Decision Intelligence Suite

Frame the business question: predict weekly SKU-level demand across 400 stores to reduce overstock and lost sales.

Choose a time-series approach, define KPIs (MAPE < 8 %), set data governance policies and model review cadence.

Rank promotions, seasonality, price elasticity, and local events as top predictive signals; drop low-importance features.

Ingest 3 years of POS data, impute gaps from stockouts, engineer lag features and rolling averages.

Benchmark LightGBM, Prophet, and LSTM on held-out quarters; LightGBM wins on accuracy and inference speed.

Run Bayesian hyperparameter search across 200 trials, log all experiments, register best checkpoint with full lineage.

Backtest against 6-month holdout, run fairness checks across store demographics, generate SHAP explainability report.

Ship model as a REST API behind an auto-scaling gateway, monitor MAPE weekly, trigger retraining on drift alerts.