US Army Logistics Management College
Part 1: Simulation Modeling
w/ Built in Excel Tools
Simulation - 1
US Army Logistics Management College
Walton Bookstore
• In August, Walton Bookstore must decide how many of next year’s
nature calendars to order.
• Each calendar costs the bookstore $7.50 and is sold for $10.
• After February 1 all unsold calendars are returned to the publisher
for a refund of $2.50 per calendar.
• Walton believes that the number of calendars it can sell by February
1 follows this probability distribution.
Calendars
Demanded
100
150
200
250
300
Probability
0.30
0.20
0.30
0.15
0.05
MyWalton1.xls
• Walton wants to simulate 1000 replications for order quantities
100, 125, 150, … , 300 to determine the quantity to order so as to
maximize the expected profit from calendar sales.
Simulation - 2
US Army Logistics Management College
Step 1: Identify Inputs
Walton Bookstore 1
• Constant Inputs (No Uncertainty):
Unit Cost (B4):
$7.50
Unit Price (B5):
$10.00
Unit Refund (B6):
$2.50
Order Quantity (B9): 200
Note
Named
Cells
• Random Inputs (Probability Distribution):
(D5):
0
(D6:D9): =D5+F5
Random # (B19): =Rand()
Excel Math&Trig function
Demand (C19): =VLOOKUP(B19,Lookup,2)
Excel Lookup&Reference function
Simulation - 3
US Army Logistics Management College
Step 2: Build Basic Model
Logic to Convert Inputs into Outputs
• Revenue (D19): =UnitPrice*MIN(C19,OrderQuan)
Min is Excel Statistical function
• Cost (E19): =UnitCost*OrderQuan
• Refund (F19): =UnitRefund*MAX(OrderQuan-C19,0)
Max is Excel Statistical function
• Profit (G19): =D19-E19+F19
• Copy (B19:G19) to (B19:G1018)
• Name (G19:G1018) “Profits”
Simulation - 4
US Army Logistics Management College
Step 3: Create Summary Statistics
Walton Bookstore 1
• Average Profit (B12): =AVERAGE(Profits)
• Stdev Profit (B13): =STDEV(Profits)
• Minimum Profit (B14): =MIN(Profits)
• Maximum Profit (B15): =MAX(Profits)
• 95% Confidence Interval
Lower limit (E12): =AvgProfit-NORMSINV(0.975)
*StdevProfit/SQRT(1000)
Upper limit (E13): =AvgProfit+NORMSINV(0.975)
*StdevProfit/SQRT(1000)
Simulation - 5
US Army Logistics Management College
Step 4: Determine the “Best” Order Quantity
One-Way Data Table Method
Walton Bookstore 1
• Identify Table Output (B1022):
=AvgProfit
• Select range (A1022:B1031)
•Select:
Data + What If Analysis + DataTable
• Set the Column Input cell to B9
Data table for average profit
versus order quantity
Order Quantity
AvgProfit
$197.38
100
$250.00
125
$258.13
150
$266.25
175
$231.81
200
$197.38
225
$112.31
250
$27.25
275
($88.38)
300
($204.00)
Simulation - 6
US Army Logistics Management College
Step 5: Graph the Results
Walton Bookstore 1
• Select Insert + Column Chart + Clustered Column
• Choose Select Data
• Add Series (Series Name: $B$1021, Series Values
$B$1023:$B$1031)
• Edit Horizontal Category Axis Labels (Label Range
$A$1023:$A$1031)
Simulation - 7
US Army Logistics Management College
Two-Way Data Table Method
Walton3.xls
• Note the change in the basic model.
Demand (A19): =VLOOKUP(RAND(),Lookup,2).
MyWalton3.xls
Simulation - 8
US Army Logistics Management College
Create the Two-Way Data Table
Walton3.xls
• Identify Table Output (A23): =Profit
• Select range A23:F1023 for the Data Table
•Select: Data + What If Analysis + DataTable
• For the Row input cell enter B9
• For the Column Input cell enter G23 or any
other blank cell you choose.
Simulation - 9
US Army Logistics Management College
Part 2: Intro to
Simulation Modeling
with @Risk
Simulation - 10
US Army Logistics Management College
Walton Bookstore Revisited
Walton4.xls
• Recall that Walton Bookstore buys calendars for $7.50, sells them at
a regular price of $10, and gets a refund for all calendars that cannot
be sold.
