AI Inventory Management and Demand Forecasting
Forecast demand per SKU and location, recalculate reorder points as conditions change, and see stockout and overstock risk before it costs you. Workisy turns your stock history into ranked buying and rebalancing decisions your planners can act on each cycle.
Features
Powerful Capabilities, Built for Scale
Every tool you need to run a world-class operation, from day one to enterprise scale.
SKU-Level Demand Prediction
Forecasts are generated per SKU and per location from sales history, promotional calendars, and observed velocity shifts. Slow movers and intermittent-demand items use different statistical treatment than fast-turning lines.
Reorder Point Optimization
Reorder points and order quantities are recalculated as demand and lead times move, instead of sitting at values someone set two years ago. Each recommendation shows the service level it is engineered to hold.
Stockout Risk Scoring
Every item carries a forward-looking probability of running out inside its replenishment window, ranked by revenue exposure. Buyers work a prioritized list rather than scanning a full stock report.
Overstock and Aging Alerts
Excess coverage, dead stock, and items trending toward obsolescence are surfaced with the capital tied up in each. Suggested actions include markdown timing, transfer between locations, and order cancellation windows.
Seasonality Modeling
Recurring seasonal peaks, holiday effects, and day-of-week patterns are learned automatically from multi-year history. Buy plans lift ahead of a season instead of reacting once shelves are already empty.
Supplier Lead-Time Variability
Actual delivery performance is tracked per supplier and per item, so safety stock reflects real variability rather than the quoted lead time. Unreliable suppliers are ranked by the buffer cost they impose.
Multi-Location Balancing
Stock imbalances across warehouses, stores, and channels are detected and resolved with transfer suggestions before new purchase orders are raised. Transfer recommendations weigh freight cost against expected margin recovery.
Working Capital Simulation
Scenario modeling shows what different service-level targets, order frequencies, or supplier terms would do to inventory value and cash. Planners can compare options before committing to a buying policy.
How It Works
Up and Running in Three Simple Steps
Ingest Stock and Sales History
Connect item master data, movement history, purchase orders, and supplier records. Two or more years of history gives the seasonal models the most to work with.
Train and Segment
Items are segmented by velocity, margin, and demand pattern, and each segment gets the forecasting approach that suits it. Service-level targets are set per segment or per location.
Act on Recommendations
Planners receive a ranked replenishment and rebalancing list each cycle, approve or adjust it, and the outcomes feed straight back into the next forecast.
A closer look at Workisy AI inventory management
Forecasting is the decision layer above your stock records
Knowing what is on the shelf is a solved problem. Knowing what should be on the shelf in six weeks is where most operations still rely on intuition, a spreadsheet, and minimum levels that nobody has revisited since they were entered. Workisy separates these concerns cleanly: the operational record stays where it belongs, and prediction runs on top of it.
Stock balances, movements, batches, and the invoicing attached to them continue to be handled by inventory and billing. The AI layer reads that history and returns recommendations — order quantities, reorder timing, transfer suggestions — without altering how any transaction is recorded.
Why static reorder points quietly cost money
A fixed minimum level encodes an assumption about demand and lead time on the day it was set. Demand shifts, a supplier slips from three weeks to five, a product moves from growth to decline, and the number stays put. The result is the familiar pairing of empty bins on the lines customers want and warehouse space consumed by items that stopped selling months ago — both symptoms of the same stale parameter.
Recalculating those parameters against current demand and observed supplier performance addresses both at once. Our guide to inventory and billing management covers the operational groundwork, while AI for logistics and supply chain looks at the wider planning picture.
Built for multi-location, multi-channel operations
Single-warehouse forecasting is straightforward. The difficulty appears when the same item sells at different rates across stores, regions, and online channels, and a shortage in one place coexists with excess in another. Location-level forecasting plus transfer recommendations resolves that imbalance before a purchase order is raised, which is usually the cheapest fix available.
Sector-specific configurations are outlined for retail and hospitality and for manufacturing, where component dependencies drive the planning horizon. To review your own demand history with our planning team, arrange a consultation.
Results That Speak for Themselves
Measurable Impact on Your Business
Fewer Stockouts
Lower Carrying Cost
Forecast Granularity
FAQ
Frequently Asked Questions
Ready to See It in Action?
Book a personalized demo and discover how Workisy can transform your operations in weeks, not months.