Solution
Inventory Intelligence
Hold the right stock in the right place by combining accurate, unified inventory data with demand forecasts and clear replenishment recommendations.
The problem
Too much of the wrong stock, not enough of the right
Inventory ties up working capital, yet stock-outs cost sales and customer trust. Balancing the two is difficult when stock data is fragmented across locations and channels, and when replenishment depends on spreadsheets and intuition.
Seasonality, promotions and supply delays make the problem harder still.
Causes and impact
Why it happens and what it costs
Fragmented stock data
Warehouses, stores and channels report stock differently.
Static reorder points
Fixed thresholds ignore changing demand.
Supplier variability
Lead times change but plans do not.
Operational impact
- Stock-outs on popular items
- Excess stock and markdowns
- Working capital locked in slow movers
- Time-consuming manual planning
The solution
One view of stock, guided by demand
We consolidate inventory data across locations into a single, reliable view, forecast demand at the right level of detail, and generate replenishment and transfer suggestions that planners can review and approve.
Alerts highlight emerging stock-out and overstock risks so teams can act early.
Main features
Key features
Unified inventory view
Stock positions across warehouses, stores and in-transit.
Demand forecasting
SKU and location-level forecasts accounting for seasonality.
Replenishment suggestions
Recommended order quantities considering lead times.
Transfer recommendations
Rebalance stock between locations.
Risk alerts
Early warnings for stock-outs, overstock and slow movers.
Planner dashboards
Prioritised actions and key inventory metrics.
High-level workflow
How it works
Illustrative example: multi-store retailer
Each morning, planners see a prioritised list of items at risk of stock-out by store, with recommended transfers from locations holding surplus and suggested supplier orders for the rest.
- 1
Data unified
Stock, sales and supplier data consolidated daily.
- 2
Demand forecast
Expected demand calculated per item and location.
- 3
Recommendations
Orders and transfers proposed.
- 4
Planner review
Suggestions approved, adjusted or rejected.
- 5
Orders placed
Approved actions sent to ERP or suppliers.
Integration requirements
Systems typically involved
- ERP and inventory systems
- Point-of-sale and e-commerce platforms
- Warehouse management systems
- Supplier portals or EDI
Implementation considerations
Data accuracy
Forecasts depend on reliable stock and sales records.
Planner control
Keep humans in the loop for purchasing decisions.
Granularity
Choose the right level (SKU, category, location) for forecasting.
Promotions and events
Feed known events into forecasts.
Potential benefits
What this solution is designed to improve
Actual results depend on your starting point, data and adoption; we agree how to measure them during discovery.
Better availability
Fewer stock-outs on items customers want.
Leaner stock
Less capital tied up in excess inventory.
Faster planning
Planners review suggestions instead of building spreadsheets.
Early warnings
Act before problems affect customers.
Explore more
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Learn more about Data Engineering & AnalyticsFAQs
Frequently asked questions
Answers to the questions we hear most often. If yours is not covered here, our team is happy to talk it through.
More history helps capture seasonality, but useful forecasts are often possible with less. We assess your data before committing to an approach.
It can, if you choose, but we usually recommend planner approval, especially initially.
Balance stock with demand
Share your inventory challenges and data sources to explore a pilot.
