Transportation Case Study

Business Problem

Without data backed evidence on full cycle transportation cost, and its drivers, investment decisions regarding facility expansion could not be properly de-risked and often became contentious and inefficient. A misstep in expansion could lead to significant and long-term erosion of grower return and a reduction in competitiveness.

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Decisions included:

-      Which location should receive the next round of capital upgrades?

-      Should a new location be opened?

-      What is the impact of uneven utilization? Where and when do queues form, how much do they cost in real dollars, and how could they be mitigated economically?

-      Is the current allocation of trucks to locations optimal? Is it better to drive farther and unload faster or closer and wait in line?

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Root Causes

1) Cost Per Ton varied by station,but drivers of cost remained opaque.
Decisions were made based on averages or anecdote, not measured unit cost or utilization profiles.

2) High peak to average utilization ratios created the appearance of a capacity constraint.
Certain stations experienced sharp utilization spikes that led to long queues, raising per-ton cost, however, adding further capacity would come with a suboptimal return

3) Cost per field wasn’t calculated or visualized.
True cost-to-serve varied dramatically across locations, but this wasn’t captured in decision-making tools.

4) No marginal acreage model to guide growth.
The organization lacked a threshold-based model to show where the next acre would reduce, not grow, profit.

Solutions

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Station-Level Cost Modeling
Oxrow.ai used historical ticket data to model end-to-end truck cycle times across field load, road, and unload segments. Combining the time data with a bottom-up analysis of trucking costs per hour unveiled a full picture of transportation cost and its drivers.

Utilization and Queue Impact Analytics
By comparing peak vs. average arrival windows across stations, we identified where cost spikes were driven by uneven arrival time and not a lack of capacity.

Tactical Insights for Capital Planning
The findings equipped leadership to make better decisions about equipment upgrades and explore possible contingencies, e.g., develop incentives for off-peak deliveries, grounded in economics, not guesswork.

Integrated Field-Level Cost Modeling
We combined field, station, and factory data to model the full journey—from harvest to delivery. Using cycle time and a total cost of trucking, we create data per-field cost map that revealed true contribution margin per location.

Heatmaps and Center-of-Mass Analysis
Oxrow.ai developed intuitive heatmaps and centroid-tracking tools to visualize how the footprint of acreage was drifting over time—and at what cost.

Scenario Planning for Expansion Decisions
Five acreage growth scenarios were modeled, showing how additional volume would impact total transport costs based on receiving station.

Actionable Tools, Not Just Insights
Decision-ready dashboards and spatial models that could be embedded in station expansion decisions and operational workflows.

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‍The Oxrow.ai Edge
By treating transportation not just as a cost, but as a performance system with levers, Oxrow.ai turned a high-friction area into a controllable advantage. When 15-minute delays can add up to cost six figures over harvest, clarity creates confidence. Imagine what happens when the client layers on Ai to this workflow.

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