Replenishment Insights
Retail indicators that enable cost reduction and improve availability
Aldata’s Replenishment Insights aggregates meaningful business information from your distribution center replenishment systems into a set of visual and interactive indicators that allows managers to respond to operational conditions quickly and decisively – in real-time and without the need for a data warehouse.
With Replenishment Insights, managers benefit from a bird’s eye view of their operations - across inventory locations - as well as drill-down capability to identify underlying causal factors. Opportunities for business improvement in inventory levels and availability can be identified at the supplier; buying department and warehouse levels with drill-down support to item level where appropriate. Equipped with this insight, managers and buyers alike can react quickly to reduce inventory costs and increase availability.
Key indicators available with Replenishment Insights help you:
- Determine your Inventory Stratification: Understand precise inventory levels across distribution centers. From buffer levels, on-hand, idle, rounded inventory and more — all are identified and quantified in a single view.
- Improve Capacity Planning: Slice and dice available inbound capacity over different shifts and warehouses, to identify any potential capacity constraints and take corrective action.
- Manage Supplier KPI’s: Support buyers with supplier performance management. Analyze service levels and delivery times by supplier to improve performance and reduce the need for safety stock.
- Understand Available and Forecasted Stock Positions: Including current and forecasted inventory, this indicator enables potential out of line inventory positions to be identified.
Ask for details on other indicators available with Replenishment Insights.
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Download the Aldata Insights solution sheet.
Optimize your operations with Aldata's Forecasting and Replenishment solutions
In-memory computing involves storing data in the main random access memory (RAM) rather than retrieving it from complex relational databases for processing. As a result users can analyze huge volumes of data, detect patterns quickly and adjust their operations almost immediately.