• The company does not know exactly how many calendars its
customers will demand, but it does have historical data on demands
for similar calendars in previous years. Walton wants to use these
historical data to determine a reasonable probability distribution for
next year’s demand for calendars.
• Walton wants to use this probability distribution, together with @Risk,
to simulate the profit for any particular order quantity.
• Walton eventually wants to find the “best” order quantity.
MyWalton4.xls
Simulation - 11
US Army Logistics Management College
Solution Approach
Walton Bookstore 4
1. Use BestFit to identify demand probability
distribution.
2. Use @Risk to Simulate 1000 runs for each
potential order quantity.
3. Use @Risk RiskSimTable function to
determine the “best” order quantity.
(does the work of the Data Table)
Simulation - 12
US Army Logistics Management College
Opening an Existing @Risk File
1. Open @Risk for Excel
2. Open the file: MyWalton4.xls
3. Use File + Save As to save this file under a
different name (such as Class MyWalton 4)
Simulation - 13
US Army Logistics Management College
Fitting a Probability Distribution
• The historical demand data is on the Data tab of
Walton4.
• The hard part is to find historic data that is appropriate for
estimating the probability distribution of demand for next
year’s calendars.
• To select a probability distribution to match the
histogram well, we can use @Risk’s fitting ability.
Simulation - 14
US Army Logistics Management College
3. Copy and Paste Data into the
FitTab
Fitting a Probability Distribution
• Click on the “Show Excel Window” button
• Select the range A7:A121.
• Click on the copy button.
• Click on the “Show @RISK-Model window” button.
• Select Edit + Paste from the Menu Bar
Simulation - 15
US Army Logistics Management College
4. Select Candidate Distributions
Fitting a Probability Distribution
To see the candidate probability distributions
from which to choose, click on the SpecifyDistributions-to-Fit button from the tool bar.
• You can check as many of
the candidates as you like.
• Stick with familiar
distributions such as the
normal and triangular.
• Clicked on “OK” which
accepts the defaults .
Simulation - 16
US Army Logistics Management College
5. Do the Fitting
Fitting a Probability Distribution
Click on the “Fit-Distributions-to-Input-Data”
button in the tool bar .
• Note the distributions are
ranked by the Chi-Sq test.
• Change “Rank by” to K-S.
The Weibull is better than
the Normal.
• Change “Rank by” to A-D.
The Normal is better than
the Weibull
For Normal:   168.1,   57.6
Simulation - 17
US Army Logistics Management College
Solution Approach
Walton Bookstore 4
1. Use BestFit to identify demand probability
distribution.
2. Use @Risk to Simulate 1000 runs for
each potential order quantity.
3. Use @Risk RiskSimTable function to
determine the “best” order quantity.
(does the work of the Data Table)
Simulation - 18
US Army Logistics Management College
Step 1: Identify the Input Cell(s)
Creating the @Risk Simulation Model
1. Enter the values for the mean and standard
deviation estimated by BestFit!
 Mean = 168.1 in cells E4
 StDev = 57.6 in cells E5.
2. In cell A13, use the @RISK
Distribution function
RiskNormal within the
Excel Math & Trig function
ROUND to enter
the formula:
=Round(RiskNormal
(MeanDem,StdevDem).
Simulation - 19
US Army Logistics Management College
Step 2: Build the Basic Model
Creating the @Risk Simulation Model
• Revenue (B13):
=UnitPrice*MIN(OrderQuan,Demand).
• Cost (C13):
=OrderQuan*UnitCost
• Refund (D13):
=UnitRefund*MAX(OrderQuan-Demand,0)
Still the Hardest Part
and the Heart of Simulation
Simulation - 20
US Army Logistics Management College
Step 3: Identify the Output Cell(s)
Creating the @Risk Simulation Model
• In cell E13 enter the formula for Profit:
=B13+D13-C13
• Designate cell E13 as an @Risk output cell by clicking on the
the Add Output Cell button on the @Risk toolbar.
=RiskOutput() + B13+D13-C13
• Any number of cells can be designated in this way as output
cells.
They are typically “bottom line values of primary interest.”
• Click on the “Display List of Outputs &Inputs” button on the
@Risk toolbar to check the list at any time.
