hello and welcome to this presentation
of crystal ball fundamentals my name is
Fred Chicano crystal ball solution
specialist and Six Sigma master black
belt let's begin with an overview of
crystal ball fundamentals we'll start
with some basic terminology within the
crystal ball system there are only three
variables we define as the first
variable and assumption and an
assumption is no more than or nothing
more than the probabilistic input to a
crystal ball model or to a risk model
this assumption defines the range
through usage of a probability
distribution and it is the probabilistic
representation of an uncertain model
input it is frequently called an input
variable X an independent variable a
random variable or a probability
distribution the second crystal ball
variable here as a yellow road sign is
called a decision variable when we start
an optimization on a problem the
decision variable represents a variable
that is a controllable input or a factor
whose setting is solved by the
optimization engine these are variables
that typically can be resolved through
management decision we like to refer to
these or they are frequently referred to
as control input a factor such as in a
design of experiments or a decision
variable and finally a forecast variable
a forecast variable is our output where
a an assumption and a decision variable
are both inputs to an
our forecast represents a variable being
designated as a response variable or
output that we would like to analyze
there can be one or many of these within
a simulation run and if it really is a
crystal ball object for designating a
KPI or a response variable for which
simulation results are generated and
charts are produced it is typically
known as an output variable a response
variable Y f of X or a dependent
variable next we turn our attention to
how do we actually launch crystal ball
there are in fact several methods the
first decision to be made is would you
like crystal ball to start automatically
for you each time you start Microsoft
Excel if that is so then what we will do
is take the approach on the left what
you'll do is you'll go to your Start
menu under all programs under oracle
crystal ball and we will invoke the
crystal ball application manager a a
dialog box will pop up and what you will
see is at the top of that dialog box you
will see a check box that says when
starting Microsoft Excel automatically
launch crystal ball if we then click OK
after that each time we start Excel
crystal ball will automatically start as
well if we choose not to do that
approach we can start crystal ball
manually each time we can typically do
that one of several ways we can first of
all go into the menuing system under the
Start menu
and do Start programs crystal ball and
then select the crystal ball application
icon displayed in the upper right of
your screen circle
or from the Start menu
once you use crystal ball several times
the crystal ball icon will appear there
and then a fourth method would be for
you to create an application shortcut on
your desktop defining a probabilistic
input is a several steps as follows the
first thing we do is from the crystal
ball toolbar within Excel in the upper
left you will find an icon that looks
like a normal distribution and it will
be labeled define assumption if we click
on that icon we can then see a gallery
of choices that appear when these
choices appear we can select from any of
those different distribution types or
there is an option which we will see in
another presentation that allows us to
fit data that you provide and what we'll
do is goodness a fit test to see which
of the distribution types fit best
and then finally based on the
distribution you choose a dialog box of
that distribution will pop up and you'll
be able to fill in the appropriate
parameters then click OK next word about
selecting one of those distributions
families wanted to point out that when
selecting a distribution to represent or
characterize either uncertainty or
variation first and foremost there seems
to never be enough information so there
always is associated with this process
ambiguity the sources of available
information to you for this selection
are theories and facts about the
underlying business process
or system obviously observed data
historical data if you will and expert
opinion subject matter expertise
obviously theory and facts are better
than opinion however you should not
discount that opinion and a lot of data
can be used to support or disprove a
theory we want to use sensitivity
analysis to test the impact of our
choice in other words if I characterize
a distribution using subject matter
expertise I can still then perform the
analysis but at the end of that analysis
when I run my sensitivity analysis I can
tell whether my selection was critical
to the overall problem or not if it is
critical that may indicate that I have
to go collect more data or data
simulation outputs are the process of
defining your forecasts and in this
regard what we do is we select a cell
within our spreadsheet that represents
essentially a calculation it can be our
final output or it can be any other cell
that's an intermediate result that has a
formula in it now having selected that
cell then I can come up on the crystal
ball toolbar and click the third icon
circled here in this diagram which says
define forecast when I do that again a
dialog box will pop up and typically
what we will provide is a name so that
we can distinguish it from a cell
reference and furthermore I can specify
additional parameters such as here a
lower specification limit an upper
specification limit and a target that we
would like to achieve
next once we have defined our inputs or
our assumptions and we've designated our
output or outputs as forecasts we run a
simulation running a simulation
typically is a two-step process
in which we set or establish the number
of trials or experiments that we would
like to run here circled in red to the
right and after having done that we can
then move over to the other circled icon
which is the start icon once clicked
then the simulation will proceed until
we have reached the 2,000 trials so
indicated and at the conclusion of the
simulation a forecast chart or a
distribution will pop up and complete
with the chart and statistics and some
data entry fields at the bottom of that
screen that allow us to actually analyze
in an ad hoc fashion the simulation
results indicate the range of possible
outcomes for that output variable they
can be superimposed on the with the
lower specification limit and upper
specification limit to see what portion
of the number of trials succeeds or
falls within spec or fails and falls
outside of spec specifications can be
two-sided such as you are looking at or
it could be one-sided in the case of an
analyzing cycle times or so or costs or
it could be one-sided in terms of
analyzing our revenues using for example
lower specification limits again quality
metrics can be reported on in the
simulation to examine the goodness of of
the result
next what we would choose to do is run a
sensitivity analysis to determine which
of our uncertain or variable inputs is
causing the preponderance of variance in
our output we do this to for two reasons
one is to help validate the model to see
if we actually believe that that those
are in fact the variables that that
should have the most variation or the
most impact on the result and secondly
we do this when we have started our
continuous improvement or our
improvement efforts in order to
establish an improvement strategy here
in an improvement strategy we would be
looking to move the nominal values or
decrease or allocate the variation or
move the spec limits and this particular
sensitivity will tell us which variables
we could choose to optimize with that
would be most expedient or most cost
effective in the optimization process
finally if we put together all pieces of
this analysis the simulation the
sensitivity analysis and the
optimization here we wind up with the
overall crystal ball simulation process
here we start by building a Microsoft
Excel spreadsheet we then turn our
attention to those inputs in the
spreadsheet or your model that you feel
to have either uncertainty or variation
and what we do is we define them as
probabilistic input by building crystal
ball assumptions next we define an
output or outputs that we want to have
simulation results for and we do that by
defining fork
next we set the number of simulation
trials and after having done so we then
run the simulation interpret the
forecast chart and run and evaluate the
sensitivity at this point this completes
the simulation and gives us at least the
key probabilistic inputs that would be
of high value in our optimization step
we will define an optimization process
in a later video this concludes the
session on crystal ball fundamentals
thank you