Enterprise inbound operations

The situation

Inbound at a fulfillment center is a dock-to-stow pipeline that lives and dies by standard work: every process path documented, every rate measured, every shift managed against a plan. The operation was mature. The cost was hiding in middle management's day, where problem-solving and daily reporting still ran on repetitive manual effort.

Problem-solving happened by walking the floor: a manager saw a signal, went to the station, gathered context by hand, and acted. Daily reporting was assembled the same way, pulled together at end of shift from multiple systems, formatted by a person, and sent up the chain.

The constraint

The constraint was not data. A building like this produces more operational data than anyone can read. The constraint was how much of a manager's shift went to repetitive collection and formatting work instead of decisions. Every hour spent rebuilding a report or walking to find context was an hour not spent running the operation.

What was built

AI tooling was layered onto the existing standard work rather than replacing it. The problem-solve loop was instrumented so exceptions surface directly to the person who owns them with context already attached. The walk happens after the signal, not in search of one.

Daily reporting was rebuilt the same way: drafted automatically from operational data, then reviewed and owned by the manager instead of written from scratch. The standard work stayed the backbone. The tooling removed the repetitive steps around it.

What it changed

Middle management saw a 43% operational improvement once the repetitive work moved to tooling. Problem-solve loops close faster because they start with context instead of a walk, reporting costs the floor almost nothing to produce, and managers spend their shift managing. The operation kept the thing that made it good in the first place: documented process, owned by the team that runs it.