Retail Demand Forecasting with Custom ML Models

  • Services:
  • Industries: Enterprise retail
  • Engagement: Build
  • Timeline: Q1-Q2 2024

About the project

A European enterprise retail chain with hundreds of locations and a large e-commerce catalogue was losing margin to inaccurate demand forecasts. Overstock tied up capital; stockouts pushed customers to competitors. We designed and shipped a machine learning development example that the client's data team now runs in production: a gradient-boosted forecasting pipeline integrated into their existing ERP, reducing mean absolute percentage error by about 35% within the first quarter of operation.

Background

The retailer had relied on rules-based forecasting for over a decade. Seasonal spikes, promotional events, and supply-chain delays created systematic blind spots the legacy system could not handle. The data team had experimented with off-the-shelf forecasting tools but found them too rigid for their SKU mix of over 50,000 items. The constraint was clear: build something that fits their data schema, trains on their historical patterns, and exports predictions the ERP can consume without a middleware rewrite.

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What we built

A production-grade machine learning development example: an end-to-end demand-forecasting pipeline built for enterprise retail scale.

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Feature engineering pipeline

We extracted over 40 features from raw ERP exports including promotional calendars, weather proxies, and lagged sales at multiple time horizons. Automated feature validation catches schema drift before it reaches the model.

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Incremental model training

LightGBM models train nightly on a rolling 18-month window. Incremental training keeps compute costs flat as the catalogue grows, with no full retraining needed unless a model accuracy threshold is breached.

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ERP-native output format

Forecast files match the ERP's existing CSV intake schema exactly. The operations team saw no change to their planning workflow, which accelerated adoption and removed the need for middleware translation.

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Monitoring and drift alerts

A lightweight monitoring layer tracks prediction accuracy against actuals each week. When error climbs above a configurable threshold the system flags the affected SKU clusters for manual review before the next planning cycle.

Takeaway

This project shows how a well-scoped machine learning development example can deliver measurable operational value without displacing existing systems. We started from the client's data constraints, built incrementally, and shipped something the ERP could consume on day one. If your team has historical transactional data and a forecasting problem that rules-based tools are not solving, a focused ML build often pays back faster than a platform migration.

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