Strategic Decision Making:
A Systems Dynamic Model of a Bulgarian Firm
David L. Olson, University of Nebraska
Madeline Johnson, Univ. of Houston-Downtown
Margaret F. Shipley, Univ. of Houston-Downtown
Nikola Yankov, Tsenov Academy of Economics
Transition Economies
• Transition from
centrally-planned to
market economies
• Face ambiguous
information and cues
– Challenge existing
ownership & operating
principles
– Firms responsible for
strategic decisions
Joint Effort
• University of HoustonDowntown
– NSF Grant – Joint
International Workshop on
the Use of Information
Technologies in Modeling
the Bulgarian Firm in
Transition from a Planned
to a Free Market Economy
• Tsenov Academy of
Economics
– Svishtov, Bulgaria
Subjective System Dynamics Model
•
•
Winery
Tool to simulate impact of key strategic
decisions:
1.
2.
3.
4.
5.
Market selection (local, national, international)
Promotion & pricing
Product quality decisions
Capacity (vineyards and bottling)
Distribution
Open Systems Theory
• Ludwig von Bertalanffy
– An organization exists in relation to its
environment
– There is a continuous flow of energy &
information
– System features:
• Self-organization - progressive differentiation
• Equifinality – initial condition doesn’t matter
• Teleology – systems are purpose-driven
Cybernetics
• Stafford Beer
– Cybernetic systems are complex, probabilistic,
self-regulatory, purposive, have feedback
and control
– Operations research only works when you
consider the whole
– Viable System Model – organization regulated,
learns, adapts, evolves, or doesn’t survive
Mental Models
• Systems consist of interacting parts working
toward some end, feedback control
–
–
–
–
Purposive
Synergistic
Complex
Feedback
System Dynamics
• Jay Forrester
– Developed technique for deterministic
simulation of systems
• Identify influences
• Estimate effects
• Develop feedback model
Forrester’s World Dynamics
Model
• Sectors
–
–
–
–
Population
Natural Resources
Capital Investment
Pollution
• Metrics
– Quality of life
– Material standard of living
– Ratios for FOOD, CROWDING, POLLUTION
Soft Systems Theory
Peter Checkland
•
•
Interpretive action research
Model interacting system
1.
2.
3.
4.
5.
6.
7.
Define problem
done
Express situation
done
Root definition
Conceptual model done – simulation model
Compare model/real world
Use model to determine improved methods
Action
Simulation Approaches
• DYNAMO/Ithink/Stella/PowerSim
• VENSIM
– Commercial implementation of system dynamics
– Support conceptual modeling
• EXCEL
– Probabilistic simulation over time
• CRYSTAL BALL
– Probabilistic simulation output
Development of Model
• Symposium in Svishtov, Bulgaria
– May 2002
– About 20 from U.S., 20 from Svishtov
• Selected winery because of knowledge of
Tsenov Academy faculty
• Selected system dynamics because:
– Problem involved subjective data
– Complex interactions among decisions, time
Winery Model
• Time frame: 6 years
– Show impact of strategic
decisions
• Inputs:
– Promotion
– Pricing
– Quality (grow or purchase
grapes)
– Market selection (local,
national, international)
• Outputs
– Profit
– Cash flow
– Market share by product (3
levels of quality)
Promotion
• Lagged over three month
• Impact differentials
– 0.5 prior month
– 0.35 two months prior
– 0.15 three months prior
• Media: firm representatives interacting with
distributors
• Management could constrain local, national, or
export markets to emphasize others
– Demands in each market probabilistic
Quality
• If winery controls vineyard, quality higher
• Constrained by amount of hectares in vines
– Three years between planting, use
– Use own grapes as much as possible
• Any extra production capacity used for purchased
grapes (lower quality bottles)
System Variables
• Exogenous:
• System Variables:
• Control Inputs:
Exogenous Variables
• Demand (normally distributed, change per month)
– By market (local, national, export)
– By product (correlated)
– Seasonal
• Market Price (normally distributed, change per month)
– Independent of firm decisions
• Competitor promotion (normally distributed by market)
• Market share possibilities
– Prior market share multiplied by ratio of prior promotion to base
promotion, divided by that of competitors
• Crop yield
Control Inputs
• Price
– By product by month
• Promotion
– By product by month
• Plant Capacity
– Depreciation, plus construction
• Labor
– Permanent (higher quality) vs. temporary
System Variables
• Sales
– By market, by product
• Inventory
– High, low quality
• Bank Balance
– 5% gain on positive balance, 15% cost on
negative
Results
• Varied prices, promotion levels
– Price: base, cut 10%, increase 20%
– Promotion: base, emphasize local, emphasize export
• Measured
–
–
–
–
bank balance after 6 years
Probability of losing initial capital (going broke)
Probability of breaking even
Market share (low, high quality)
Base Run
Wine M ode l
1
0.8
0.6
DemN
0.4
DemEx
MktShNat
MktShEx
0.2
-0.2
Key param eters
70
67
64
61
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
0
1
index
Balance
Base Model
•
•
•
•
•
1000 replications
Crystal Ball software
Cyclical demand for high quality
Base case has National focus
Without pricing & promotion, loss
End Bank Balance
Forecast: endyr6 bank balance
100 Trials
Frequency Chart
100 Displayed
.070
7
.053
5.25
.035
3.5
.018
1.75
.000
0
-3 5 ,0 0 0 . 0 0
-2 6 ,2 5 0 . 0 0
-1 7 ,5 0 0 . 0 0
le v
-8 ,7 5 0 . 0 0
0.00
Bank Balance
•
•
•
•
•
Mean 117,458 Lev
Probability of losing bankroll: 0.0
Probability of losing money: 0.0
Most optimistic:
Worst: loss:
Market Share - National
Forecast: Market Share - National
100 Trials
Frequency Chart
100 Displayed
.060
6
.045
4.5
.030
3
.015
1.5
.000
0
0.00
0.10
0.20
p ro p o rti o n
0.30
0.40
Mixed Price, Promotion
Forecast: endyr6 bank balance
1,000 Trials
Frequency Chart
1,000 Displayed
.027
27
.020
20.25
.014
13.5
.007
6.75
.000
0
-10,000.00
-1,250.00
7,500.00
lev
16,250.00
25,000.00
National Market Share
– Mixed policies
Forecast: Market Share National - end year 6
1,000 Trials
Frequency Chart
1,000 Displayed
.023
23
.017
17.25
.012
11.5
.006
5.75
.000
0
0.30
0.36
0.43
proportion
0.49
0.55
Model Validation
• Initial visit May 2002
– 3 day workshop to build model
• Built model summer 2002
• Followup visit October 2003
– Went over model in detail
– Refined model structure
– Identified detailed data needs
Conclusions
• System dynamics useful to model subjective
input, complex interactions in temporal
environment
• Need for validation