Imagine a warehouse receiving household goods while preparing store orders for afternoon dispatch. The receiving team is staffed against booked deliveries. On paper, the work fits comfortably into the morning.
Then several late trucks arrive together. The supervisor pulls qualified people off picking to help unload. The queue clears, but the store orders still need finishing. Some of the time recovered at receiving has come out of the time available before outbound trucks leave.
Paying for extra receiving cover would protect that picking time. On quieter days, though, the cover might go unused. How often does that protection need to matter before it is worth the cost?
Start by establishing what is actually at risk. If picking catches up without overtime or a late departure, the receiving queue may be tolerable. If the same disruption repeatedly threatens the afternoon dispatch, follow the consequences through to the store. A dock-level staffing decision needs to account for the work it displaces.
DataDocks CEO Nick Rakovsky would first examine why the receiving plan keeps breaking:
“Before adding people, I’d look at what’s coming in, how often and with what lead time. Can we change the receiving process? Throwing more people at it isn’t always the right answer.”
Appointments alone do not answer those questions. Two trucks can bring very different workloads: one carries a few straightforward pallets; another needs extensive checking and sorting. Compare booked and actual arrivals, the notice given when plans change, and the time each load occupies people and equipment. Check whether receiving information is ready before the truck arrives.
Then examine how the team responds to lateness. Automatically sending a late truck to the back of the queue might restore order while delaying goods needed for imminent store orders. The rule has to reflect the work the warehouse must complete.
A 2024 study using data from eight days and 547 trucks at an Italian grocery distribution centre explored this opportunity. In computational comparisons, using estimated arrival times to revise truck schedules reduced waiting with unloading resources held fixed. Those were modelled results, but they give a reason to examine arrival decisions before treating every queue as a staffing shortage. The receiving study.
Even a better schedule leaves a timing problem when arrivals depart from plan.
Take a deliberately simple example. Two complete receiving crews can each process one load per hour. Two loads are booked for 09:00, two for 10:00 and two for 11:00. Everything should finish at noon.
The first two trucks arrive an hour late. There are now four loads at 10:00, followed by two more at 11:00.
| Receiving arrangement | Loads finished by noon | Final loads finished |
|---|---|---|
| Booked arrivals, two crews | 6 | 12:00 |
| Bunched arrivals, two crews | 4 | 13:00 |
| Same bunched arrivals, third crew available 10:00–12:00 | 6 | 12:00 |
The third row assumes a third usable dock, suitable equipment and enough staging space. It also assumes a complete qualified crew that is genuinely available, without abandoning critical picking work.
The two original crews had spare time at 09:00. They could have used it for useful preparation or other duties. They could not use it to unload trucks that had yet to arrive. By 10:00, the same morning’s work had been compressed into fewer hours before the deadline.
Queueing research explains why this becomes difficult as resources fill up: with continuing variation in arrivals and processing times, waiting can rise sharply as a resource approaches full utilisation. The underlying mathematics does not give every warehouse a universal safe utilisation percentage. It does explain why average workload alone is a poor basis for promising short waits. Research on queues near capacity.
Our six-load example still does not establish that hiring another crew is worthwhile. It shows which window needs protecting. The next question is what would actually increase the work completed in that window.
If staging is full because putaway cannot keep up, another unloading crew may accomplish little. If receiving and picking need the same forklift, that equipment must be included in the plan. Cross-training helps people change tasks; it cannot make someone available for two urgent jobs simultaneously.
Once the constraint is clear, compare practical forms of cover. A planned overlap between receiving shifts might protect the busy window. A trained relief team might work on tasks that can safely wait until later. Call-in help only works if it can arrive before the backlog threatens the deadline.
Nick connects those choices to knowing the incoming workload:
“If you know when the work is coming, you can plan for it. Maybe you bring someone in for a busy day, or use a little overtime. Otherwise, you keep pulling people away from their own work, and they feel they can never catch up.”
To assess the options, reconstruct representative recent days. Keep actual arrival times, unloading and checking durations, available resources, and outbound deadlines together. Include ordinary mornings as well as the troublesome ones. Preserve the clusters of late arrivals: spreading them out in the analysis would remove the problem being tested.
Compare a better receiving schedule on its own with the same schedule plus targeted cover. For each option, ask how often receiving finishes in time, how long trucks wait, and whether store orders are ready for dispatch. Track where the work moves when people are reassigned.
A study published in August 2026 by Junhao Wang and colleagues provides a useful example. The researchers evaluated warehouse picking schedules under variable execution times, using historical prediction errors to test departures from the expected plan. Their demonstration covered one operating day at a small fabric distribution centre. It does not establish the value of spare receiving staff; the idea we can apply is to test a proposed arrangement against plausible variation before relying on its expected performance. The 2026 warehouse scheduling study.
For a simple operation, replaying actual days on a timeline may narrow the choices. Where labour, docks, equipment and staging interact, a simulation checked against observed operations can help. Either way, the comparison needs the same workload and service commitments.
Price the arrangement you could actually run. Two hours of peak demand may require a longer paid shift. Training, minimum paid hours and cover on uneventful days all belong in the cost. A flexible arrangement also needs a credible response time and someone authorised to activate it.
Compare that cost with consequences the cover could realistically prevent: overtime, chargeable truck waiting, additional transport or missed store deliveries. Check actual detention terms before treating waiting minutes as an invoice. Avoid counting the same consequence twice. Where the service impact is uncertain, keep it visible without manufacturing a precise monetary value.
Test the strongest option over a representative period, with a specific deadline to protect. Measure receiving completion, truck waits, picking readiness and overtime together. An improvement at the dock is less valuable if the same pressure simply appears elsewhere.
One quiet day does not establish that the cover is wasteful. One rescued dispatch does not establish that it should become permanent. The decision depends on how often the arrangement helps, what it prevents and what it costs across the whole period.
Take one recurring arrival pattern and put a concrete proposal against it: which resource, available when, protecting which commitment? That gives the business something more useful to decide than whether the warehouse looks busy enough.
