I managed a project in the past that required a detailed
analysis of the demand curve encountered pertaining to demand for
services. The project was a consultant
model utilizing SME (subject matter experts) resources and project management
resources to help the business better manage its demand, properly utilize its
supply, and increase revenues.
The first step in determining current utilization was to
determine the true demand for services.
In order to do this, the business measured statistics for current usage
levels as shown in the graph below:
Notice in the graph that the utilization statistics were
monitored for 3 different services offered considering the age ranges shown on
the left. While this does give them a
view into the service utilization and does give some view into the demand for
these services based upon certain demographic age ranges, it does not
accurately develop a demand curve.
Consider that the age categories are of different
cross-sections. The first is a span of 4
years; the second is a span of 13 years; the third is a span of 9 years and the
last is a span of 24 years. The input
from the business regarding the choice for the demographics used is based upon
the variances found within these age divisions.
It was determined that there is very little movement or variation in the
51-75 category, so they tend to analyze this as a single category since the 51
y/o chooses services very similarly to the 75 y/o.
While this may be true, consider that to accurately chart a
demand curve to show the true curve without affecting the elasticity of the
demand curve, the incremental measurements must be congruent in size. Also, the more incremental the measurements,
meaning measuring at 1/2c instead of a 1c increment will allow you a better view
into the nuances experienced with the curve.
By analyzing congruent demographic measures, and the smaller
these divisions are, the more accurate the curve will be, you can more
precisely capture the behavior of demand.
This is simply the first step in capturing the demand. Once you establish the congruent size of
measure you want to obtain, you need also to consider the duration of data you
wish to obtain. Within every industry
there are seasonal variations of demand, so capturing but a short window of
service utilization will cause the results to not be an accurate depiction of
the true demand.
When considering the length of time to consider for capture,
determine the fluctuations of your industry or specific business’s
experiences. Consider that time line,
and be sure to encompass this entire span.
In this measurement, just as with the demographic measure used, consider
using smaller increments. Plotting more
incremental data will give a more specific measure and accurate measure to the
demand curve through your timeline.
Let’s look at the example provided below:
Service A
Service B
Service C
Referencing the above charts, imagine if you had only
gathered statistics for any one month.
The fluctuations for each demographic throughout the year are
significant. It may even be necessary to
then take the aggregate demand needs and use a smoothing method and even
consider utilizing a multiplier to consider smoothing for seasonality depending
upon the type of resourcing model or inventory management system you use.
For this example however, we will remain basic and notice
simply that the demand fluctuates month to month for these services. This is significant when it comes to determine
the supply needed and how to deliver it cost effectively. For thoughts around this example, if it takes
one employee to provide one instance of each service, imagine the labor force
projections you can develop by accurately gathering the true demand curve.
If you are producing a good, consider that you can level
your resources and produce more than the demand when demand is low and below
the demand when the demand is high, maintain steady production, and fulfill the
supply needs. This is something
accomplished through the use of the seasonality smoothing equation. It helps you develop the demand calculation
but it helps smooth fluctuations you may encounter throughout your product life
cycle so you can better manage labor force projections to maintain a steady
workforce.
Similarly, if you do have an extremely accurate demand
curve, and find that your work is extremely seasonal, once you have analyzed
the cost of maintaining a steady workforce, paying the overhead that comes with
it, and the cost to store materials and front the capital to make the product,
it may be determined that you want to move the size of your workforce to match
the demand on your good or service.
Another key consideration in production of a good or
offering of a service you should consider is, “do you want to operate at 100%
capacity?” This is an important
consideration for several reasons:
- Do you encounter “add-ons” to contracts from customers
- Do you encounter delays in production or deliver
- Do your employees take vacation or call in sick
-
· Do you want the ability to accept last minute contracts
Hopefully you get the point.
Having available capacity helps mitigate these risks encountered in all
businesses, and provides you the capacity to help with client related issues or
personnel fluctuations. The amount of capacity
to operate at is up to your specific business, however many industries utilize
the 80% rule.
Hopefully the thoughts offered in this brief post can help
open up discussion within your organization around properly developing a demand
and supply analysis. These are
instrumental in labor force projections, inventory projections, revenue cycles,
and many other measures and should be something you do with precision to ensure
your business the best tools to learn from.
Chris Thompson PMP,
SSYB
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