The Challenge
Forecasting and planning ran through manual, disconnected processes with no real-time view of key metrics. Planning, sales and supply chain teams worked from separate versions of the numbers, forecast accuracy suffered, and the organisation reacted to market changes rather than anticipating them.
Each team planning from its own numbers.
Ingestion and forecast cycles run by hand.
Key metrics unavailable when decisions were made.
Market shifts noticed after they had landed.
Approach & Solution
smartData built an AI forecasting engine using machine learning models that generate short and long-term forecasts for finished goods and raw materials from historical data, trends, seasonality and demand signals. Raw material requirements are derived from bill-of-materials consumption, so procurement plans against the same forecast as demand planning.
Data ingestion, forecast generation, review cycles and approvals are automated end to end. Scenario planning lets teams simulate demand cases and assess the impact on materials, and anomaly detection flags market shifts in near real time so stakeholders can respond.
Key Features
Models trained on trends, seasonality and demand.
Raw material needs derived from finished goods.
What-if demand cases simulated before committing.
Change tracking and approval logs on revisions.
Product Showcase
Final Outcome
Planning, sales and procurement teams work from one forecast with a full audit trail across revisions, scenarios can be tested before they are committed, and demand anomalies surface early enough to act on rather than absorb.