Simulation - 21
US Army Logistics Management College
Step 4: Create Summary Statistics
on the Output Cell(s)
Creating the @Risk Simulation Model
1. In cell B16, use the @RISK Statistics function:
=RiskMin(Profit)
2. In cell B17, enter:
=RiskMax(Profit)
3. In cell B18, enter:
=RiskMean(Profit)
4. In cell B19, enter:
=RiskStdDev(Profit)
Simulation - 22
US Army Logistics Management College
Step 5: Specify the Simulation Settings
Creating the @Risk Simulation Model
• Click on the “Simulations Settings” button.
• Click on the “Iterations” tab in the Simulation Settings
dialog box.
 Set # Iterations to 1000.
 Set # Simulations to 1.
 Check Update Display.
• Click on the “Sampling” tab in the Simulation Settings
dialog box.




Set Sampling Type to Latin Hypercube .
Set Standard Recalc to Monte Carlo .
Set Random Generator Seed to Choose Randomly .
Set Collect Distribution Samples to All .
Simulation - 23
US Army Logistics Management College
Step 6: Specify the Report Settings
Creating the @Risk Simulation Model
• Click on the “Report Settings” button.
• For At the End of Each @RISK Simulation:
 Check Show Interactive @RISK
Results Window.
 Check Generate Excel Reports
Selected Below.
• For Excel Reports:
 Check Simulation Summary .
 Check Detailed Statistics.
• For Excel Reports:
 Check Active Workbook.
Simulation - 24
US Army Logistics Management College
Step 6: Run the @Risk Simulation
Creating the @Risk Simulation Model
• To run the simulation, Click on the “Start Simulation”
button.
• In the @Risk Results window
 To see Summary Statistics, use the “Summary Statistics
Window” button.
 To see Detailed Statistics, use the “Detailed Statistics
Window” button.
Simulation - 25
US Army Logistics Management College
Analyzing the Output
Walton Bookstore 4
@Risk generates a large number of output measures.
Summary Report. Assuming that the top box was
checked in the @Risk Reports dialog box, we are
immediately transferred to the @Risk Results window. This
window contains the summary results shown here.
Simulation - 26
US Army Logistics Management College
Detailed Statistics
Analyzing the Output
All of the
information
in the
Summary
Report is
here, plus
some.
Simulation - 27
US Army Logistics Management College
Charts
Analyzing the Output
To create a histogram of the 1,000 profits:
In the left pane of the Results window, click on Profits
From the menu bar select: Insert+Graph+Histogram
Note the 27.4%
chance of
losing money
Simulation - 28
US Army Logistics Management College
Solution Approach
Walton Bookstore
1. Use BestFit to identify demand probability
distribution.
2. Use @Risk to Simulate 1000 runs for
each potential order quantity.
3. Use @Risk RiskSimTable function to
determine the “best” order quantity.
(does the work of the Data Table)
Simulation - 29
US Army Logistics Management College
Using RISKSIMTABLE
Walton Bookstore 5
• Walton’s ultimate goal is to choose an order quantity that provides a
large average profit.
• We could rerun the simulation model several times, each time with a
different order quantity in the OrderQuan cell, and compare the
results.
• The RISKSIMTABLE function in @Risk enables us to obtain a fair
comparison quickly and easily.
• There are two modifications to the previous model.
– We will create a list of order quantities to test.
– Instead of entering a number in cell B9 (the Order Quantity), we
will use the @RISK function RISKSIMTABLE( ).
MyWalton5.xls
Simulation - 30
US Army Logistics Management College
@Risk Simulation
Walton Bookstore 5
Step 1: Identify Input Cell(s):
(A13): =ROUND(RiskNormal(MeanDem,StdevDem),0)
Step 2: Build the Basic Model:
(D9:L9): add 9 order quantities 100, 125, 150, …, 300
Step 3: Identify Output Cell(s):
(E13): =B13-C13+D13
Click:
=RiskOutput() + B13-C13+D13
Step 4: Instead of entering a number in cell B9, enter
=RiskSimtable(OrderQuanList)
Make sure cells D9:L9 are named OrderQuanList
Simulation - 31
US Army Logistics Management College
Step 5: Specify the Simulation Settings
Walton Bookstore 5
• Click on the “Simulations Settings” button.
• Click on the “Iterations” tab in the Simulation Settings
dialog box.
9 Order
 Set # Iterations to 1000.
Quantities
 Set # Simulations to 9.
 Check Update Display.
• Click on the “Sampling” tab in the Simulation Settings
dialog box.




Set Sampling Type to Latin Hypercube .
