Tens of thousands of slow- and fast-moving SKUs, intermittent spare-parts demand and relentless fill-rate pressure. DemandIntel right-sizes every line on your own data, so you free cash without risking service.
An industrial or MRO distributor carries a huge catalogue, tens of thousands of lines, most of them slow movers. Blanket reorder rules over-stock the long tail and under-stock the movers at the same time, so cash is frozen and fill rate still slips.
The hard part is not the fast movers; it is the parts that sell a few times a year against a fill-rate promise you cannot break. That takes per-SKU demand modelling, not a one-size rule.
Tens of thousands of lines, most slow-moving. Blanket rules over-stock the tail and starve the movers at once.
Many parts sell a handful of times a year. Classic forecasting smooths them to zero or over-buys them.
A stockout on a critical part can halt a customer’s line. Service commitments force buffers you have to size, not guess.
Long, variable import and manufacturer lead times make the reorder point a moving target, not a fixed number.
Cash is spread thin over thousands of lines; a little over-stock per SKU adds up to a large frozen total.
Stock split or centralized across branches. Every network change invalidates yesterday’s settings.
US wholesalers' inventory-to-sales ratio in late 2022, the highest sustained level since the 2008-09 crisis
of inventory held by US merchant wholesalers (August 2026)
Source: U.S. Census Bureau, Monthly Wholesale Trade (FRED series WHLSLRIRSA), 2022-2026.
DemandIntel forecasts intermittent, per-SKU demand with the right model for slow movers, so the long tail stops silently absorbing cash. Safety stock and reorder points are sized on real error and lead-time variability, line by line, and tuned to your fill-rate target rather than a blanket rule.
Because the settings are reproducible and auditable, a buyer can see exactly why a part’s reorder point moved, and defend it. It reads your ERP or WMS data, works across branches, and runs on-site.
The outcome is the one that matters to a distributor: less cash frozen in the tail, protected service on the parts that matter, and a stock policy your team can actually explain.
That is the core strength. DemandIntel models intermittent demand per SKU, so you right-size the long tail instead of applying a rule that misses it both ways.
Safety stock is sized to your service target on real error, so you protect fill rate on critical parts without blanket over-stocking.
It plans across branches and re-optimizes when you consolidate or redistribute, so a network change does not silently break your settings.
It accounts for lead-time variability directly, so the reorder point reflects reality instead of a static assumption.
Picture your next S&OP meeting: one number on screen that nobody argues with. See it free, on your own data, in 45 minutes; and if it does not hold up, we part as friends.