ashley heller craig whitnack - university of oklahoma wi… · ashley heller craig whitnack che...
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Ashley HellerAshley HellerAshley HellerAshley HellerAshley HellerAshley HellerAshley HellerAshley Heller
Craig WhitnackCraig WhitnackCraig WhitnackCraig WhitnackCraig WhitnackCraig WhitnackCraig WhitnackCraig Whitnack
ChEChEChEChEChEChEChEChE 42734273427342734273427342734273Dr. Miguel Dr. Miguel Dr. Miguel Dr. Miguel Dr. Miguel Dr. Miguel Dr. Miguel Dr. Miguel BagajewiczBagajewiczBagajewiczBagajewiczBagajewiczBagajewiczBagajewiczBagajewicz
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OverviewOverview� Background� Process Overview� Goals and Modeling Requirements� Consumer Preference Model� Financial Analysis� Analysis Under Uncertainty� Conclusions
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BackgroundBackground
History of Wine� Enjoyed by many civilizations� Celebratory and ceremonial uses� “Nectar of the Gods”� Increasing desire of consumer
satisfaction
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Wine TodayWine Today� BEFORE: Wine quality/characteristics
influence by producer� NOW: Consumers control wine quality� New tasks for producer
� Identify consumer wants� Adjust manufacturing� Adjust price
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SolutionSolution� Identify consumer utility� Manipulate process to meet desired
quality
� Determine wine characteristics before bottling
� Meet profit aspiration
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Process OverviewProcess Overview
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Process OverviewProcess OverviewBasic Process: 4 main steps
� Harvest and Crushing
� Fermentation• Reduction of sugar, increase in –OH• Malolactic Fermentation, decrease acidity
� Clarification/Hot and Cold Stabilization
� Oak Aging
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Harvest and CrushingHarvest and Crushing� Cold soaking is
where hue and brightness can be altered (color)
� Tannins extracted from seeds and grapes (bitterness)
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Fermentation and ClarificationFermentation and Clarification� Fermentation
� Increase in percent alcohol (body/texture)� Reduction in residual sugar (sweetness)� Increase fermentation time decreased acidity
� Clarification/Hot and cold stabilization� Clarity� Filtering
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Aging: Natural Grape AromaAging: Natural Grape Aroma• Flowery aroma
comes from β-damascenone
• Berry aroma associated with β-ionone
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Toasting EffectsToasting Effects
• Barrel Heating� Open Flame� Time vs. Temperature
Profiling� Toast Levels
• Light• Medium • Medium Plus• Heavy
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TimeTime--Temperature ProfileTemperature Profile
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TimeTime--Temperature ProfileTemperature Profile
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CompoundsCompounds• Cellulose and amino
acids undergo Maillard reaction� Responsible for caramel,
butterscotch flavor
• Lignin →Pyrolysis� Responsible for clove,
vanilla flavor
• Oak and clove flavor from non-caramelized wood
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Modeling RequirementsModeling Requirements
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GoalsGoalsDevelop model for calculation of NPW with inputs:� Physical properties of the wine
� Selling price
� Competitor selling price
� Advertising level
� Consumer budget
