Strategic Decision Making: A Systems Dynamic Model of a Bulgarian Firm

David L. Olson, University of NebraskaMadeline Johnson, Univ. of Houston-DowntownMargaret F. Shipley, Univ. of Houston-DowntownNikola 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 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

• Winery• Tool to simulate impact of key strategic

decisions:1. Market selection (local, national, international)

2. Promotion & pricing

3. Product quality decisions

4. Capacity (vineyards and bottling)

5. 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 TheoryPeter Checkland

• Interpretive action research• Model interacting system

1. Define problem done

2. Express situation done

3. Root definition

4. Conceptual model done – simulation model

5. Compare model/real world

6. Use model to determine improved methods

7. 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 Model

-0.2

0

0.2

0.4

0.6

0.8

1

1 4 7 10

13

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

67

70

Key parameters

ind

ex

Balance

DemN

DemEx

MktShNat

MktShEx

Base Model

• 1000 replications

• Crystal Ball software

• Cyclical demand for high quality

• Base case has National focus

• Without pricing & promotion, loss

End Bank Balance

Frequency Chart

lev

.000

.018

.035

.053

.070

0

1.75

3.5

5.25

7

-35,000.00 -26,250.00 -17,500.00 -8,750.00 0.00

100 Trials 100 Displayed

Forecast: endyr6 bank balance

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

Frequency Chart

proportion

.000

.015

.030

.045

.060

0

1.5

3

4.5

6

0.00 0.10 0.20 0.30 0.40

100 Trials 100 Displayed

Forecast: Market Share - National

Mixed Price, Promotion

Frequency Chart

lev

.000

.007

.014

.020

.027

0

6.75

13.5

20.25

27

-10,000.00 -1,250.00 7,500.00 16,250.00 25,000.00

1,000 Trials 1,000 Displayed

Forecast: endyr6 bank balance

National Market Share – Mixed policies

Frequency Chart

proportion

.000

.006

.012

.017

.023

0

5.75

11.5

17.25

23

0.30 0.36 0.43 0.49 0.55

1,000 Trials 1,000 Displayed

Forecast: Market Share National - end year 6

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