Predict stockouts per SKU per location, not after
Every SKU at every location has a stockout probability for the next 7, 14, 30, and 60 days — and reorder triggers fire before the customer hits 'add to cart' and finds nothing.
The problem
A 200-location retail brand with 10,000 SKUs runs at a 12-25% SKU stockout rate at any given time. Each percentage point is real money: total revenue loss runs $1-5M per month. The default operating mode is reactive — the inventory analyst sees a POS-zero signal, files a reorder, and the shelf is empty until the truck arrives. Demand forecasting platforms (PredictHQ, Singuli, Inventoro, Toolio, RELEX, Logility, Kinaxis, Blue Yonder, ToolsGroup) and retail-specific demand-planning enterprise tools (Anaplan Retail, Oracle Retail, SAP IBP Retail, Manhattan Associates) exist for this problem — they are built for enterprise supply chains with corresponding price tags. AI inventory-optimization newcomers (C3 AI, Onebeat, Increff, StockIQ) bring better models but still leave the multi-location, multi-channel coordination to your team. None of them produce 2 million SKU-location-day forecasts daily and tie those forecasts to marketing actions.
What success looks like
Every SKU at every location has a stockout probability forecast for the next 7, 14, 30, and 60 days. Forecasts use multiple model families — Prophet for seasonality, LSTM for non-linear patterns, XGBoost for tabular signal mixes (promotions, weather, local events), ARIMA for stationary signals, plus a reorder-point calculator with lead-time, safety-stock, and service-level constraints. Reorders trigger automatically when probability crosses your threshold. Marketing surfaces adjust in coordination — when a SKU's stockout probability rises, ad spend on that SKU at that location decreases ahead of the actual stockout. The 12-25% rate drops to under 5%.
How most operators solve this today
Several categories forecast demand. None of them produce 2M SKU-location-day forecasts daily with marketing-action coordination:
Demand forecasting platforms (PredictHQ, Singuli, Inventoro, Toolio, RELEX Solutions, Logility, Kinaxis, Blue Yonder, ToolsGroup)
$249 to $1,000,000+/year
Strong at enterprise demand planning. The downstream marketing-action coordination (ad spend pause, email suppression, PDP CTA changes) is your team to build.
AI inventory optimization (C3 AI Inventory Optimization, Onebeat, Increff, StockIQ, Skupreme, Inscaler, Optiply)
$199 to $500,000+/year
AI-driven forecasting and replenishment optimization. Marketing-surface integration remains your team.
Retail-specific demand planning enterprise (Anaplan Retail, RELEX Retail, Blue Yonder, Oracle Retail Demand Forecasting, SAP IBP Retail, Manhattan Associates)
$30,000 to $1,000,000+/year
Enterprise retail merchandise planning. Built for very large operators. The multi-location marketing coordination is still custom work.
Inventory analyst with Excel and reactive reorders
$80-130k/year analyst time
Detects stockout at POS-zero. The shelf is already empty.
Build it in-house
Senior engineer ($130-220k) + data scientist ($140-250k) + inventory analyst ($80-130k) + six to sixteen weeks
A custom Snowflake plus Python (Prophet, LSTM, XGBoost, ARIMA) plus ERP integration gets you to v1. Maintaining 2M forecasts daily is the cost.
What changes when this is an agent skill
Stockout probability is forecasted per SKU at every location for 7, 14, 30, and 60-day horizons. The ensemble combines Prophet (seasonality including local patterns), LSTM (non-linear behavior like promotion-driven inflection), XGBoost (feature-rich signals including weather, local events, competitor activity, promotion calendar), ARIMA (stationary baseline demand), and a reorder-point calculator that respects lead-time, safety-stock, and service-level constraints per SKU. When probability crosses your threshold, a reorder trigger fires — automatically for low-risk SKUs, with analyst review for high-impact items. Forecasts feed downstream to the marketing surfaces: when stockout probability rises, paid spend on that SKU at that location decreases ahead of the actual stockout, lifecycle emails suppress that SKU recommendation, the PDP CTA prepares to change, and the SEM bid lowers. Marketing stays in sync with inventory state before the stockout, not after.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Inventory-Aware Retail Marketing Agent
Watches SKU stock state and fans out coordinated ad-gating, storefront, email, SMS, social, and PDP actions across every channel.
FAQ
- How is this different from demand forecasting platforms (PredictHQ, RELEX, Logility, Kinaxis, Blue Yonder, ToolsGroup)?
- Those platforms forecast demand and optimize replenishment well. The downstream coordination with marketing surfaces (ad spend, email, GBP, PDP, SEM) is your team. We add that coordination layer.
- How is this different from AI inventory optimization (C3 AI, Onebeat, Increff, StockIQ)?
- AI inventory tools improve forecasting accuracy. Marketing-surface integration remains custom work. We focus specifically on the SKU-location-day forecast plus the marketing action.
- How is this different from retail-specific demand planning (Anaplan Retail, Blue Yonder, Oracle Retail, SAP IBP)?
- Enterprise retail demand planning suits very large operators. We are built for the 50-500 location operator who needs the same accuracy at a different price point and with marketing-surface coordination.
- Which models drive the forecast?
- Prophet (seasonality), LSTM (non-linear patterns), XGBoost (feature-rich tabular signals like weather, events, promotion calendar), ARIMA (stationary baseline), plus a reorder-point calculator. The ensemble weights tune per SKU and per location.
- How do marketing surfaces respond to the forecast?
- When stockout probability rises ahead of the actual stockout, paid spend lowers, lifecycle emails suppress the SKU, the PDP CTA prepares to change, and SEM bids drop. Marketing stays ahead of inventory state.
- Does this work with our existing ERP (NetSuite, SAP, Oracle, ToolsGroup)?
- Yes. The forecast reads from your ERP via API and writes reorder triggers back. We layer on top — the ERP remains the system of record for orders and inventory state.
- Does this work for operators with under 50 locations?
- Yes. The economics improve as SKUs and locations scale, but the configuration time and the model quality work at smaller scales too.