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Demand ModelDemand Model
� Satisfaction
� Maximize Satisfaction
� Consumer Budget
( ) ρρρ /1
21 ddS +=
ρρ
βα −− = 1
221
11 dpdp
2211 dpdpY +≥
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Happiness FunctionHappiness Function
1. Happiness Function:
2. Superiority Function:
3. Demand 1: ρρρ
βα
1
1
2
11
1
21 d
p
dpY
p
pd
−
−
=
2 1/H Hβ =
H 1 = ∑ wi yi
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Advertising LevelsAdvertising Levels
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Consumer Preference ModelConsumer Preference Model
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Wine CharacteristicsWine Characteristics� Clarity
� Body/Texture
� Bouquet� Flowery� Berry� Vanilla� Butterscotch� Clove� Coconut/Oak
� Acidity
� Sweetness
� Bitterness
� Color� Hue� Brightness
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Consumer PreferenceConsumer Preference� Survey over
sample population� Determined
relative importance of characteristics
� Resulted in values of wi
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Happiness Curve: BouquetHappiness Curve: Bouquet--FloweryFlowery
• Happiness related to consumer descriptions
• Descriptions related to amount of component
• Happiness description of Flowery components
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Example Flowery Preference CurveExample Flowery Preference Curve
Happiness vs Flowery
0
0.2
0.4
0.6
0.8
1
Flowery
Hap
pin
ess
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Flowery Flowery vsvs ββ--damascenonedamascenone
0 2 4 6 8 10 12 14b-damascenone (mg/L)
Flo
wer
y
Elusive
D elicat e,C lean
C o mp lex,F lo wery
Developed, Pronounced
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Preference Preference vsvs bb--damascenonedamascenone
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14
b-damascenone
Hap
pin
ess
y=1-e-5x
(mg/L)
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HappinessHappiness--Perfect BottlePerfect Bottle
2 1/H Hβ =
yi wi
1 Weights2
0.78 0.1538460.64 0.0769230.70 0.076923
Flowery (b-damascenone) 1.00Berry (b-ionone) 1Vanilla (Vanillin) 0Clove (Eugenol) 0.19Butterscotch (Furfural) 0.1198Oak/Coconut (Lactones) 0.2153Combined Score of 6 0.42085 0.3076923
1.00Sweetness 1.00Bitterness 0.67
Combined Score of 3 0.89 0.230769Body 0.44 0.153846
Total Happiness (H1) 0.62452 1.00
Acidity
Color (Brightness)Color (Hue)
Production YearAging Year
Happiness
Clarity
H 1 = ∑ wi yi
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Financial AnalysisFinancial Analysis
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Final Product DesiredFinal Product Desired• For particular α level and β,
find:� Optimum selling price p1 at each
production rate K, by:
� Maximizing NPW, such that:
0)( 1
1
2
112111 =
−
−=Φ−
ρρρ
βα
dp
dpYpdpd
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Financial Analysis: Financial Analysis: ““ Perfect WinePerfect Wine””High α
0
20
40
60
80
100
120
140
160
180
200
20 30 40 50 60 70 80 90 100 110 120 130Bottle Price ($)
NP
W (
Mill
ion
s$)
2M100K
250K500K
750K1M
1.5M2.5M3M
3.5M4M
4.5M5M
Optimum
Production Rate
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$/Bottle vs. NPW
$0
$25
$50
$75
$100
$125
$150
$175
$200
$20 $30 $40 $50 $60 $70 $80 $90 $100
$/bottle
NP
W (M
$)
HIGH α
MEDIUM α
LOW α
Increased Advertising
NPW vs $/bottle
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Analysis Under UncertaintyAnalysis Under Uncertainty
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UncertaintyUncertainty� Driving factor for quantifying risk� Sensitivity analysis
� Consumer happiness, H2� Competitor price, P2
