Delivery operations + inventory proxy

Olist Delivery SLA Risk

Which sellers, categories, regions, and order patterns create highest delivery SLA risk?

Decision summary

What this proves

The Olist case turns ecommerce fulfillment data into SLA risk triage: late delivery rates, delay severity, review-score association, and seller/category/region investigation candidates.

  • 96,470 delivered orders analyzed in public aggregate outputs.
  • 6.77% late-delivery rate surfaced as operational risk signal.
  • Seller/category/customer-region combinations prioritized for investigation.
  • Inventory risk is framed as proxy analysis because Olist lacks stock-on-hand, replenishment, warehouse availability, purchase orders, or backorder fields.
Reviewer mode

Fast evidence review

Claims stay bounded by available public data. Outputs are decision-support candidates, not automated operational instructions or unsupported causal proof.