Pulse
Unified data. Reliable forecasts.
Forecasting for businesses that don’t fit a template.
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Designed to reflect how your business actually operates, even when signals and data aren’t uniform.
Forecasts adapt as conditions change, increasing confidence as the system learns from real outcomes.
Move beyond projections to guidance that informs real operational decisions.
How can AI help us manage and track complex inventory with thousands of SKUs across multiple locations?
No. Many organizations begin by unifying data from existing systems, like ERP, POS, and e‑commerce, into a common model, comparing AI demand forecasts against current forecasts on a few categories, and expanding once they see accuracy and operational impact.
AI can automatically model demand at a very granular level (for example SKU–store–day) and group similar patterns so each segment gets an appropriate forecasting approach. This lets you maintain accurate, scalable forecasts across thousands of SKUs, stores, or customers without manually tuning models for every single item.
AI demand forecasting can update much more frequently than traditional planning, because it continuously ingests new sales, channel, and external signals. That means it can detect emerging patterns (a spike after a promo, a slow start on a new SKU, a regional surge) in days or even hours, and refresh forecasts so planners can adjust orders, production, and allocation before issues show up as stockouts or excess.
AI demand forecasting models detect patterns in sales, seasonality, and promotions at SKU/location level, so you can set smarter reorder points and safety stock instead of relying on averages. That tighter alignment between inventory and real demand reduces stockouts on fast movers while cutting overstock and markdowns on slower items.
AI demand forecasting can translate predicted demand by store, channel, and time of day into expected workload, giving workforce planners a clearer view of when and where they’ll need people. You can feed those demand curves into scheduling tools to set smarter staffing levels, reduce overstaffing during slow periods, and prevent understaffing during peaks—improving service levels without blindly adding labor.
