Build a daily demand forecast for 5,000 products across 200 stores. How would you approach it?

What the interviewer wants to hear

• Understand use: decision, horizon, granularity, cost of errors
• Baselines: seasonal naive, moving averages
• Global models (gradient boosting) with lag, calendar, price, promo features
• Handle intermittent demand, new products, stockouts
• Time-based backtesting and probabilistic forecasts

Model answer

First I learn how the forecast is used: if it drives store replenishment, I need daily forecasts per product and store, a horizon matching lead times, and the cost of overstock versus stockouts. I build simple baselines first, like seasonal naive using the same weekday last week. Then I train one global model, often gradient boosting, across all series with features such as lagged sales, rolling averages, day of week, holidays and festivals, price, promotions and store attributes. A global model shares patterns, which helps sparse products. I treat stockouts carefully, since zero sales during a stockout are not zero demand. For new products I use similar-item features. I evaluate with rolling time-based backtests using metrics like weighted MAPE, and produce quantile forecasts because replenishment needs uncertainty, not only a point estimate.

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