Session 10b
Overview
• Marketing Simulation Models
• New Product Development Decision
– Uncertainty about competitor behavior
– Uncertainty about customer behavior
• Market Shares
– Modeling the dynamics of a 3-supplier market
– Customer loyalty
– Benefits of quality improvement
• Pricing Strategy
– American Airlines
Decision Models -- Prof. Juran
2
Marketing Example:
New Product Development Decision
Cavanaugh Pharmaceutical Company (CPC) has enjoyed a monopoly on
sales of its popular antibiotic product, Cyclinol, for several years.
Unfortunately, the patent on Cyclinol is due to expire. CPC is considering
whether to develop a new version of the product in anticipation that one of
CPC’s competitors will enter the market with their own offering.
The decision as to whether or not to develop the new antibiotic (tentatively
called Minothol) depends on several assumptions about the behavior of
customers and potential competitors. CPC would like to make the decision
that is expected to maximize its profits over a ten-year period, assuming a
15% cost of capital.
Decision Models -- Prof. Juran
3
Costs and Revenues
Cyclinol costs $1.00 per dose to manufacture, and sells for $7.50 per dose. The proposed
Minothol product would cost $0.90 per dose and sell for $6.00, allowing CPC to protect its
market share against lower-priced competition. This would, however, require a one-time
investment of $140 million.
Fixed Cost
Variable Cost
Selling Price
Cyclinol
None
$1.00
$7.50
Minothol
$140 million
$0.90
$6.00
Competition
There is really only one other company with the potential to enter the market, Ahrens
MethLabs, Inc. (AMI). Competitive analysis indicates that AMI is 20% likely to introduce a
competing product if CPC stays with the higher-priced Cyclinol product, but only 5%
likely to enter the market if CPC introduces Minothol.
Decision Models -- Prof. Juran
4
Customer Demand
Analysts estimate that the average annual demand over the next ten years
will be normally distributed with a mean of 40 million doses and a
standard deviation of 10 million doses, as shown below.
This demand is believed to be independent of whether CPC introduces
Minothol or whether Cyclinol/Minothol has a competitor.
Decision Models -- Prof. Juran
5
CPC’s market share is expected to be 100% of demand, as long as there is
no competition from AMI. In the event of competition, CPC will still enjoy
a dominant market position because of its superior brand recognition.
However, AMI is likely to price its product lower than CPC’s in an effort
to gain market share. CPC’s best analysis indicates that its share of total
sales, in the event of competition, will be a function of the price it chooses
to charge per dose, as shown below.
Market Share vs. Price
Market Share
100%
80%
60%
40%
20%
0%
$-
$2
$4
$6
$8
$10
$12
$14
Price
The Cyclinol product at $7.50 would only retain a 38.1% market share,
whereas the Minothol product at $6.00 would have a 55.0% market share.
Decision Models -- Prof. Juran
6
Questions
What is the best decision for CPC, in terms of maximizing the expected
value of its profits over then next ten years?
What is the least risky decision, using the standard deviation of the
ten-year profit as a measure of risk?
What is the probability that introducing Minothol will turn out to be
the best decision?
Decision Models -- Prof. Juran
7
U~(0, 1)
(whether or not AMI
enters market)
N~(40, 10)
(Total market
demand)
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
B
0.70928
Price
Total Demand
P(Competition)
Competition?
Market Share
Cyclinol Units Sold
Revenue
Fixed Cost
Variable Cost
Annual profit
Discount Rate
10-year PV
Results
Cyclinol
Minothol
Minothol Better?
Income statement-like calculations
for each of four scenarios
C
Cyclinol
No Competition
Competition
$
7.50 $
7.50
40.2
20%
No
100.0%
38.1%
40.2
15.3
$
301.28 $
114.86
$
$
$
1.00 $
1.00
$
261.11 $
99.55
15%
$1,310.45
$499.61
$
$
D
E
Minothol
No Competition
Competition
$
6.00 $
6.00
5%
No
$
$
$
$
100.0%
40.2
241.02
140.00
0.90
204.87
$888.20
$
$
$
$
55.0%
22.1
132.56
140.00
0.90
112.68
$425.51
1,310.45
888.20
0
3 Forecasts:
NPV in $millions for each decision
Yes/No New Product Better
Decision Models -- Prof. Juran
8
Decision Models -- Prof. Juran
9
Decision Models -- Prof. Juran
10
Example: Market Shares
Suppose that each week every family in the United
States buys a gallon of orange juice from company A, B,
or C.
Let PA denote the probability that a gallon produced by
company A is of unsatisfactory quality, and define PB
and PC similarly for companies B and C.
Decision Models -- Prof. Juran
11
If the last gallon of juice purchased by a family is
satisfactory, then the next week they will purchase a
gallon of juice from the same company.
If the last gallon of juice purchased by a family is not
satisfactory, then the family will purchase a gallon from
a competitor. Consider a week in which A families have
purchased juice A, B families have purchased juice B,
and C families have purchased juice C.
Decision Models -- Prof. Juran
12
Assume that families that switch brands during a
period are allocated to the remaining brands in a
manner that is proportional to the current market
shares of the other brands.