Set Standard Recalc to Monte Carlo .
Set Random Generator Seed to Choose Randomly .
Set Collect Distribution Samples to All .
Simulation - 32
US Army Logistics Management College
Step 6: Specify the Report Settings
Walton Bookstore 5
• Click on the “Report Settings” button.
• For At the End of Each @RISK Simulation:
 Check Show Interactive @RISK
Results Window.
 Check Generate Excel Reports
Selected Below.
• For Excel Reports:
 Check Simulation Summary .
 Check Detailed Statistics.
• For Excel Reports:
 Check Active Workbook.
Simulation - 33
US Army Logistics Management College
Step 6: Run the @Risk Simulation
Walton Bookstore 5
• To run the simulation, Click on the “Start Simulation”
button.
• In the @Risk Results window
 To see Summary Statistics, use the “Summary Statistics
Window” button.
 To see Detailed Statistics, use the “Detailed Statistics
Window” button.
Simulation - 34
US Army Logistics Management College
Multiple Sources of Uncertainty
Walton Bookstore 6
• As in previous examples, Walton needs to place an order for next year’s
calendar. We continue to assume that the calendars will sell for $10
and customer demand for the calendars at this price is normally
distributed with mean 168.1 and standard deviation 57.6. However,
there are now two other sources of uncertainty.
• First, the maximum number of calendars Walton’s supplier can supply is
uncertain and is modeled with a triangular distribution. It’s parameters
are 125, 250, and 200. Once Walton places an order, the supplier will
charge $7.50 per calendar if he can supply the entire Walton order.
Otherwise, he will charge only $7.25 per calendar.
• Second, unsold calendars can no longer be returned to the supplier for
a refund. Instead, Walton will put them on sale for $5 each after Feb 1.
At that price, Walton believes the demand for leftover calendars is
normally distributed with mean 50 and standard deviation 10. Any
calendars still left over after March 1 will be thrown away.
• Walton plans to order 200 calendars and wants to use
simulation to analyze the resulting profit.
MyWalton6.xls
Simulation - 35
US Army Logistics Management College
Part 3:
@Risk Simulation Modeling
An Example
Simulation - 36
US Army Logistics Management College
Drug Production Model with Uncertain Yields
Trying to Meet an Order Due Date at Wozac
• Wozac is a drug manufacturing company. It has recently
accepted an order from its best customer for 8,000
ounces of a new miracle drug, and wants to plan its
production schedule to meet the customer’s promised
delivery date of December 1, 2000.
• There are three sources of uncertainty that make
planning difficult.
Simulation - 37
US Army Logistics Management College
Sources of Uncertainty
Wozac Drug Company
• First, the drug must be produced in batches, and there is
uncertainty about the time required to produce a batch,
which could be anywhere from 5 to 11 days. This
uncertainty is described by the discrete distribution of this
table.
Distribution of Days to Complete a Batch
Days
5
6
7
8
9
10
11
Probability
0.05
0.10
0.20
0.30
0.20
0.10
0.05
Simulation - 38
US Army Logistics Management College
Sources of Uncertainty
My Wozac Drugs.xls
(Incomplete)
Wozac Drug Company
Continued
Wozac Drugs.xls
(Complete)
• Second, the yield (usable quantity) from any batch is
uncertain. Based on historical data, Wozac believes the
yield can be modeled by a triangular distribution with
parameters 600, 1000, 1100.
• Third, all batches must go through a rigorous inspection
once they are completed. The probability that a typical
batch passes this inspection is only 0.8. Therefore, the
probability is 0.2 that the batch fails inspection and none
of it can be used to help fill the order.
Wozac wants to use simulation to help decide how many
days prior to the due date it should begin production.
Simulation - 39
US Army Logistics Management College
Building the Basic Model
Wozac Drug Company
• Batch Index: Limit of 25 by trial & error. Big enough.
• Days (for this batch):
(B25:B48): =IF(OR(F24=“Yes”,F24=“”),” “,RiskDiscrete(Day, Probs))
The formula means:
IF Enough? = Yes or is blank
THEN Leave blank which acts as 0
ELSE RiskDiscrete(Day, Probs))
• Batch Yield: (C25:C48):
=IF(OR(F24=“Yes”,F24=“”),” “,RiskTriang($D$19,$E$19,$F$19))
• Pass Inspection? (D25:D48):
=IF(OR(F24=“Yes”,F24=“”),” “,IF(Rand()<PrPass,”Yes”,”No”))
RiskDiscrete & RiskTriang are @Risk Distrib. functions
Simulation - 40
US Army Logistics Management College
Is the Order Filled?
