by Proximaagents
Representative sample
Shipment exception recovery diagnostic.
This is what the evidence looks like before a real customer diagnostic runs. It is not a real case study.
Workflow overview
Delayed shipments move across too many handoffs.
Exception work jumps between WMS, TMS, carrier portals, email and customer support. While an exception waits for manual routing, revenue leaks out in failed deliveries, RTO, SLA credits and unhappy customers.
Sample data
Representative input set
Fields include order value, SLA deadline, exception type, resolution timestamp, carrier status, support-touch count and the final outcome.
Operations summary
Most of the delay comes from cases waiting between teams. Proxima would first test whether it can sort and route the routine cases correctly. Customer communication and the complex exceptions stay with people.
Exception breakdown and agent fit
| Category | Share | Representative action | Illustrative accuracy | Mode |
|---|---|---|---|---|
| Address/contact mismatch | 26% | Draft customer confirmation and carrier update | 88% | Human approval (HITL), then routine automatic handling (Autopilot) candidate |
| Carrier scan gap | 22% | Classify delay, request carrier trace, notify support | 82% | Human approval (HITL) |
| Warehouse dispatch hold | 18% | Route to warehouse owner with evidence | 79% | Suggest without acting (Shadow), then human approval |
| Payment/document hold | 12% | Prepare internal escalation | 72% | Human review only |
| Complex exception | 22% | Escalate with summary | Not automated | Understand the work (Observe) |
The illustrative percentages compare recommendations with reviewed human decisions for each category. They do not measure business outcomes and do not, by themselves, approve automation.
Revenue impact estimate
| Mechanism | Illustrative monthly basis | Impact to validate |
|---|---|---|
| Recovered deliveries | 2,000 affected shipments x INR 1,800 average order value x 4-8% incremental recovery | INR 1.44L-2.88L revenue protected |
| Reduced RTO | Lower return-to-origin from faster address/contact recovery | Gross margin protected plus logistics leakage reduced |
| SLA-linked revenue | Fewer delayed exception closures for priority accounts | Credits or penalties avoided, renewals protected |
| Throughput lift | More exceptions resolved same day without service degradation | Peak-season volume absorbed without lost orders |
Recommended scope
Promote only what proves reliable.
- ObserveUnderstand the workRun read-only classification on the approved exception feed for a duration agreed during implementation. No writes, no actions.
- ShadowSuggest, don’t actCompare our suggestions with the team’s real decisions, category by category, without touching live systems.
- Human approvalYour team decidesSupervisors approve every customer or carrier update Proxima prepares before anything is sent.
- Controlled automationAutomate what has proved safeConsider only customer-approved action categories after the performance measure, threshold, operating limits, monitoring, and response procedure are agreed and tested.
Next step
Run this on one real workflow.
Bring sample records, system and API notes, and one operations champion who knows the work. Proxima returns the real bottleneck map, the impact case, and a go/no-go recommendation.