Thus, if a customer switches from brand A, there is
probability B/(B + C) that he will switch to brand B and
probability C/(B + C) that he will switch to brand C.
Suppose that 1,000,000 gallons of orange juice are
purchased each week.
After a year, what will the market share for each firm
be? Assume PA = 0.10, PB = 0.15, and Pc = 0.20.
Decision Models -- Prof. Juran
13
A
B
C
D
Inputs
1
2 Probabilities unsatisfactory
3
A
B
C
4
0.10
0.15
0.20
5
6
Week A buyers B buyers C buyers
7
1
200
200
200
8
2
224
194
182
=B7-H7+R7+U7
9
3
235 =C7-I7+V7+O7
193
172
10
4
237
191
172
=(SUM($B$7:$D$7))-(SUM(B8:C8))
11
5
246
192
162
12
6
257
195
148
13
7
279
172
149
14
8
281
181
138
15
9
282
191
127
16
10
290
199
111
E
F
G
H
I
Ending Market Shares
A
B
C
0.53
0.36
0.11
J
K
L
M
N
O
P
Q
R
S
T
U
V
=D58/SUM($B$58:$D$58)
# A bad # B bad # C bad
200 200 200
18
33
43
18 33
224 194 182
25
32
34
25 32
=MAX(D8,1)
235 193 172
26
24
22
26 24
237 191 =MAX(C8,1)
172
23
24 =MAX(H8,1)
32
23 24
246 192 162
27
27
37
27 27
257 195 148
21
41
26 =MAX(I8,1)
21 41
=SUM(B8:D8)-SUM(F8:G8)
279 172 149
29
18
31
29 18
281 181 138
29
19
29
29 19
282 191 127
27
24
28
27 24
290 199 111
28
30
19
28 30
B/Not A # A to B # A to C A/Not B # B to A # B to C A/Not C # C to A # C to B
43
0.50
9
9
0.50
17
16
0.50
25
18
34
0.52
14
11
0.55
19
13
0.54
17
17
22
0.53
12
14 =(H8-O8)
0.58
16
8
0.55
12
10
=M8-U8
32
0.53
14
9
0.58
11
13
0.55
21
11
37
0.54
16
11
0.60
15
0.56
23
14
=(L8-R8) 12
=MAX((F8/(F8+G8)),0.0000001)
26
0.57
8
13
0.63
27
14
0.57
16
10
31
0.54
15
14
0.65
12 =MAX((E8/(E8+F8)),0.0000001)
6
0.62
19
12
=MAX((E8/(E8+G8)),0.0000001)
=MAX(J8,1)
29
0.57
16
13
0.67
14
5
0.61
16
13
28
0.60
20
7
0.69
19
5
0.60
16
12
19
0.64
17
11
0.72
23
7
0.59
9
10
In order to keep the scale of the model within the capabilities of Crystal Ball, we
have reduced the number of users to 600.
Decision Models -- Prof. Juran
14
1
2
3
4
5
6
7
8
9
10
11
A
B
C
Inputs
Probabilities unsatisfactory
A
B
0.10
0.15
D
C
0.20
Week
A buyers B buyers C buyers
1
200
200
200
2
224
194
182
=B7-H7+R7+U7
=C7-I7+V7+O7
3
235
193
172
4
237
191
172
=(SUM($B$7:$D$7))-(SUM(B8:C8))
5
246
192
162
B4:D4 contain the probabilities that a given unit of the respective companies’
products will be unsatisfactory. These will be used with Crystal Ball to generate
“bad” orange juice using the binomial distribution.
Decision Models -- Prof. Juran
15
B7:D7 contain the initial distribution of customers across the three brands. Every
week, the number of customers for each brand is calculated using this formula:
number of customers from the previous week
- total customers lost
+ customers gained from one competitor
+ customers gained from the other competitor
= number of customers this week
Decision Models -- Prof. Juran
16
For brand A in week 2, this formula is calculated with:
B7
- H7
+ R7
+ U7
= B8
For brand B in week 2, this formula is calculated with:
C7
- I7
+ V7
+ O7
= C8
Brand C will have all of the total customers from the previous week minus the
Brands A and B current customers, so
=(SUM($B$7:$D$7))-(SUM(B8:C8))
Decision Models -- Prof. Juran
17
Now recall that a binomial random variable X is an integer
between 0 and n, viewed as the number of “successes” out
of n “trials”. The binomial distribution assumes that there is
a probability p of a success on any one trial, and that all
trials are independent of each other.
In this case, X is the number of gallons that are “bad”, n is
the total number of gallons purchased of a particular brand,
and p is the probability that any one gallon is “bad”.
Decision Models -- Prof. Juran
18
In the first week, each brand has 200 customers, so n will be 200 for all three
brands, in the second week, n is 224 for Brand A, 194 for Brand B, and 182 for
Brand C. (These will change during the course of the simulation.)
