Predictive Analytics in Supply Chain, Retail and Finance

For most use cases, 1–2 years of historical data is a solid starting point. Demand forecasting with strong seasonality benefits from 2–3 years to capture seasonal cycles. Fraud detection can work with as little as 6 months if fraud events are frequent enough. We assess your data during the audit phase and recommend strategies to work effectively with what you have.

Accuracy varies by problem type and data quality. Our demand forecasting models typically achieve 80–92% accuracy (MAPE basis). Churn models typically achieve 75–90% AUC. No model is 100% accurate — the goal is to be significantly better than current heuristics or manual judgment. We always benchmark against your existing approach and report results honestly before deployment.

Model drift is real, and we design for it from day one. We implement automated monitoring that tracks prediction accuracy over time and alerts when drift is detected. Retraining pipelines are scheduled (weekly or monthly) to incorporate new data. For high-stakes models, we implement champion-challenger frameworks where new model versions are validated before replacing the incumbent.

Rules-based systems are better when your logic is well-understood, rarely changes, and needs to be fully explainable (e.g., regulatory compliance). ML models win when patterns are complex, data volume is high, conditions change frequently, or accuracy gains are commercially significant. In practice, many production systems combine both — ML for prediction, rules for hard constraints.

We build explainability into every model using SHAP values, which show exactly which features drove each prediction. For regulated industries (finance, insurance, healthcare), we choose inherently interpretable models (logistic regression, decision trees) when accuracy trade-offs are acceptable. We also build business-facing dashboards that translate model outputs into plain-language explanations for non-technical audiences.