Cold-chain capacity: why orders miss the noon cut-off

A refrigerated grocery depot increasingly misses its noon departure cut-off. Management reports spare daily picking capacity and wants more orders dispatched on time.

The interviewer adds an operating constraint: the first improvement must use the existing footprint. Staffing and schedules can change, but order accuracy and the depot's approved temperature controls must be maintained.

Start by defining the outcome: an accurate order loaded and ready for its scheduled departure. “Picks completed” is an intermediate measure. A pallet waiting at the dock has not solved the customer's problem.

Ask when orders become available to work, when products clear receiving, when picking finishes and when trucks can load. Also confirm whether different orders have very different workloads. Counting a one-item order and a hundred-item order as equivalent could hide the real pressure.

Your first structure can be short:

“I would follow the morning orders through receiving, picking and dispatch. At each step, I would compare the work arriving before noon with usable capacity during those hours, then examine the waits between steps. I would test changes against completed on-time dispatches, while preserving accuracy and temperature controls.”

This gives you a place for the obvious causes without choosing one prematurely:

The handoff deserves its own line. Two teams can each meet their productivity target while work waits between them.

The interviewer gives you a simplified morning. There are 300 comparable orders ready for picking at 8 a.m. Picking completes 100 orders per hour. Loading can handle 100 per hour, and a separate team is available to load from 9 a.m.

Under the current rule, the whole batch is held until picking finishes. Assume no travel or setup time between the two stages for this exercise.

Picking runs from 8 to 11 a.m. Only then do the 300 orders reach loading. Between 11 and noon, loading completes 100 orders. The other 200 miss the cut-off.

The depot has enough daily picking capacity. It also has enough total loading capacity if allowed to work later. The problem is the sequence before the deadline.

A recommendation to “increase utilisation” would be unhelpful here. You need to change when completed orders become available downstream.

Suppose extra staff increase picking to 150 orders per hour. All 300 orders are now picked by 10 a.m. The depot keeps the same whole-batch release rule.

Loading has two hours before noon, so it completes 200 orders. 100 remain late.

Picking productivity improved by 50%, but the depot still misses one-third of the departures in this example. That result should send you back to the end-to-end process.

Now test a separate change: restore picking to 100 orders per hour and release completed work in hourly lots. The table below is a proposed schedule calculated from the original processing rates, not a new observed shift:

Under the stated conditions, all 300 orders finish loading by noon. The improvement comes from overlapping the stages, with separate teams, rather than increasing either team's processing rate.

Test a release rhythm that fits the orders and downstream operation. Smaller lots can improve flow, but compare that benefit with any additional handling cost.

The interviewer now corrects one assumption: the loading team achieves only 80 orders per hour during the morning.

Even with the first 100 orders available at 9 a.m., loading can complete at most:

3 hours × 80 orders = 240 orders by noon.

That leaves 60 late. Smaller releases still help, but they do not create the missing loading capacity.

Your next question should concern the 80-order rate. Is it limited by staff, dock positions, truck availability, paperwork or repeated handling? Moving a picker to loading helps only if picking can still feed the required work. You cannot count the same employee in both teams at the same time.

The distinction matters: the release rule was a cause of waiting, while loading capacity becomes the limiting factor after that rule changes. An operational improvement can expose the next problem.

Before proposing a wider rollout, follow one representative order group through a live shift. Record when work arrives, starts, finishes and is released at each stage. Separate processing time from waiting time.

Compare similar days and order mixes. A quiet morning with easy orders cannot establish that the process handles the difficult peak. Check whether the new release rhythm increases walking, scanning or repeated handling enough to reduce the assumed picking rate.

Use the existing approved temperature controls throughout. The candidate's task is to improve the flow within those requirements, not invent new storage limits or remove checks to save time.

A useful pilot scorecard includes on-time dispatches, order accuracy, waiting time, labour hours and compliance with the depot's handling conditions. It should also show where unfinished orders accumulate. A better picking number alone is not enough.

With the original 100-order loading rate, you can say:

“The whole-batch release rule is delaying loading until 11 a.m. Releasing completed orders hourly would let the two teams work in parallel and, on these figures, finish all 300 orders by noon. I would test that change on one order group, checking handling effort, accuracy and temperature controls before expanding it.”

With the revised 80-order loading rate, add the remaining gap: the new schedule reaches 240, so the depot still needs a credible way to load another 60 orders before noon.

Continue with the operations framework guide, or practise the same flow reasoning in the drive-thru structure drill.