How I Built Smart Inventory Forecasting
Forecasting designed around inventory decisions, planning routines and executive visibility rather than model accuracy alone.
Smart Inventory Forecasting was built to help teams anticipate stock risk before it became an operational problem. The aim was to move from reactive inventory management to a planning view that combined demand patterns, stock positions and replenishment signals.
How I Built It
The work started with the decisions planners needed to make: what to replenish, what to hold, what to investigate and what to escalate. From there, the data model brought together historical demand, current inventory, lead times and planning parameters.
Forecasting logic was paired with Power BI views so users could see not only a projected number, but also the exception list behind it. The dashboard highlighted inventory risk, potential overstock, demand movement and items requiring planner review.
What It Does
The tool supports forecast review, inventory risk prioritisation, exception management and leadership reporting. It gives teams a more structured way to focus attention on the SKUs and categories where action matters most.
What I Learned
Forecasting tools should be judged by the decisions they improve. A model can be mathematically strong and still unused if it does not fit the planning routine. The interface, trust layer and operating cadence matter as much as the algorithm.