Predictive modelling

Demand Planning on Microsoft Fabric

Name: Demand Forecasting
Brand: MII Microsoft

In trial

Demand forecasting built end-to-end on Microsoft Fabric with operational and IoT data.

Business value

Fewer stock-outs, lower waste, better inventory planning, and a shift from reactive reporting to proactive demand decisions.

Planners often react to demand after it changes because operational, sales, and sensor data are scattered across systems, which creates stock-outs, excess inventory, and planning decisions that depend too much on manual spreadsheets. Demand Forecasting is an MII-built capability pattern that unifies operational and IoT signals, prepares planning-ready datasets, trains forecasting models, and presents Power BI dashboards that show expected demand, patterns, and exceptions for planners to act on. Forward-looking demand forecasts, planning dashboards, trend visibility, and exception signals are delivered from one governed platform, so planners can act before shortages or overstock happen.

Where it helps

Forecasting demand from unified operational and sensor signals
Comparing scenarios and testing planning assumptions interactively
Generating alerts for stock-outs and overstock conditions before they occur

What the pattern does

End-to-end forecasting pipeline on Microsoft Fabric
Integration of operational, sales, and IoT data streams
Interactive planning dashboards and exception alerting in Power BI

The challenge

Planners often react to demand after it changes because operational, sales, and sensor data are scattered across systems. This creates stock-outs, excess inventory, waste, and planning decisions that depend too much on manual spreadsheets.

What was built

The solution unifies operational and IoT signals, prepares planning-ready datasets, trains forecasting models, and presents Power BI dashboards that show expected demand, patterns, and exceptions for planners to act on.

What changed

Forward-looking demand forecasts, planning dashboards, trend visibility, and exception signals are delivered from one governed platform so planners can act before shortages or overstock happen.

Why it matters