Data Science in Predictive Analytics & Forecasting

Any metric with enough historical history to reveal a pattern — sales, demand, revenue, equipment failure risk, patient load, deposit flows — is a candidate. We confirm feasibility against your actual data during the readiness assessment.

It depends on the time horizon and seasonality of the metric, but as a rule of thumb we look for at least two to three full seasonal cycles of clean historical data. We assess this directly against your data rather than quoting a generic minimum.

Accuracy varies by problem, but we report it honestly using backtested metrics against your own historical data, alongside a confidence range for every forecast, rather than a single number presented as certain.

Built-in ERP forecasting typically uses a simple trailing average or linear trend. Our models incorporate seasonality, external drivers and confidence intervals specific to your business, and are validated against your real historical performance before go-live.

Most engagements run 6 to 12 weeks from discovery to dashboard handover, depending on data readiness and the number of metrics being forecast.

Yes, where the underlying data supports it. Granular forecasts require more historical volume per segment, which we assess during the readiness phase.

Our monitoring tracks forecast accuracy against actuals and flags material deviation, triggering a recalibration so the model adapts rather than continuing to predict against an outdated pattern.

No. The dashboard is built for self-service use by planning, finance or operations staff, with training included as part of handover.

Yes. We build data pipelines that pull directly from your existing systems rather than requiring manual data exports.

It lets your team adjust an assumption — a price change, a new region, a 15% demand drop — and immediately see the projected impact on the forecast, before committing budget to that decision.

It tells you the realistic range of outcomes rather than a false single number, so you can plan for the low end (avoid a stockout) and the high end (avoid overordering) instead of betting everything on one guess.

It is harder, and we say so upfront. In these cases we typically use analogous product or market data combined with a wider confidence range, and recalibrate quickly once real sales data starts coming in.

You do. Source code, model documentation and dashboard configuration are handed over in full, along with training so your team is not dependent on us for routine operation.

Retail and distribution, banking and finance, manufacturing, agriculture, healthcare, education and government — the techniques transfer across sectors; what changes is the specific driver set and seasonality pattern.

We offer 30/60/90-day post-launch support as standard, with optional retainer arrangements for scheduled recalibration, new metric additions, or dashboard enhancements.

We start with the simplest method that can meet the accuracy requirement, because a transparent model your finance team can audit is usually more valuable than a marginal accuracy gain from a black box. We only reach for a more complex approach when the data genuinely requires it.