iData success story
An analytics layer connected demand, inventory, and sell-out to prioritize assortment and replenishment with greater precision.
Context
The challenge
The commercial operation had to respond to high variability by season, region, and product mix. Teams worked with partial readouts of inventory and demand, leading to overstocks in some references and stockouts in others.
Objective
What the client wanted to achieve
An analytics layer connected demand, inventory, and sell-out to prioritize assortment and replenishment with greater precision.
Solution
How the response was structured
A commercial analytics layer was designed with forecasting for prioritized product families, inventory dashboards, and sell-out visuals for weekly planning. The model aligned purchasing, distribution, and commercial management around a single readout of demand and rotation.
Impact and outcomes
Quantitative impact
Qualitative impact
Closing
The value that was put in place
An analytics layer connected demand, inventory, and sell-out to prioritize assortment and replenishment with greater precision.
