01
Connected existing transaction data through a separate analytics layer.
Case study · Delivery evidence
Turning retail transaction data into operating decisions
A non-invasive analytics and AI layer designed around an existing SaaS ERP for an Italian-Chinese supermarket operation.
By Onyx Devs Lab · Published · Updated
The operating challenge
Transaction data existed, but inventory, workforce, and loss signals were fragmented. The business needed better decisions without replacing the operational ERP.
Designed intervention
02
Designed missing workforce and inventory-loss data modules.
03
Applied inventory forecasting and market-basket analysis to operational decisions.
04
Kept recommendations reviewable by operators rather than automating high-impact decisions invisibly.
Validation snapshot
These figures separate delivered scope from pilot validation. Each metric retains its measurement definition so product coverage is not mistaken for business impact.
stores covered
Cross-store sales, inventory, and operating analysis
legacy tables mapped
Fields, sources, and metric ownership documented
reconciliation consistency
Daily sales and inventory metrics checked against source systems
actionable inventory alerts
Alerts followed by replenishment, transfer, or stock-reduction action
median decision-data time
From a cross-store question to a reviewable answer
Cross-scenario architecture guide
Compare the retail decision layer, accounting multi-agent controls, and legal-AI provenance in one public guide while retaining each case’s evidence boundary.
Evidence boundary
The published metrics come from project records and describe the delivered scope and pilot validation. They should not be extrapolated as expected results for another organisation.