# Warehouse task assignment: 30% less waiting for tasks

> How assigning warehouse tasks straight to handheld terminals, by floor, zone and distance, cut task waiting time in mezzanine picking by 30%.

Page: https://opsalute.ai/en/cases/taskrouter
Русская версия: https://opsalute.ai/llms/ru/cases/taskrouter.md

Case study · Team experience · Yandex Market

## Ready to work, but waiting. How warehouse task assignment removed the queue for the supervisor.

TaskRouter is a “digital supervisor” that takes over warehouse task assignment. It sends the next task straight to a worker’s handheld terminal and suggests who to move where a backlog is growing. The supervisor has the final say.

**−30%** task waiting time in mezzanine picking

**−80%** manual task assignment by supervisors in the pilot

**+8%** operator efficiency in the pilot

### A worker finishes a task, then waits.

A picker closes a task on the terminal and is ready for the next one. But the next one comes from the supervisor, assigned by hand and by eye. While the supervisor deals with another area, the worker stands still. In a warehouse with around 800 shifts a day, those pauses repeat all day long.

The supervisor is stretched too, watching queues in several areas at once and deciding who goes where. A wrong call means work piles up somewhere and a shipping deadline slips.

### Step one: the next task arrives on its own.

As soon as a worker is free, a new task appears on the terminal, chosen by floor, zone and distance. The system matches people and tasks for everyone at once rather than one at a time. The first area was automated picking on the mezzanine.

### Step two: people move to where the queue is growing.

The system sees where work is piling up and where a deadline is at risk. It suggests specific people based on their history: where they have worked and how productive they were. Newcomers are not moved during their first shifts, and anyone sent to an unfamiliar process goes with a mentor. The supervisor approves, changes or cancels the move.

### Results, and what turned out harder than the algorithm.

In mezzanine picking, task waiting time fell by 30%. The pilot also cut manual task assignment by supervisors by 80% and improved operator efficiency by 8%. These results come from this project, not a forecast for every warehouse.

Transfer suggestions were not always accepted. The reason was human: a supervisor’s bonus depended on their own area’s numbers, so giving a strong picker to a neighbor hurt their results. A suggestion needs a clear reason, and the supervisor who gives up people should get credit for it.

This work was done at Yandex Market, and the code belongs to Yandex. It is part of our team’s experience; Hrusty is a separate product for frontline teams.

### See what this could look like for your team.

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### Further reading

- [Warehouse incentive pay and piece rates, without the disputes](https://opsalute.ai/en/blog/piece-rate-pay) — Warehouse incentive pay people can trust. Why hourly pay caps output and how piece rates work with per-task standards, scan data and a balance workers see.
- [Where warehouse labor costs leak, and how to find it in a week](https://opsalute.ai/en/blog/frontline-payroll-leaks) — Five places warehouse labor costs leak, from guesswork timesheets and buddy punching to agency no-shows, unchecked overtime and workers waiting for tasks.
- [Warehouse shift planning: how we stopped reconciling hours by eye](https://opsalute.ai/en/cases/hrms) — Warehouse workforce management at a fulfillment center: shift planning, check-ins, timesheets and agency invoices linked, so hours are settled with data, not chats.