� Consumer budget, Y
� Consumer happiness, H1� Interest rate
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Risk Curve For Net Present Worth
0
0.2
0.4
0.6
0.8
1
-300 -250 -200 -150 -100 -50 0 50 100 150 200 250
Net Present Worth (MM$)
Pro
bab
ilit
y
High α Low α Medium α
Initial Risk AnalysisInitial Risk Analysis
K=2.5M/yr
p1=$36
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Initial Risk AnalysisInitial Risk AnalysisRisk Curve For Net Present Worth
0
0.2
0.4
0.6
0.8
1
-300 -250 -200 -150 -100 -50 0 50 100 150 200
Net Present Worth (MM$)
Pro
bab
ilit
y
High α, 5M High α, 250K High α, 1M
K=5M/yr
p1=$25
K=250k
p1=$78
Random point off curve
K=1M, p1=$30
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Complete Risk Analysis: Complete Risk Analysis: ““ Perfect Perfect WineWine””
High α
0
20
40
60
80
100
120
140
160
180
200
20 30 40 50 60 70 80 90 100 110 120 130Bottle Price ($)
NP
W (
Mill
ion
s$)
2M100K
250K500K
750K1M
1.5M2.5M3M
3.5M4M
4.5M5M
Optimum
Production Rate
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Risk Curves: Risk Curves: ““ Perfect WinePerfect Wine””
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-650 -600 -550 -500 -450 -400 -350 -300 -250 -200 -150 -100 -50 0 50 100 150 200 250 300 350 400 450
ENPW ($millions)
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““ Perfect WinePerfect Wine””
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-400 -350 -300 -250 -200 -150 -100 -50 0 50 100 150 200 250 300
ENPW ($M)
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Example ENPWExample ENPWBEST ENPW EXAMPLE
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-200 -150 -100 -50 0 50 100 150 200NPW ($M)
ENPW$111M
$-68M $190M
VAR
OV
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Summary of Risk: Decreasing Summary of Risk: Decreasing ENPWENPW
SORTED BY DECREASING ENPWK (mil) p1 NPW ($M) ROI ENPW ($M) ? = 0 VAR ($M) OV ($M) Decreasing ENPW Continued
2.0 $40 $164 174% $111 13% $200 $68 K (mil) p1 NPW ($M) ROI ENPW ($M) ? = 0 VAR ($M) OV ($M)2.0 $38 $145 154% $106 11% $182 $52 4.0 $30 $135 73% $51 26% $256 $1142.5 $38 $180 153% $104 19% $239 $95 4.0 $32 $154 82% $50 31% $297 $1562.5 $36 $158 134% $102 16% $199 $75 2.5 $28 $56 48% $47 5% $54 $282.0 $36 $125 133% $100 8% $137 $38 3.5 $38 $127 78% $46 41% $293 $2272.5 $34 $134 114% $99 10% $171 $50 4.0 $28 $99 53% $44 23% $199 $792.5 $40 $197 167% $98 23% $231 $124 1.0 $34 $44 92% $43 1% $9 $91.5 $40 $121 170% $98 8% $130 $33 1.5 $30 $43 61% $41 1% $13 $143.0 $34 $161 114% $92 19% $208 $91 2.0 $28 $42 45% $39 3% $18 $191.5 $38 $106 149% $90 6% $96 $26 4.0 $34 $126 67% $38 41% $282 $1993.0 $36 $184 131% $90 24% $245 $119 3.5 $26 $50 30% $33 10% $102 $422.0 $34 $105 111% $88 6% $99 $31 1.0 $32 $34 70% $33 1% $8 $83.0 $38 $185 132% $87 26% $254 $152 3.0 $26 $40 28% $31 8% $49 $313.0 $32 $133 94% $86 15% $188 $67 3.5 $40 $99 60% $30 44% $287 $2692.5 $32 $108 92% $85 8% $123 $40 4.0 $26 $59 32% $29 18% $136 $551.5 $36 $90 127% $80 3% $65 $21 2.5 $26 $30 26% $26 4% $22 $233.5 $32 $153 93% $75 23% $228 $101 1.5 $28 $27 39% $26 1% $14 $133.0 $40 $157 112% $74 32% $282 $192 4.5 $28 $108 52% $25 33% $231 $1172.0 $32 $84 89% $74 5% $67 $25 4.0 $36 $97 52% $23 45% $305 $2463.5 $34 $176 108% $72 29% $238 $137 1.0 $30 $23 48% $23 0% $9 $83.0 $30 $102 73% $72 11% $156 $53 4.5 $30 $122 58% $21 39% $280 $1601.0 $40 $76 157% $70 2% $21 $13 2.0 $26 $21 22% $20 4% $17 $172.5 $30 $82 70% $68 7% $86 $31 4.5 $26 $67 32% $16 25% $209 $771.5 $34 $75 105% $68 3% $25 $17 1.0 $28 $13 26% $12 2% $9 $93.5 $30 $121 74% $66 20% $206 $76 1.5 $26 $12 16% $11 7% $13 $131.0 $38 $65 135% $62 1% $11 $11 4.5 $32 $95 45% $11 45% $311 $2083.5 $36 $155 95% $61 34% $277 $183 3.5 $24 $13 8% $5 29% $43 $332.0 $30 $63 67% $58 3% $24 $20 3.0 $24 $9 6% $3 33% $29 $281.5 $32 $59 83% $56 2% $13 $13 2.5 $24 $4 3% $2 41% $20 $193.5 $28 $86 52% $56 14% $153 $55 1.0 $26 $2 5% $2 33% $8 $83.0 $28 $71 51% $54 9% $122 $40 4.0 $38 $69 37% $2 51% $292 $2911.0 $36 $55 113% $52 1% $10 $11 4.0 $24 $18 9% $1 35% $99 $45
