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 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 Theory Peter Checkland Interpretive action research Model interacting system 1.Define problemdone 2.Express situationdone 3.Root definition 4.Conceptual modeldone – 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

Base Model 1000 replications Crystal Ball software Cyclical demand for high quality Base case has National focus Without pricing & promotion, loss

End 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

Mixed Price, Promotion

National Market Share – Mixed policies

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