Mid-Market Manufacturer Improves Forecast Accuracy by 22%

22%

improvement in forecast accuracy

$380K

estimated annual savings

6 weeks

from data to production

Overview

A $40M components manufacturer was using spreadsheet-based demand forecasting, leading to stockouts and excess inventory. We built an ML-driven forecasting system integrated directly into their ERP.

The Challenge

Forecast errors were causing 8–10% inventory overstock and regular stockouts on high-velocity SKUs, costing the business roughly $600K/year in carrying costs and lost sales.

Our Solution

We built a gradient boosting model trained on 4 years of sales, seasonality, and supplier lead time data. The model runs weekly, outputs are pushed to their ERP via API, and a Power BI dashboard shows forecast vs. actuals with confidence intervals.

Services & Tools

AI & MLMLOpsForecastingERP Integration

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