Col. E&F
Building the Basic Model
• CumYield (cumulative usable product) (E25:E48):
=IF(OR(F24=“Yes”,F24=“”),” “,IF(D25=“Yes”,C25+E24,E24))
IF
THEN
ELSE
Enough? = Yes or is blank
Leave blank which acts as 0
IF
this batch passed
Then Add this batch to sum
Else Use previous sum
• Enough? (Is the order filled) (F25:F48):
=IF(OR(F24=“Yes”,F24=“”),” “,IF(E25>=AmtReqd,”Yes”,”Not yet”))
IF
THEN
ELSE
Enough? = Yes or is blank
Leave blank which acts as 0
IF
CumYield>= cell B5
Then Yes the order is filled
Else No, we must do next row
Simulation - 41
US Army Logistics Management College
Run Summary Measures
Building the Basic Model
• Batches required (I23): =COUNT(B24:B48)
(count the cells that are not blank)
• Days to complete (I24): =SUM(B24:B48)
(blanks count as 0)
• Day to start (I25): =DueDate-DaysReqd
 Cell formatted for Date
 Assumes 7 day production week
I23 & I24 are @Risk Output cells,
but we’ll handle that later
Simulation - 42
US Army Logistics Management College
@Risk Summary Measures
Wozac Drug Company
For 1,000 runs, we want @Risk to Report:
• Max batches reqd (I28): =RiskMax(I23)
• How long does it take?
 Avg days reqd (I30): =Int(RiskMean(DaysReqd))
 Min days reqd (I31): =RiskMin(DaysReqd)
 Max days reqd (I32): =RiskMax(DaysReqd)
 5th perc days reqd (I33): =RiskPercentile(DaysReqd,0.05)
95th perc days reqd (I34): =RiskPercentile(DaysReqd,0.95)
• Prob of meeting any given due date (I37) :
=RiskTarget(DaysReqd,DueDate-H37)
RiskMax, RiskMean, etc., are @Risk Statistics functions
Simulation - 43
US Army Logistics Management College
Identify Output Cells
Wozac Drug Company
1. Select “Batches required”, cell I23
2. Click on the “Add Output” button.
=RiskOutput()+COUNT(B24:B48)
3. Select “Days to complete”, cell I24
4. Click on the “Add Output” button.
=RiskOutput()+ SUM(B24:B48)
Simulation - 44
US Army Logistics Management College
Specify the Simulation Settings
Wozac Drug Company
• Click on the “Simulations Settings” button.
• Click on the “Iterations” tab in the Simulation Settings
dialog box.
 Set # Iterations to 1000.
 Set # Simulations to 1.
 Check Update Display.
• Click on the “Sampling” tab in the Simulation Settings
dialog box.




Set Sampling Type to Latin Hypercube .
Set Standard Recalc to Monte Carlo .
Set Random Generator Seed to Choose Randomly .
Set Collect Distribution Samples to All .
Simulation - 45
US Army Logistics Management College
Specify the Report Settings
Wozac Drug Company
• Click on the “Report Settings” button.
• For At the End of Each @RISK Simulation:
 Check Show Interactive @RISK
Results Window.
 Check Generate Excel Reports
Selected Below.
• For Excel Reports:
 Check Simulation Summary .
 Check Detailed Statistics.
• For Excel Reports:
 Check Active Workbook.
Simulation - 46
US Army Logistics Management College
Run the @Risk Simulation
Wozac Drug Company
• To run the simulation, Click on the “Start Simulation”
button.
• In the @Risk Results window
 To see Summary Statistics, use the “Summary Statistics
Window” button.
 To see Detailed Statistics, use the “Detailed Statistics
Window” button.
Simulation - 47
US Army Logistics Management College
Part 4:
Using TopRank with @Risk
for Powerful Modeling
Simulation - 48
US Army Logistics Management College
New Product Development
At SIMTEX
• SimTex, a pharmaceutical company, is in the early
stages of developing a new drug called Biathnon. As
with most new drugs, the future of Biathnon is highly
uncertain. For example, its introduction into the market
could be delayed, pending tests by the FDA. Also, its
market could be diminished by a potential rival product
from SimTex’s competition.
