A briefing from Enmovil

Demand down 43%.
Steel lead times up.
Every yard day burns.

The squeeze is live on both sides. Demand fell off in February.[5] Steel didn't follow.[3] The math only closes if every function tightens at once.[9]

The hypothesis

The margin gap isn't in the product. It's in the execution lag between what the market is doing and what the plant can see.

Orders dropped fast.[5] Steel didn't. The lag isn't softness, it's inbound, build, yard, and dispatch on separate clocks. Caddie closes the loop.[9]

43% drop
US trailer net orders, MoM Feb 2026. Season −19% YoY.[5]
7.5weeks
Plate mill lead times, Apr 2026. Multi-year highs, still extending.[3]
39%carry cost up
Dealer floor-plan expense per unit, YoY. Yard days carry real cost.[13]
6families
Dump, gooseneck, equipment, tilt, step deck, car hauler. Three markets, three clocks.[4]
100+dealers
North American network. Lead-time variance compounds every planning miss.[2]
3–4weeks
Caddie go-live over existing ERP. API, EDI, or CSV. No migration.[9]
Caddie
Enmovil's agentic AI. Sits above your ERP, TMS, and planning tools.[9] Closes the loop without a migration. Three to four week go-live.
Caddie · Enmovil's AI co-pilot

Six agents. One continuous loop. Over your existing ERP. No rip and replace.

A trailer order enters at Demand Sensing. It exits at Freight Settlement. Autonomously. In minutes. Then the next one enters. The loop runs 24/7.[9]

Demand Sensing
Dealer pull by family, region, season. Long tail included.
Inbound Capacity
Six-month MRP. Sequences intake to mill reality.
Line & Plant
Sequences Mount Pleasant against inbound. Matches build to firm dealer demand.
Dispatch
FTL across 100+ dealers. Compresses lead-time variance.
Multimodal
FTL, LTL, car hauler. One control tower.
Freight Settlement
Audits every invoice. Landed cost per trailer. Closes the margin loop.
Caddie, in Enmovil's own words: “Your AI co-pilot for supply chain orchestration. Unifies planning, logistics, and execution into one autonomous intelligence layer.”[9]
Caddie applied to your operation

Six places Caddie removes the lag. Pick the one bleeding the most.

01 · The inbound question
When a mill slips a steel delivery two days, how long before the Mount Pleasant build sequence reflects it, and how many dealer promises move before anyone calls?
Today
Mills run your plant schedule.
Cost
Plate at 7.5 wk lead[3] · every slip arrives twice: expedite freight + a broken dealer commit.
Caddie
Inbound Capacity rolls six-month MRP. Sequences production to actual inbound, not static plan.[9]
02 · The yard question
Which trailer families sit longest on the finished yard, and at what unit age does a build shift from asset to working capital drag?
Today
Custom-config tail parks longest with the weakest demand signal.
Cost
20–30% holding/yr[12] · ~90 day stale inflection[16] · carry cost up 39% YoY[13]
Caddie
Inventory agent matches build to firm dealer demand. Yard age per unit. Alerts before stale.[9]
03 · The dispatch question
Which trailer, on which truck, to which dealer, in which order. And what happens when a dealer moves a window at 4pm?
Today
Spreadsheet dispatch optimizes the mean and ignores the tail.
Cost
Variance, not average lead time, is the lever.[7] Dealer-absorbed variance returns as floorplan cost.
Caddie
Dispatch agent sequences FTL across 100+ dealers. Auto-routes the 4pm window exception.[9]
04 · The forecast question
Ag, construction, and landscaping don't peak together. Where does the forecast miss hardest on the custom-config long tail?
Today
Three seasonal curves on one weekly forecast. Custom-config tail loses worst.
Cost
Forecast accuracy: hero 80–90%, custom-config tail drops to 30–60% at weekly SKU-by-location.[8] Each gap funds an expedite or parks a unit on the yard.
Caddie
Demand Sensing reads the full distribution. Multi-echelon absorbs the tail without over-building heroes.[9]
05 · The freight question
Across FTL to dealers, LTL consolidation, and car hauler to direct customers. How much carrier invoicing gets audited line by line before payment?
Today
Quarterly reconciliation is late reconciliation.
Cost
5–15% invoice errors · 3–8% of spend recoverable · ~40% from accessorials[10]
Caddie
Freight Settlement audits every invoice continuously. Landed cost per trailer.[9]
06 · The system question
Is demand, capacity, dispatch, and inventory running as one loop, or as separate screens a planner reconciles by hand every week?
Today
Five functions, five screens, one weekly hand-reconcile.
Cost
CRM, inventory, financials never talk until a human pastes numbers[14] · five chances to land a week behind.
Caddie
One agentic layer over your existing ERP. No migration. Three to four week go-live.[9]
Operational impact

Six operating numbers you can measure.

