How do you know you’ve developed the right schedules? It seems pretty simple. You take the forecast and create schedules. Then, you graph the schedules against the staff requirement. If the lines look close, you’re done!
Though many call centers take this approach, developing efficient schedules may not be this simple. If this is what you do in your call center, read on to learn why that approach can be so damaging.
No time to read the full article? Just download it as a PDF and read it offline, anytime, anywhere.
Why Measuring Schedule Efficiency is Important
Inconsistencies in your statistical approach
First, you aren’t taking a statistical approach to this process. Think about everything you measure in your center: call volume, handle time, service level, abandon rate, forecast accuracy, schedule adherence, shrinkage, and on and on. Not measuring your scheduling efficiency is not measuring a critical step in your WFM processes. So, you’re going from measuring (forecasting) to not measuring (scheduling) to measuring (service level results).
Give you input for forecasting headcount better
Second, scheduling efficiency is a critical input into your headcount forecasting process. You won’t see this factor in an Erlang calculator in Excel. You may not even see this as input in your forecasting or workforce management software. But if you think about it, you have a certain number of heads required for a given workload. You lose efficiency as soon as you apply schedules to this. When you create schedules, you can’t perfectly align the requirement with the demand… unless everyone works 30 minute shifts. And if you do that, I’d love to learn more!
Unlike graphs, measuring schedule efficiency is strategic
Graphs can be misleading. One of my favorite tricks (I mean “analytical techniques”) is to set the graph axes to show the picture I want to show. You can take any graph and extend each axis so much that gaps look more significant or less significant than they really are.
Take a look at the two graphs below:
Both of these graphs have identical data points. The first graph looks like it has pretty tight alignment. You see a few variations, but it doesn’t look alarming. The second graph looks almost completely misaligned. So, to have your schedules accepted, you show the first chart. If you want to drive an action, you show the second chart. But regardless, you still haven’t actually measured the efficiency of these schedules. It’s subjective.
As you become more strategic, your workforce management department should be able to quantify the impact of various business rules on scheduling. For example, consecutive days off and all full time (or most, full time) employees are common scheduling constraints. While there may be good reasons to have these constraints, they come with a cost. If you measure your schedules, you can measure with and without these constraints. You can also provide feedback to leadership, so they know the cost. Having that information enables them to make better decisions.
How Do You Measure Schedule Efficiency?
There are many ways to measure schedule efficiency. In my experience, the best way is to set an efficiency threshold (e.g. 20% per interval). Then, you measure the percentage of intervals within that threshold. It reads a bit like service level. You may choose to have a target of 90% of your intervals within a 10% threshold. The best place to start is to measure where you are now, and set a target that makes you better.
This is the data set for the graphs above:
If you measure the percentage variance of scheduled-to-required, you get:
You have 3 intervals more than 10% off of the required:
This results in 18 of the 21 intervals, or 86%, within the threshold. In this example, we’re trying to get 90% within the threshold. Looking at the graphs, you may need to go back and figure out how to add some staff to 10 am or remove some staff from 1:30 and 2pm. By improving one of these intervals, we can get to the 90% threshold.
Generally, you measure and report service levels on a weekly or monthly basis. From a statistical perspective, you can tackle the largest volume intervals. You can capture the calls you need within service level. In reality, businesses have to achieve service levels consistently. The more inconsistent the service levels, the more inconsistent the customer experience. The more inconsistent the workload for your staff, the more likely you are to get complaints. Scheduling your staff within a measured threshold puts you in a position where staffing is as closely aligned as possible for each interval.
Where WFM Software Can Help
This is an example of where technology can help you. Building schedules— and changing them— without the benefit of technology is tedious and error-prone. It’s time-consuming and produces sub-optimal results. You need the ability to automatically generate schedules against the demand. You need the ability to make changes to schedules and scheduling rules. You may want to adjust requirements quickly. By doing so, you can invest your time in the measurement and analysis of the data.
How to Take First Steps to Measuring
If you're not measuring your schedules today, try this method: You can start by measuring yourself without reporting the results. See what your current performance is. Start to work on improving it to see what actions move the needle. Once you are comfortable with the process, share it with your leadership.
You will gain credibility with your leader for proposing this. Your workforce management team will gain credibility for suggesting this measure. Most importantly, you’ll have less stress and “back-and-forth” on scheduling. You’ll now have an agreed measure that has the objective of aligning staff to requirement.
Use this method to show the impact of new scheduling constraints, as well. If your Operations team wants to layer in a new employee benefit or requirement, you can reflect the results. You can work in terms of a reduced percentage of intervals within the threshold, which means more inconsistency in service levels.
Learn more about schedule efficiency metrics, by talking with the experts.
Did you find the article interesting and would like to share it with your colleagues? Download the article as a PDF.
Originally published on Jun 21, 2017, updated on Apr 09, 2020