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DECISION SUPPORT SYSTEM ARCHITECTURE: THE MODEL COMPONENT

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What are Models? A model: “A simple representation or abstraction of a real situation or problem” Models may be: Iconic: physical representation Analog: behavioural representation Mathematical: numerical/ quantitative representation DSS models are mathematical…usually…

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Consider a simple maths model… X= y+ 10% Where x= sales current year and y= sales last year Represents the situation that sales increase by 10% each year…

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Maths Models Purpose Optimisation- e.g. linear programming Description– e.g simulation models, forecasting. More typical for DSS Generality Custom built- more usual for DSS Off the shelf Randomness- certainty of outcomes Probablistic – more realistic Deterministic- many models treat data as more certain. Typical for DSS N.B. ALL MODELS INVOLVE MAKING ASSUMPTIONS

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Relevant models for levels deterministic descriptive External focus custom built Strategy long term deterministic optimisation ready made Tactical deterministic Operational Internal focus optimisation short term ready made

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Why use Models? Decrease time spent in solving the actual problem Decrease cost Easier to attempt solutions Fewer options to deal with (less complex) Less chance of mistakes Less risk Can improve education about the real problem or situation

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Problems with Models (in management science) Input data for models is hard to obtain and input Interpretation of the models’ output Inability of users to develop own models Integration of different models to deal with a variety of problem types Lack of confidence (or too much) in model results due to lack of understanding Poor user/ model interaction

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Model Base Management System Emphasis is on integration of models with whole system (interface and data sources) Ease of use of models Flexibility to build models appropriate to the problem situation Procedures to update models Procedures for the output of one model to feed into another BUT…No comprehensive MBMS available

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Linear Regression Forecasting- predicting the future based on understanding current patterns, trends. Linear Regression is the forecasting technique where a straight line is drawn through the data points as plotted on a scatter diagram This line is called the Line of Best Fit

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Your case study Historical Population growth (Line Fit Plot) 0 100 200 300 400 500 600 700 800 900 1000 02468 Years Population (000's) Y Predicted Y

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Line of best fit The way to describe a straight line on a graph in numerical terms is to know something about where it starts off on the axis AND how steep it is. These 2 numbers (intercept and gradient) are the output of the linear regression functions in Excel…

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Linear regression assumptions A straight line fit is the best way to describe the data… The deviations from the line are random, without pattern and average out to be zero over all. some are positive- above the line, some negative below the line… There is enough data to see obvious trends about 20 or so… Predictions should be made within a limited range There is a causal or dependent relationship. e.g increasing advertising causes more sales to be generated.

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Causal relationships ?

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