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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.

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strategic decision making a systems dynamic model of a bulgarian firm

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 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
Joint Effort
  • University of Houston-Downtown
    • 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
Subjective System Dynamics Model
  • Winery
  • Tool to simulate impact of key strategic decisions:
    • Market selection (local, national, international)
    • Promotion & pricing
    • Product quality decisions
    • Capacity (vineyards and bottling)
    • Distribution
open systems theory
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
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
Mental Models
  • Systems consist of interacting parts working toward some end, feedback control
    • Purposive
    • Synergistic
    • Complex
    • Feedback
system dynamics
System Dynamics
  • Jay Forrester
    • Developed technique for deterministic simulation of systems
      • Identify influences
      • Estimate effects
      • Develop feedback model
forrester s world dynamics 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
Soft Systems TheoryPeter Checkland
  • Interpretive action research
  • Model interacting system
    • 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
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
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
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
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
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
System Variables
  • Exogenous:
  • System Variables:
  • Control Inputs:
exogenous variables
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
Control Inputs
  • Price
    • By product by month
  • Promotion
    • By product by month
  • Plant Capacity
    • Depreciation, plus construction
  • Labor
    • Permanent (higher quality) vs. temporary
system variables1
System Variables
  • Sales
    • By market, by product
  • Inventory
    • High, low quality
  • Bank Balance
    • 5% gain on positive balance, 15% cost on negative
results
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 model
Base Model
  • 1000 replications
  • Crystal Ball software
  • Cyclical demand for high quality
  • Base case has National focus
  • Without pricing & promotion, loss
bank balance
Bank Balance
  • Mean 117,458 Lev
  • Probability of losing bankroll: 0.0
  • Probability of losing money: 0.0
  • Most optimistic:
  • Worst: loss:
model validation
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
Conclusions
  • System dynamics useful to model subjective input, complex interactions in temporal environment
  • Need for validation
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