• SimTex has identified a number of key inputs that will
affect Biathnon’s future profitability:
Simulation - 49
US Army Logistics Management College
Key Inputs Affecting Profitability
SIMTEX Product Development
1. Number of years after product is developed until it is produced (due
to potential FDA delays).
2. Number of years for which the product sells.
3. Initial cost incurred in developing the product.
4. Salvage value obtained from equipment after production of the
product has been discontinued.
5. Fixed production cost incurred during years in which the product is
manufactured.
6. Unit cost of producing the product.
7. Unit price for the product.
8. Initial demand for the product during first year it is sold.
9. Annual percentage growth in demand for the product.
10. Percentage of demand for the product that is lost to the competition.
11. Discount rate used to discount cash flows from the product.
Simulation - 50
US Army Logistics Management College
Key Questions
SIMTEX Product Development
• How do changes in the inputs affect the key
output, the Net Present Value of Biathnon over
its lifetime?
• Which inputs have a major affect on the Net
Present Value of Biathnon over its lifetime?
• How can SimTex use TopRank and @Risk to
analyze this problem?
SimTex1.xls
Simulation - 51
US Army Logistics Management College
Understanding SIMTEX1: Cell B29
The Key to understanding SIMTEX1 is the
TIMING in Row 29
(Last Prod Yr)
B13
B13+B14
B29=IF(AND(B27>Delay,B27<=(Delay+Life)), “Yes”,”No”)
The formula means:
IF Year No. > Delay and <= Delay + Life
THEN
“Yes” = Product IS being Produced this year
ELSE “No” = Product is NOT being Produced this year
Simulation - 52
US Army Logistics Management College
Financial Formulas (C30:C36)
Understanding SIMTEX1
C30: Fixed Cost
=IF(C29=“Yes”, FixCost,0)
C31: Total Demand
=IF(AND(B29=“No”,
C29=“Yes”),
InitDem,
IF(C29=“Yes”,
B31*(1+DemGrowth),
0))
If this is a production Year
Then Fixed cost (B17) is incurred
Else Fixed Cost = 0
If Last year was not a prod. year (B29=“No”)
AND This year is a production Year (C29=“Yes”)
Then this is 1st Prod year and
Total Demand = InitDem (B20)
Else If this is a production Year (C29=“Yes”)
(It cannot be the initial prod. year)
Then Total Demand (C31) =
Last years demand (B31) times
1 + DemGrowth (B21)
Else this is not a prod year
And Total Demand = 0
Simulation - 53
US Army Logistics Management College
Financial Formulas (continued)
C32: SimTex’s Demand
=IF(C31=0,0,
C31*(1-PctDemLost))
C33: Variable Cost
=IF(C32=0,0,
C31*(1-PctDemLost))
C34: Revenue
=IF(C32=0,0,
C31*(1-PctDemLost))
If Total Demand = 0, Then SimTex’s Demand =0
Else SimTex’s Demand = Total Demand
less part lost to competition
If SimTex’s Demand = 0, Then Variable Cost =0
Else Var. Cost = SimTex’s Demand
* Unit Cost (B18)
If SimTex’s Demand = 0, Then Revenue =0
Else Revenue = SimTex’s Demand
* Unit Price (B19)
C35: Salvage Value
=IF(AND(C29=“Yes”,D29=“No”), If This is prod. yr & next yr is not
SalvVal,
Then salvage val. incurred
0)
Else no salvage val. incurred
C36: Net Profit =-C28-C30-C33+C34+C35
Simulation - 54
US Army Logistics Management College
Calculate the Net Present
Value
B38=NPV(DiscRate,C36:AF36)+B36
Or
B38=NPV(DiscRate,Profits)+B36
Where
Profits = C36:AF36
Simulation - 55
US Army Logistics Management College
Evaluating Profitability
SIMTEX Product Development
Now that the model is developed we can:
• Use trial and error to see how the NPV reacts to
changes in the inputs.
• Use data tables to see how the NPV reacts to
changes in the inputs.
• However, TopRank does this easily.
