Building a Demand Forecasting System for Multi-Location Retail with No POS Integration

The article discusses the architecture of a lightweight demand forecasting engine built on manually entered operational data, without POS integration or external feeds. The system aims to provide weekly forward-looking directives to operators.

Details

The proposed demand forecasting system is designed for multi-location retail operations with limited data sources. Operators manually log 4-5 daily signals, including revenue, covers, waste, category mix, and contextual flags. The system aims to provide a weekly forward-looking directive on what to expect, prepare, and order, along with a stated confidence level. \n\nFor the first 30 days, the system will use a statistical baseline with day-of-week decomposition and trend analysis, without any machine learning. After 30 days, it will transition to a light global model, where similar venues are trained together but predictions are made individually. \n\nThe key challenges addressed are handling small data volumes (under 10 venues and 90 days of history per venue) and dealing with sparse, potentially corrupted data. The system will flag and exclude outliers before training, rather than modeling them explicitly or masking and interpolating. \n\nThe confidence intervals need to be interpretable by non-technical operators, so the system is considering lightweight approaches like conformal prediction or quantile regression, rather than full probability distributions.