4 minute read

The Humble Queue: A Few Simple Principles

Mark Thomas, Director – Operational Excellence (UK)

Scheduling checkout teams to manage queues is not as frightening as you may think. Whilst there is variability on a daily and hourly basis, you don’t really have to worry about a delivery being late, or a huge spike in workload due to the night staff calling in sick. Yes, there are scenarios you need to plan for, but with the right thinking and team in place you can run a successful ship by following a few simple principles. In this post I will walk through these principles which are based on my 33 years in the retail and WFM workplace and have been proven successful across several businesses. Be sure to also check out Part 1 and Part 2 in this queueing series!

The teatime tightrope

Being British, teatime is of course the hours between 3 p.m. and 5 p.m., which is traditionally the transition period from a lengthy lunch into a hearty dinner. In retail, it’s a time that is notoriously difficult to manage from a front-end perspective. There are many reasons for this, but two significant ones are:

The sad thing is that the person running the operation in the evening usually takes the flak for the above not being properly managed. You will not see the impact between 4 and 5 p.m., which is when the problems start to manifest themselves. The problem will be visible once the manager is safely down the road.

A mass exodus at 5 p.m. is one key area to fix. Spread your shifts evenly to avoid The Teatime Tightrope. Believe it or not, most associates are happy to move their shifts to accommodate the business needs. You just need to ask them!

Whilst the above sounds like common sense (and it is), every retailer has the same problem pretty much every day of the week—and they know it. Don’t just talk about it, do something about it!

The manager’s day off

Just like Ferris Bueller, everybody has a day off now and then. The next line may seem like a fantasy statement, but it happens week in week out.

Assuming all things are equal, your store will use proportionately more hours on the manager’s day off.

Now, this isn’t the manager’s fault. Or is it?

In a world of increasingly competitive key performance queueing metrics, nobody likes failure. What I find is that there is a distinct fear of failure when the manager is off work, and the desire to please them when they return the following day leads teams to use more actual hours than they need.

You may think this is a crazy hypothesis, but go check your actual hours reports for differing days and normalise them to a “per customer” or “per unit” and see the answer for yourselves. I can pretty much guarantee your manager’s day off has lower levels of productivity.

Reactive scheduling

This is one of my favourite topics, but to endeavour to maintain your engagement I’ll keep it brief!

What do I mean by reactive scheduling? Scheduling more people at 11 a.m. because there’s always a problem with queues at 11 a.m.

You have reacted to a regular pattern and tried to solve it with better scheduling at that time. What’s wrong with that? Sounds sensible to me.

However, the problem you’re trying to fix is not actually at 11 a.m., it’s earlier in the day. Queues do not magically appear at a given time. They manifest and creep up on you. If you have queues at 11 a.m., it’s likely you were understaffed around 10:15 to 10:30. This is where you have a scheduling issue, and not at 11 a.m. This is what I term preventative scheduling, versus being reactive. Let’s prevent the queue from happening in the first place by fixing 10:30, rather than try a band-aid approach at 11 a.m. and reacting to an issue that needn’t have been there in the first instance.

Hopefully the key take out from this is to not always believe what you see. Look deeper into the data to understand where the actual problem is and stem the flow before it gets out of hand (11 a.m.).

Items per customer fluctuations

What does the number of items per customer have to do with my scheduling and queues? Everything.

The Teatime Tightrope I’ve already talked about is back on the scene again. Whilst I’m applying the example to the hour between 4 and 5p.m. for items per customer fluctuations, every store is different, and you will know what time I’m referring to in your store instantly.

A lower items per customer means a lower throughput figure. The scanning of items is the primary variable element of the checkout journey. Tender has low variability, it’s cash or card. Each time the number of items moves, the dynamic between throughput and queueing moves.

Traditionally the items per customer drops between 4 p.m. and 5 p.m. primarily due to people popping in on the way home from work to pick up a few bits for dinner (not tea!)

The point I’m trying to make here is to bring more dimensions into your decision-making process. If you only use customer volumes to support scheduling, you won’t pick up the items per customer variability. Not only do you have shift changeover and the manager going home, but we also now throw smaller basket sizes into the mix. Controlling a tight ship between 4 and 6 p.m. will set you up well for the rest of the evening. The majority of your evening issues stem from teatime and the propensity to react.

The next post in this series will look at checkout types and explore how the correct type, mix and maintenance of your fleet can support queue management.

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