Shiptheory

Before any recommendation, we map what actually triggers a Shiptheory booking: order status, stock allocation, carrier rules, and who owns each one across the ecommerce platform, the ERP and the warehouse. Most Shiptheory problems are not shipping problems. They are upstream status problems that only become visible once a label fails to print or a customer orders a line that was never really in stock.

We trace the order lifecycle end to end, find where ownership is ambiguous, and quantify what that ambiguity costs in dispatch delay and support hours. The engagement closes on a fixed-scope recommendation: what to fix, in what order, and what it protects in fulfilment cost and customer experience.

The decision that makes or breaks a Shiptheory implementation is simple to state and hard to enforce: which system owns order status once it leaves the ecommerce platform. If Shiptheory, the ERP and the WMS all think they own it, you get duplicate dispatch, silent re-shipments, and a returns team working from a different truth than finance.

Ownership line set: - Order and stock source of truth: ERP or ecommerce platform, never Shiptheory - Carrier rule logic and label generation: Shiptheory - Physical pick, pack and dispatch confirmation: WMS or warehouse floor - Exception resolution: named team, not whichever system happens to surface the error first

Get this wrong and Shiptheory becomes the place where discrepancies are discovered, not resolved. That is expensive in fulfilment cost and in the time it takes finance to close a period cleanly.

Shiptheory already integrates with Patchworks, and for most retailers that is the sensible route rather than a point-to-point build held together by a developer's goodwill. Patchworks gives a faster path to onboarding a new carrier, a new warehouse or a new sales channel without touching custom code each time, and that speed compounds as the stack grows.

We operate this as expert consultants using AI-assisted delivery and monitoring on top of the platform, not as an AI vendor selling automation for its own sake. The result is predictable cost to add the next channel, safer change when a carrier rule needs updating, and fewer late nights spent debugging a point-to-point script nobody fully understands anymore.

Mechanics only matter if someone can see when they stall. The first sign is usually a thin one: a handful of orders sitting in Shiptheory's queue slightly longer than normal, not yet an outage, not yet a ticket spike.

What we build on top of the integration tracks queue depth by carrier, exception volume by order type, and drift between what the ERP believes was dispatched and what Shiptheory confirms. AI-assisted monitoring flags the pattern before volume: a rising failure rate on one carrier rule, repeated retries on the same SKU, a 3PL feed that has gone quiet.

Caught at that stage, it is a configuration fix. Caught after a customer emails asking why their order shows shipped but never arrived, it is a support ticket, a goodwill refund, and a dent in repeat purchase that nobody attributes back to the actual cause.

Teams need a runbook, not a tour of the Shiptheory dashboard. We document the common exception types, who owns each one, and the fix for each: carrier rule mismatch, stock allocation failure, address validation reject, duplicate booking.

Escalation is named by role, not department: who re-triggers a failed shipment, who checks stock before manually re-queuing an order, who decides a carrier override is acceptable during peak. Without that clarity, the exception queue becomes everyone's problem and therefore no one's.

Hypercare after go-live is where new carrier rules, new warehouse additions and real order volume expose assumptions that testing never surfaced. We stay on the exception queue daily for the first weeks, watching for patterns rather than one-off noise.

Peak is the real test. Order volume climbs, carrier capacity tightens, and a Shiptheory rule that worked fine at normal volume can silently fail to assign a service when a carrier's own thresholds are hit. We plan for that before the peak period starts, not during it, with named escalation routes and a clear line to whoever can authorise a manual override.

After hypercare, support continues as managed operation: monitoring, exception triage, and carrier rule maintenance as channels and 3PLs get added, so dispatch delay does not become the brand's problem to explain to customers.