The following article has been authored by Blair McGavin and is part 1 of a 2 part series. If you don't know who Blair is then check out his LinkedIn profile or his contact details are the bottom of this post feel free to reach out. He recently wrote a very thought provoking article on the future of WFM systems - if you have not already read this then worth checking it out, you can find it in the following link - What is the future of wfm systems?. Both myself and Blair welcome your thoughts and comments we would love to hear from you. - Doug Casterton
Capacity Planning – ‘Piece of Cake’
Figuring out how we capture and model all
of these data to forecast our service levels and determine our true FTE
requirements - otherwise known as Capacity Planning (CP) – that’s my passion. I
think I am drawn to this discipline because is has proven to be so difficult
for the WFM industry to figure out. On
the surface, it seems pretty strait forward.
There are four basic pieces of data in our models (volume, Handle Time,
Occupancy and Shrinkage / or Utilization.
Slap those numbers into some formulas, and there you have it – we need
150 FTE to meet SL’s next month.
Not so fast. I’ve been at this for about 25 years, and I
have never seen two analysts or companies do it the same way. So, if we are all using different
assumptions, different definitions, and slightly different formulas – and
clearly coming up with different answers:
Is any of us correct, or are we all correct? And is there a way for us to prove to
ourselves we are doing CP correctly? In
Engineering school, If I was taking an exam and either used a wrong formula,
wrong coefficient, or skipped a step – I came up with the wrong answer, and I
lost 10 pts on that part of the exam.
So, are all of ‘our’ varied CP methods correct? Nope.
Is there a way to prove to ourselves that our methods are solid? Yes!
More on that at the end.
Isn’t LinkedIn great! Having a powerful network can be an amazing
asset to help advance our careers. But
don’t you find the ‘endorsement’ process to be a little humorous. Select a skill, get a few people to endorse
that skill, and now you are an industry expert.
I have been endorsed by dozens of people for skills for which I have very
little experience. But the WFM skill
that I think I am most proficient at is Capacity Planning, yet only 2 people
have endorsed me for that skill. How is
that possible? Those in my network who
are close to me and know about my career, know that my CP skills are at the top
of my skill set. But still only two
endorsements.
I think that observation is indicative of
the problem. I believe that there is a
perception that if your company/WFM team has ‘a’ Capacity Plan, you therefore have an ‘accurate’ capacity plan. Anyone
can do it. It is a commodity that
doesn’t require any specialized skills.
From my experience, that’s just not true. And you are certainly not going to get the
right answer from your WFM software – don’t get me started on this one – I see
a therapist who helps we work through these deep-seated frustrations. The key is learning to manage your
expectations – the fewer you have of your WFM software, the less therapy you
will need. 😊
Let’s
review the ABC’s of CP
There is a basic formula at the root of our
capacity plan that tells us what our FTE requirements will be. The hard part is figuring out what the
correct value is for each of these variables:
Ok – let’s call it the ABC’s and D of CP. This is a real basic formula that all of us
can remember as we build out the models that help us understand the value of
each. Here is what each variable
represents:
- Volume = A
- Productivity = B
- Occupancy = C
- Utilization = D
So, expanding this out a little bit, we get
the following:
There is also another factor in the
denominator that sinks up with our volume.
If we are using ‘weekly’ volume, then we would have a factor of 40 hours
in the denominator – assuming that 1 FTE works 40 hours of regular shift time
during a week. Utilization is the
inverse of ‘shrinkage,’ which we will discuss later.
Note:
It is possible to derive your FTE requirements using multiple steps in
your maze of spread sheets, as long as the integrity of this formula is not
compromised.
Which of these variables do you think will
pose the biggest challenge to figure out?
From an effort perspective, accurately tracking your shrinkage will
require the most time and effort. Occupancy
will require some unique modeling techniques.
Volume and HT are a little more strait forward, but still require some
thought before inserting values into this formula.
Now, let’s take a deeper dive into some of
the challenges we face in determining the correct values for each of these
variables.
