# Machine learning improves a medical-device supplier's sales forecasting

> A private-equity-backed orthopaedics and medical-device supplier · Industrial & Aviation

A seasonality-based machine-learning model cut sales-forecast error by around a third and extended forecasting to 18 months, optimising stock across international markets.

## At a glance

- **31%** reduction in absolute sales-forecast error

## What was the problem?

A private-equity-backed supplier of orthopaedic and healthcare products to hospitals across several international markets wanted more accurate sales forecasts to improve supply-chain performance, using historical trends to forecast future sales automatically.

## What did QuantSpark do?

QuantSpark began with exploratory analysis of the supplier's historical sales, uncovering strong seasonal patterns unique to each market and product that the business had not previously identified. A machine-learning model then decomposed historical sales into seasonal patterns, growth trends and residual variation, extrapolating these to forecast sales up to 18 months ahead. A simulation method generated confidence intervals, and the logic was packaged into a lightweight, robust tool for the client's ongoing use.

## What changed?

The model reduced absolute forecast error by around 31 per cent against the business's existing forecasts, while extending the forecasting horizon to 18 months with minimal effort. More accurate forecasts optimised stock levels, cut waste and freed staff from manual forecast preparation.

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