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Effect of UncertaintyEffect of Uncertainty
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
$24 $25 $26 $27 $28 $29 $30 $31 $32 $33 $34 $35 $36 $37 $38 $39 $40p1
NP
W/E
NP
W (
$mill
ion
)
3.5
3.5 Risk
K (millions of bottles)
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Wine ManipulationWine Manipulation
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Wine ManipulationWine ManipulationNPW vs. Price p2
-200000000
-100000000
0
100000000
200000000
300000000
400000000
500000000
10 30 50 70 90 110 130
Price p2
NP
W (
$M)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.8
0.9
1
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Wine ManipulationWine ManipulationNPW vs. Price p2
-2.E+08
-1.E+08
0.E+00
1.E+08
2.E+08
3.E+08
4.E+08
5.E+08
6.E+08
7.E+08
8.E+08
$5 $10 $15 $20 $25 $30 $35 $40 $45 $50
Price p2
NP
W (
$M)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.8
0.9
1
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Complete Risk Analysis: Complete Risk Analysis: ββ = 0.8= 0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-600 -400 -200 0 200 400 600
ENPW ($millions)
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"Perfect Wine" β = 0.8K (mil) p1 NPW ($M) ROI ENPW ? = 0 VAR ($M) OV ($M) K (mil) p1 NPW ($M) ROI ENPW ? = 0 VAR ($M) OV ($M)
1.0 $34 $44 92% $43 1% $9 $9 1.5 $38 $100 140% $42 28% $174 $721.5 $30 $43 61% $41 1% $13 $14 1.0 $40 $74 153% $41 20% $118 $422.0 $28 $42 45% $39 3% $18 $19 1.5 $34 $73 103% $39 20% $142 $454.0 $34 $126 67% $38 41% $282 $199 1.5 $40 $109 153% $38 33% $181 $913.5 $26 $50 30% $33 10% $102 $42 1.0 $36 $54 112% $36 14% $95 $251.0 $32 $34 70% $33 1% $8 $8 2.0 $34 $99 105% $32 31% $195 $823.0 $26 $40 28% $31 8% $49 $31 1.5 $32 $58 82% $31 17% $138 $383.5 $40 $99 60% $30 44% $287 $269 2.0 $32 $81 86% $30 27% $186 $674.0 $26 $59 32% $29 18% $136 $55 1.0 $34 $44 92% $28 14% $100 $242.5 $26 $30 26% $26 4% $22 $231.5 $28 $27 39% $26 1% $14 $13 1.5 $30 $43 61% $25 13% $127 $334.5 $28 $108 52% $25 33% $231 $117 2.0 $30 $62 66% $24 23% $153 $514.0 $36 $97 52% $23 45% $305 $2461.0 $30 $23 48% $23 0% $9 $84.5 $30 $122 58% $21 39% $280 $1602.0 $26 $21 22% $20 4% $17 $17 2.0 $38 $96 102% $19 43% $207 $1364.5 $26 $67 32% $16 25% $209 $77 1.0 $30 $23 48% $18 8% $38 $131.0 $28 $13 26% $12 2% $9 $9 2.5 $28 $55 47% $12 25% $189 $621.5 $26 $12 16% $11 7% $13 $134.5 $32 $95 45% $11 45% $311 $208 2.5 $32 $99 84% $10 38% $230 $1113.5 $24 $13 8% $5 29% $43 $33 2.0 $26 $21 22% $7 17% $83 $283.0 $24 $9 6% $3 33% $29 $28 1.5 $26 $12 16% $4 17% $50 $192.5 $24 $4 3% $2 41% $20 $19 2.5 $26 $30 25% $2 21% $168 $44
Comparison: Perfect to Comparison: Perfect to ββ = 0.8= 0.8
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ConclusionsConclusions� Quality of the wine can be
manipulated at negligible costs� Uncertainty needs to be incorporated
in order to make accurate decisions based on level of risk
� Risk can be adjusted by the adjustment of wine quality
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AcknowledgementsAcknowledgements� Dr. Miguel Bagajewicz, University of
Oklahoma
� Phillip Coghill, TA
� Susan Kerr
� Michael Frow
� Curtis Baade
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Questions?