2% of in-stock SKUs met sell-through targets. Of the $25M annual opportunity identified, $10.5M required zero net working capital investment.
Over-assortment and overbuying had created a $15M working capital problem in one category. Just 2% of in-stock SKUs were meeting sell-through targets. Core sizes were systematically underfunded while fringe sizes received excess inventory allocation. All stores received an identical product mix despite a 4x productivity difference between cold-climate and warm-climate fabric demand. Physical cabinet count — the primary space allocation metric — showed essentially zero correlation with store performance (r=0.05), while assortment quality correlated at r=0.84. The planning process was optimizing for the wrong variable.
Profitmind conducted a granular diagnostic of the specific assortment across over 900,000 store-SKU combinations. Every item was classified as Dead, Overbought, Underperformed, or On Target. Store clustering used KMeans (k=4), layered with traffic-band analysis, climate-based demand analysis, and demographic analysis to isolate the performance drivers. Three distinct actionable opportunities were quantified, each with a different working capital profile and implementation complexity.
The total annualized sales opportunity reached $25M, with $13M in excess working capital freed as part of the rebalancing. Of the $14M opportunity, $10.5M required zero net working capital investment — it came entirely from rebalancing warm and neutral fabric overages into cold fabric underages and correcting size curves with existing purchased inventory. The r=0.84 correlation between assortment quality and performance, compared to r=0.05 for cabinet count, establishes the evidence base for shifting space allocation methodology going forward.