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Data Requirements
Replenify works with four core data domains. If your ERP holds this data, you are ready to connect.
We recommend daily snapshots for optimal accuracy
Data Pipeline
Three steps from raw ERP data to actionable intelligence.
Your ERP
Posibolt
Data Connector
Native sync
Canonical Model
Normalized
6 Modules
Intelligence
We connect to your Posibolt instance via our native connector. No manual exports, no CSV uploads. Real-time sync.
Your data is mapped to our canonical model. Different ERPs, same format. This means switching ERP later doesn't break Replenify.
We calculate derived metrics: sell-through velocity, size curves, store affinity scores, broken range indicators. Your raw data becomes intelligence.
Readiness
Use this checklist to assess your readiness. The more boxes you can tick, the faster you will see results.
Don't have all of this? That's normal. We can work with partial data and improve quality over time. Replenify adapts to what's available.
Module Matrix
Not every module needs every data type. Here is exactly what powers each one.
| Module | Product Master | Stores | Inventory | Sales | Transfers | POs | Returns | Markdowns |
|---|---|---|---|---|---|---|---|---|
| Rebalancing | ||||||||
| Allocation | ||||||||
| Pricing | ||||||||
| Replenishment | ||||||||
| Returns & Markdown | ||||||||
| Assortment |
Native Integration
Most of our target customers run Posibolt -- South Africa's leading retail ERP. That's why we built a native connector that understands Posibolt's data model out of the box. No middleware, no manual mapping, no delays.
Compatibility
Not on Posibolt? No problem.
Implementation
A structured, predictable implementation process. No surprises.
We review your data landscape and identify gaps. A structured audit of what you have, what format it's in, and what needs attention.
Your data flows into Replenify. The connector is configured, field mapping is validated, and the first full data load completes.
You see results on your actual data. Engine parameters are tuned, thresholds are set, and the first recommendations are generated.
Continuous improvement as data quality improves. Replenify learns, recommendations sharpen, and your team builds confidence.