Simulation - 56
US Army Logistics Management College
Evaluating Uncertainty
SIMTEX Product Development
• Change the input section to reflect
uncertainty
SimTex2.xls
Simulation - 57
US Army Logistics Management College
TopRank’s RISKVARY function
Modify the range B13:B23 to use:
=RISKVARY(Expected value, low range, high range,
Range type, #Steps)
where :
Expected value is the base case
Low range is the smallest possible value for the input
High range is the largest possible value for the input
Range type is 0,1, or 2 and determines the way
minimum and maximum should be entered
#Steps is the number of values from minimum to
maximum to use for this input
For B13: =RiskVary(D13,C13*D13,E13*D13,2,8)
Simulation - 58
US Army Logistics Management College
Using TopRank
To use TopRank, we proceed in three steps
very much like in @Risk:
1. use the Change Settings button
2. use the Add Output Cells button to select one or
more output cells
3. use the Run What-if Analysis button to perform the
calculations.
Simulation - 59
US Army Logistics Management College
Using TopRank: Step 1
Avoid Lot’s of Results You Probably
Don’t Want
1. Click on the Change Settings button
2. Click on the Input ID tab
Then UNCHECK the
“Automatically
Insert AutoVary
Functions” box.
3. Click on OK
Simulation - 60
US Army Logistics Management College
Using TopRank: Step 2
Identify Output Cells
1. Select the NPV cell (B38)
2. Click on the “Add Output Cells”
button.
Simulation - 61
US Army Logistics Management College
Using TopRank: Step 3
Run the Program
Finally, run the analysis by clicking on
the “Run What-if Analysis” button.
TopRank then varies each input cell from its
minimum to maximum, using the number
of steps you specified and keeping the
other inputs at their base levels, and
keeps track of all the NPVs.
Simulation - 62
US Army Logistics Management College
Tornado Charts
Interpreting TopRank Results
•
•
Perhaps the best way to understand TopRank results is
with a tornado chart.
To create a tornado chart:
1. Click on the Graph button in the TopRank screen.
2. Choose: tornado
Simulation - 63
US Army Logistics Management College
@Risk Simulation
SIMTEX Product Development
• We will run an @Risk simulation to estimate the
distribution of NPV earned by Biathnon.
• We will keep all inputs other than the five key
inputs fixed at their base values.
• We will use a triangular distribution for each of
the random inputs: product lifetime, unit price,
unit cost and initial demand
• We will vary the discount rate systematically with
the RISKSIMTABLE function.
Simulation - 64
US Army Logistics Management College
Modify the TopRank Model
@Risk Sim - SIMTEX Prod. Dev.
• Adjust the data for the five key Inputs
SimTex3.xls
• Enter the @Risk formulas in random input cells. B14 is:
=RiskTriang(E14,F14,G14)
• In cell B23 use the RISKSIMTABLE function:
=RiskSimTable(DiscRateList)
• Select cell B38. Designate it the @Risk Output
cell by clicking the “Add Output Cell” button.
Simulation - 65
US Army Logistics Management College
Specify the Simulation Settings
@Risk Sim - SIMTEX Prod. Dev.
• Click on the “Simulations Settings” button.
• Click on the “Iterations” tab in the Simulation Settings
dialog box.
 Set # Iterations to 500.
 Set # Simulations to 4.
 Check Update Display.
• Click on the “Sampling” tab in the Simulation Settings
dialog box.




Set Sampling Type to Latin Hypercube .
Set Standard Recalc to Monte Carlo .
Set Random Generator Seed to Choose Randomly .
Set Collect Distribution Samples to All .
Simulation - 66
US Army Logistics Management College
Specify the Report Settings
@Risk Sim - SIMTEX Prod. Dev.
• Click on the “Report Settings” button.
• For At the End of Each @RISK Simulation:
 Check Show Interactive @RISK
Results Window.
 Check Generate Excel Reports
Selected Below.
• For Excel Reports:
 Check Simulation Summary .
 Check Detailed Statistics.
• For Excel Reports:
 Check Active Workbook.
Simulation - 67
US Army Logistics Management College
Run the @Risk Simulation
SIMTEX Product Development
• To run the simulation, Click on the “Start Simulation”
button.
• In the @Risk Results window
 To see Summary Statistics, use the “Summary Statistics
Window” button.
 To see Detailed Statistics, use the “Detailed Statistics
Window” button.
Simulation - 68
US Army Logistics Management College
To Complete the Worksheet
SIMTEX Product Development
• View the “Detailed Statistics Window”
• Select and copy the mean and standard deviations
for the four simulations.
• Paste it into range B42:E43
• In cell B46 enter formula: =B42-1.96*B43/SQRT(500)
Simulation - 69