$1.73T
Estimated annual global inventory distortion
IHL Group’s September 2025 estimate for global retail out-of-stocks and overstocks—6.5% of retail sales.²
Demand forecastingInventory managementAWS PartnerAdvanced Tier Services
AI that puts the right stock in the right store.
Build SKU-by-store demand forecasts, test the events that move demand, and turn the output into buyer-reviewed stock recommendations.
Store 031 · SKU-2210
Scenario
Storm warning
Four-week demand
+31%
8 weeks actual4 weeks forecast
Weather scenario selected. Storm warning. Four-week demand +31%. Recommended action: Move stock before demand peaks. Transfer 240 units to Store 031.
Signals read
Recommended action
Move stock before demand peaks
The problem
Spreadsheet forecasts and static reorder rules leave shelves empty in one store and cash idle in another.
The gap is not another dashboard. It is a decision system that sees demand shift early enough for an operator to act.
The stakes
Demand forecasting benchmarks
20–50%
Lower forecast error
The potential McKinsey reports for AI-driven supply-chain forecasting.¹
Up to 65%
Reduction in lost sales and product unavailability
Upper-bound potential reported by McKinsey for AI-driven supply-chain forecasting—not a guaranteed result.¹
Where it pays off
Forecasting becomes useful when it changes the next decision.
Weather and calendar signals
The forecast reads weather, paydays, and holidays.
Sales history misses the events that move a week: a storm warning, a payday weekend, or a long holiday.
Swarm can combine SKU-by-store sales history with selected external signals such as weather, paydays, and holidays.
Scenario planning before spend
Test the promotion before you fund it.
Pricing and stocking calls often rest on a buyer’s judgment. The result only appears after the money is spent.
Model a weather shift, promotion, or payday cycle and compare the demand, margin, and stock position each one produces.
Human approval stays in the loop
Recommend the next stock move before a gap appears.
Static reorder points refill on a calendar cycle, days after demand has already moved between stores.
The system can draft store-transfer and out-of-cycle order recommendations for buyer approval, then pass approved actions to connected purchasing and supplier workflows.
The operating system
From demand signal to approved stock move.
Each forecast stays connected to the context behind it and the operational action in front of it.
Read the context
Combine POS history with promotions, weather, paydays, holidays, lead times, and on-hand stock.
Forecast store by store
Produce demand views by SKU and location, with the signal behind each movement visible.
Draft the next move
Recommend a transfer, a new order, or no action—before a gap appears on the shelf.
Approve and execute
Keep the buyer’s final call, then update purchasing, suppliers, and the systems your teams already use.
Retail scale
15,945
7-Eleven stores in CP ALL’s Thailand network at year-end 2025.³
Retail scale turns forecasting into an operating problem.
Across a 15,945-store network, demand, lead times, and stock risk vary location by location. The figure describes CP ALL’s Thailand network—not the scope of a Swarm engagement.³
Past employers represented across our team.
Past-employer marks are based on team biographies, not Swarm client relationships or endorsements.
How we deliver
One team, from workflow redesign to daily operation.
Redesign the workflow
Map the decisions, exceptions, and handoffs first. Then build the forecasting system around the way the operation should run.
Keep buyer control
Forecasts arrive with their drivers, and every transfer or purchase remains an approval—not a black-box instruction.
Operate and improve
After launch, track accuracy against a baseline and keep POS, ERP, WMS, and supplier integrations current.
Built around the systems your operation already depends on.
POSERPWMSPurchasingSupplier portals
Talk to our team
Make replenishment a daily decision system.
Start with one category, a baseline, and the operational decision you want to improve.
Sources and benchmark notes
- 1. McKinsey & Company, AI-driven operations forecasting in data-light environments. Industry potential, not a Swarm result guarantee.
- 2. IHL Group, 2025 inventory distortion estimate. Dated estimate for global retail.
- 3. CP ALL, FY2025 Listed Company Snapshot. Company-prepared; published by the Stock Exchange of Thailand on March 24, 2026. Network size and operator context only.