The Client's Problem
A mid sized manufacturing enterprise was losing significant margin to supply chain inefficiency overstocking in some regions, stockouts in others, and a forecasting process that relied almost entirely on last year's numbers adjusted by gut feel.
They had data. Lots of it. What they lacked was a system that could turn it into actionable predictions before decisions needed to be made.
What We Built
We designed a temporal forecasting pipeline that ingested three years of historical order data, external signals (seasonality, regional events, supplier lead times), and real time inventory levels across 12 warehouse locations.
The output: a rolling 90 day demand forecast per SKU per region, updated weekly, surfaced through a dashboard their procurement team could actually use without a data science background.
The Results
- 40% reduction in overstock waste within the first two quarters
- 23% fewer stockout events compared to the prior year
- Procurement decisions moved from reactive to 6 week proactive planning cycles
What Made It Work
The model itself was not exotic a well tuned gradient boosting model with temporal cross validation. What made it work was the investment in data cleaning, feature engineering, and stakeholder alignment before a single prediction was made.
Most AI projects fail in the 60 days before the model is built, not after. Getting the data right, and getting the team to trust the outputs, is where the real work happens.
If your business is sitting on data but not acting on it, that's exactly the gap we're built to close.