No other P&G plant does.

5
Top-10 retailer DCs in a 300-mile dispatch radius
$150M
Sept 2025 expansion, Oral-B line reshoring
780K
Square feet. Built 1956. Six iconic brands.
40+
Other P&G NA plants that share this profile. Iowa City is the template.
Inside the 300-mile radius
Hy-Vee Chariton DC
~80 miles
Target Cedar Falls
~100 miles
Walmart Spring Valley
~140 miles
Walmart Belvidere
~210 miles, automated
Meijer (regional)
IL expansion 2025

Megan Katic, Director of Beauty Care Supply Chain, has publicly listed space optimization, automation, and AI as her priority challenges. The Dock and Yard Agent is the direct answer.

The why

Five things have to be true. For you, all five already are.

01
Planning stack in place. Kinaxis, Maersk, APAL, private fleet.
02
Every major NA retailer on speed dial. Density exists.
03
8,000 lanes. Machine learning pair match becomes necessary.
04
Canadian pilot already worked. Feasibility is answered.
05
2030 target is public. Freight is the biggest lever you have.
The how

Four stages. Each one compounds the last.

Q1 2026
3pairs
Replicate the unit
Two retailers. Beauty Care at Iowa City.
H2 2026
50pairs
Pan-North America
Five retailers. Fabric Care onboarded.
2027
200pairs
Cross-category density
All five categories live.
2028
500+pairs
P&G controls the rails
Mondelez, Unilever, Kraft Heinz pay to plug in.
Who's building this

Enmovil · the technology thought partner for autonomous supply chains.

One AI-native platform. Demand sensing, inventory, dispatch, execution, freight settlement, sustainability reporting. All on one intelligence layer that sits above the systems you already own.

Under orchestration
~100K
Trucks per day across the customer base
Daily ETA accuracy
99%
Road, rail, ocean, air
Forecast accuracy
97%
Demand sensing on deployed customers
Logistics cost savings
8 to 15%
Measured across deployments
Integration
3 to 4wks
On existing SAP, Oracle, TMS, WMS. No migration.
Deployed at
Dispatch Planning
Multimodal Logistics
Multimodal Orchestration
Dispatch Planning
Fleet Management
Logistics Orchestration
Logistics Resilience
Inventory Management
Freight Settlement
Dispatch Planning
Transport Management
Export Planning
Runs under GDPR and SOC 2. Data ingestion via API, EDI, or bulk upload. Enterprise SSO. Deploys over existing SAP ECC 6.0 and above.
The ask

A thirty-minute working session. Not a demo.

We leave the room with three scoped experiments and a shared view of what the Supply Chain 3.0 execution layer looks like next quarter. Three questions we'd most want to ask you before the session. Your answers tell us what experiments to bring to the table.

Question 1 of 3
On scaling the Canadian pilot, where did the real friction show up?
Carrier willingness
Data integration with retailer systems
Network-level planning across lanes
All three
We haven't tried to scale it yet
Question 2 of 3
At the 780,000-square-foot Iowa City facility, which constraint is hardest to move today?
Receiving dock capacity
Pick-face density
Cross-dock lane utilization
Outbound truck sequencing
Question 3 of 3
With 7,000 non-manufacturing roles exiting, which carrier-enablement workflows are most at risk of slipping?
Onboarding new carriers
Carrier performance review
Freight audit and settlement
Exception routing
Book 30 minutes →
At the American Supply Chain Summit · Dallas · Booth [BOOTH_NUMBER]

Why we believe this works at scale. Uber Freight reported at Deliver 2025: -38% scheduling time, -15% overdue loads, ~-80% delay duration with agentic AI [5]. That architecture works at the broker layer. Moving it to the shipper unlocks the collaborative-miles opportunity.