6
7
8
B
A buyers
200
224
C
B buyers
200
194
D
C buyers
200
182
Decision Models -- Prof. Juran
E
F
G
200
224
200
194
200
182
H
I
J
K L M
# A bad # B bad # C bad
18
33
43
18 33 43
25
32
34
25 32 34
19
6
7
8
B
A buyers
200
224
C
B buyers
200
194
D
C buyers
200
182
E
F
G
200
224
200
194
200
182
H
I
J
K L M
# A bad # B bad # C bad
18
33
43
18 33 43
25
32
34
25 32 34
We have used columns E, F, and G to truncate these random n values so that
they are always at least 1. This will not make any difference most of the time, but
occasionally one brand’s customer base will go to zero in a long simulation run,
causing an error with Crystal Ball. (Crystal Ball doesn’t know how to generate a
binomial variable when n is zero.)
So we need to set up binomial random variables, where the n for each brand is
given in column E, F, or G and the p is given in B4, C4, or D4, depending on
which brand.
Decision Models -- Prof. Juran
20
Note that we have used dollar signs in the cell references, so this can be copied
down to the rest of the assumption cells in column H.
Decision Models -- Prof. Juran
21
6
7
8
E
F
G
200
224
200
194
200
182
H
I
J
K L M
# A bad # B bad # C bad
18
33
43
18 33 43
25
32
34
25 32 34
Our model now includes random numbers of “bad” gallons of orange juice for
each brand every week. Using these numbers of bad gallons, our model imitates
the numbers of customers who abandon each brand each week. Now we need to
model the switching behavior of those customers. For example, we have
modeled the departure of 18 customers from Brand A in the first week, and
another 25 from Brand A in the second week, but we haven’t yet modeled where
those customers will go (Brand B or Brand C) to get their orange juice in the
following week.
Decision Models -- Prof. Juran
22
We can use the binomial distribution again here. The number
of people who switch from Brand A to Brand B in any given
week will be a binomial random variable, with n equal to the
total number of people who abandon Brand A in that week,
and p equal to the proportion of the non-Brand A market held
by Brand B in that week, or B/(B + C).
(Recall that the problem asks us to “Assume that families that
switch brands during a period are allocated to the remaining
brands in a manner that is proportional to the current market
shares of the other brands.”)
Decision Models -- Prof. Juran
23
We’ll set up the ns for these binomial random
variables in columns K, L, and M, using MAX
functions as before to make sure that they never go
below 1.
In column N we calculate the proportion of non-A
customers who buy B in the current week, once again
using a MAX function to make sure this is never zero.
Decision Models -- Prof. Juran
24
Decision Models -- Prof. Juran
25
The number of A customers who switch from A to Brand C is
calculated in column P; it is simply the difference between K7 and O7.
We model the switching behavior of former B customers in columns
Q, R, and S, and former C customers in columns T, U, and V.
Finally, the various numbers of switchers are taken into account for
the start of the next week in columns B, C, and D.
All of the binomial assumptions (columns H, I, J, O, R, and U) get
copied down through row 58, so we can model a 52-week year.
Decision Models -- Prof. Juran
26
Decision Models -- Prof. Juran
27
Suppose a 1% increase in market share is worth $10,000
per week to company A.
Company A believes that for a cost of $1 million per
year it can cut the percentage of unsatisfactory juice
cartons in half.
Is this worthwhile? (Use the same values of PA, PB, and
PC as in part a.)
Decision Models -- Prof. Juran
28
There are a number of ways to approach this kind of
issue. One elegant way is to run two simulations
simultaneously, in which the only difference is (in this
case) the different value for PA.
We’ll run the same model as before, but add to it, in
parallel, a second model in which PA = 0.05 instead of
0.10.
The old model is in a worksheet called Part (a) and the
new model is in a worksheet called Part (b).
Decision Models -- Prof. Juran
29
We’ll add a new forecast cell in the new model (cell K4), which will be the
difference between the two ending markets shares for Brand A:
1
2
3
4
5
A
B
C
Inputs
Probabilities unsatisfactory
A
B
0.05
0.15
D
C
0.20
E
F
G
H
Ending Market Shares
A
B
0.79
0.18
I
J
=G4-'Part (a)'!G4
C
0.04
K
A Gain
0.25
The summary statistics for this new forecast cell ought to give us a good idea as
to how Brand A’s share would change if they could reduce their probability of a
bad gallon.
Decision Models -- Prof. Juran
30
Decision Models -- Prof. Juran
31
A 95% confidence interval for the increase in market share is given by:
X
 1.96
s
n
0.1888
 1.96
0.0301
100,000
 0.000186
Or (0.1886, 0.1890)
Let’s take the most pessimistic end of this confidence interval and assume that
Brand A will get an 18.56% increase in market share for its $1 million investment.
This translates to $188,600 per week, or about $9.8 million per year.
Decision Models -- Prof. Juran
32
Summary
• Marketing Simulation Models
• New Product Development Decision
– Uncertainty about competitor behavior
– Uncertainty about customer behavior
• Market Shares
– Modeling the dynamics of a 3-supplier market
– Customer loyalty
– Benefits of quality improvement
• Pricing Strategy
– American Airlines
Decision Models -- Prof. Juran
33