Railcars, Silos, Cash Flow - The AI Playbook Every Industrial Portco Can Copy-Paste


Summary
Nine weeks, two data scientists, and off-the-shelf cloud tooling turned a 100 year old commodity exporter into a data driven price setter. If you own businesses where timing and inventory tie-up kill cash flow, this solution is repeatable.
Problem
When you move grain at the scale of a top 15 US commodity exporter, timing is EBITDA. Yet until recently this 100-facility network relied on spreadsheets and instinct to decide when to buy sorghum, corn, soybeans, and wheat. The result was they routinely over-stocked during price spikes, tying up working capital in over-filled silos, and still sent sales reps chasing growers who weren't ready to sell. Every harvest cycle this timing tax shaved margin and strained rail, truck, and barge schedules.
Five years of transaction, elevator, and weather data told the same story. Purchase orders clustered near seasonal highs and left the company holding inventory months longer than necessary. Average purchase price ran almost four points above spot, storage costs ballooned, and volatility made hedge decisions little better than coin flips. Worse, sales teams burned hours phoning farmers during the exact weeks growers preferred to wait for better prices.
Solution
My team stepped in with a two person strike team: one senior data scientist and one junior. In the first week we audited data sources and piped five years of history into our platform. Over the next four weeks we trained LSTM neural networks that could read the weather shifts, futures curves, and shipment rhythms that drive harvest swings. By week seven the models retrained themselves nightly, drift alerts went live, and forecasts flowed directly into the procurement dashboard. Two short weeks later, the ninth Monday after kickoff, the entire system was in production.
Impact
Our system predicts which growers are likely to sell within ±15 percent of weekly volume. This allowed procurement to buy orders earlier, when growers were eager and prices soft. Average purchase price fell low single digit percentage points versus the prior harvest, inventory turns jumped by roughly the same order of magnitude, and unplanned logistics spend dropped materially. Sales reps, armed with the same signals, stopped cold calling and started closing when growers actually wanted to move grain.
Cutting carry costs is a margin play but it also frees cash that can flow to capex, bolt-on acquisitions, or straight to the balance sheet, outcomes any PE operator will recognize. Just as important, the neural network backbone is domain agnostic. Any portfolio company that buys seasonal inputs (fertilizer, metals, chemicals) can drop its data into the same architecture and mitigate the timing tax on your EBITDA.
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