Occupado
– The hotel is full
Why is Occupancy even a part of this
equation? Simple math tells us that if I
have 1,000 phone calls to answer, and our HT is 6 min (10 calls per hour), we
can do the math and figure out that we need 100 hours of work done to answer
those calls [ 1000 calls / (10 calls/hr)] = 100 hours. Occupancy tells us how much of our agents’
phone log-in time is busy speaking with customers (Talk time + ACW). An agent with 90% occupancy, had 10% idle
time in between calls.
Why do we have to account for Occupancy and
add additional time to our staffing models in order to meet service level targets? It’s because we
are managing a ‘real-time’ process. Real-time processes don’t behave like a
back-office activity. There is
variability in the behavior of how customers arrive and are processed within
the system: how calls arrive (uniformly
or some funky distribution like Poisson),
How many calls arrive relative to our forecast, and how long the call occupies
an agent – one call might be only 2 minutes and the next call might be 9
min. These two variables are assumed to
follow an exponential distribution.
The Erlang formulas try to help us quantify the variability in the
process and tell us what our occupancy will be in order to meet SL’s. Figuring out our ‘true’ occupancy at Service
Level is the ‘Holy Grail’ in this entire process. Variability is the enemy. The more variability we have, the less
efficient we will be and it will drive down our occupancy and increase our
staffing. Refer to the ‘professionally’
modeled graph below:
At low volumes there is a LOT of
variability in how many calls will arrive which drives down our occupancy. At high volumes, the process behaves much
more predictably, both from how many calls will arrive relative to the forecast,
AND what our average HT will be. The
latter means that the process will be more predictable which will translate to
a higher occupancy and higher productivity.
This is similar to the concept of ‘risk pooling’ variance. This principle of managing variability is
used in Inventory Management and in the financial markets with buying a Mutual
Fund instead of individual stocks.
Deriving
Occupancy
If we were able to display our forecast occupancy (at service level),
based on the occupancy values per the Erlang formula, it would look something
like the graph below. And then if we
took a true weighted average calculation across all of the intervals in the day
and came up with an occupancy value of 80% - Is that the value that we would
want to use in our Capacity Plan? (this is assuming Erlang is correct – which
it ain’t).
The answer is ‘NO.’ There is another issue at play here, and that
is our ‘schedule efficiency.’ Poorly
aligned schedules will cost us more resulting in lower occupancy.
So how can we account for all of the flaws in Erlang and ALSO
quantify the inefficiencies in our schedules?
There is a fairly easy modeling trick that will give us the answer.
We can build a scatter plot of our ACD
data, and it will give us the answer we are looking for . The two variables that we are interested in
plotting against each other are: 1)
Occupancy and 2) Service level. The SL
could either be ASA or SL % in XX seconds.
There are 3 different levels at which you could possibly model your ACD
data: Interval, Daily, or Weekly.
For this exercise, you want to look at your
Daily
results. There are a few tricks to
building these scatter plots, but hopefully it’s pretty strait forward.
· - You will want to use 2-3 months
of recent data
· - Manually remove or delete data
points that are obvious outliers.
· - If certain days of the week
(like the weekends), have noticeably different volumes – you will want to build separate models: 1 for Mon – Fri, and a
2nd for Sat / Sun. Remember
Occupancy is volume sensitive, so you would come up with two different answers.
In the example above, the breakeven point
for the SL target of 90% of calls answered within 30 seconds – is at 73%
occupancy => (.73) is the value you would use in our standard CP formula for
occupancy. This method is a far-cry from
having an accurate substitute to replace Erlang for interval level forecasting,
but it will get the job done for high level capacity planning.
As mentioned at the start of this article this is the first part of a two part series on capacity planning. Part 2 will cover everything from Shrinkage, handle times, and Tom Cruise in Mission Impossible, the aim is to have part 2 posted this coming Sunday. If you have not already subscribed by email to this blog, then be sure to do so, in order not to miss out on part 2 and future weekly articles.
May the FORCE be with you, and a LOT of luck.
Once again, Doug and I welcome your thoughts and comments.
Blair
McGavin
https://www.linkedin.com/in/blair-mcgavin-3b97542