Every number below comes from deployed customer outcomes, not projections. Pick the shape closest to Diamond C's.

Daily ETA accuracy
97%
Road, rail, ocean, air. Deployed base.
Source: Enmovil Caddie
Forecast accuracy
97%
Deployed base, including long tail and custom-build SKUs.
Enmovil deployed benchmarks
Transportation spend
10% down
Measured on the Nestlé dispatch deployment.
Enmovil deployed benchmarks
Planner minutes reclaimed
30 to 50%
Auto-routed exceptions take clerking off the planner's plate.
Enmovil deployed benchmarks
Integration
3 to 4weeks
Over existing ERP. API, EDI, or CSV. No migration.
Enmovil deployment playbook
Dealer lead time variance
Targetcompression
Window variance across 100+ dealers. Validated on your data at the booth.
Enmovil benchmarks
Already deployed

Three customer stories that map onto Diamond C's chain.

No trailer OEM to name yet. Three deployments (auto OEM, commercial vehicle, manufacturer) match the shape closely enough to transfer. Named references at the booth.

India's largest auto OEM
Maruti Suzuki. Dealer dispatch sequencing. 4–5 years live, extended.
Decides which vehicle, on which truck, in which order, for which dealer.
Shape
Plant-to-dealer dispatch at scale. Load planning, route sequencing, multimodal orchestration on one control tower.
Outcome
~$2.15M/yr measured savings. Manual Excel vanning eliminated.
Relevance
Closest deployed analog to Diamond C's plant-to-dealer flow.
“Transfers to Diamond C: one dispatch plan across 100+ dealers, auto-routing the 4pm dealer-window exception.”
Mahindra & Mahindra
Six month material requirements driven inbound capacity plan.
Plant line sequenced against mill reality, not static plan.
Shape
Inbound capacity planned against a rolling six-month MRP. Plant intake sequenced to actual dock arrivals.
Outcome
Plant plan reconciles to inbound reality. Fewer expedites, less working capital on the yard.
Relevance
Same shape as Diamond C's inbound steel reliability problem.
“Transfers to Diamond C: Inbound Capacity agent keeps Mount Pleasant on firm build mix, not static plan.”
Ashok Leyland
Commercial vehicles. Freight Financials, Dispatch Planning, Control Tower.
Mixed FTL, LTL, and car-hauler settlement on one control tower.
Shape
Freight Settlement, Dispatch Planning, Control Tower, courier tracking on one platform.
Outcome
Single settlement path across FTL, LTL, courier. Audit runs continuously, not quarterly.
Relevance
Closest analog to Diamond C's mixed FTL/LTL/car-hauler settlement.
“Transfers to Diamond C: Freight Settlement agent audits every invoice. Landed cost per trailer by destination, carrier, month.”
Scale
~100,000 trucks/day under orchestration. 97% daily ETA accuracy. 97% demand sensing accuracy. Selected over Blue Yonder, Manhattan, Kinaxis, o9, Oracle, and EY in tier-1 evaluations.
Who built this

Enmovil · the technology thought partner for autonomous supply chains.

AI-native platform for demand, inventory, dispatch, execution, settlement, sustainability. Sits above your existing stack.

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

60 to 90 minutes. One problem scoped. Experiments defined before we leave the room.

Not a demo. We come in with a hypothesis on where the lag hurts most. You confirm or correct it. We scope two or three experiments on your data, on your system, against a measurable outcome. Three questions below shape what we bring.

Question 1 of 3
Which execution gap is bleeding the most margin right now?
Finished units aging on the yard at elevated carry cost
Inbound steel timing is running the build schedule more than we are
Dealer dispatch variance. Windows change and the plan is slow to catch up
Forecast misses on custom configurations and the long tail
All of them. They compound into one margin problem.
Question 2 of 3
When a mill slips or a dealer moves a window, how long before the plant build sequence reflects it?
Under 2 hours. We have something that catches it fast
2 to 8 hours, depends on who's watching
8 to 24 hours, typically caught at the daily standup
Longer than 24 hours. Sometimes we find out from the dealer
Varies significantly by trailer family and which dealer is involved
Question 3 of 3
For that function, is the data in your ERP or TMS, or in planner spreadsheets?
Mostly in the ERP. It's accessible but nothing reads it in real time.
Partial. Some in ERP, rest in Excel that gets reconciled weekly.
Primarily in planner spreadsheets. ERP doesn't have the granularity we need.
Mixed by function. Inbound is clean, dispatch and yard are in spreadsheets.
Not sure. That's part of what we need to figure out.

Bring whoever owns inbound, yard, and dispatch. The goal: leave with experiments scoped, not slides reviewed.

Book 60–90 minutes →
At the American Supply Chain Summit · Dallas · Booth [BOOTH_NUMBER]