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DECISION SUPPORT SYSTEM ARCHITECTURE: THE MODEL COMPONENT.

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Presentation on theme: "DECISION SUPPORT SYSTEM ARCHITECTURE: THE MODEL COMPONENT."— Presentation transcript:

1 DECISION SUPPORT SYSTEM ARCHITECTURE: THE MODEL COMPONENT

2 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…

3 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…

4 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

5 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

6 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

7 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

8 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

9 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

10 Your case study Historical Population growth (Line Fit Plot) Years Population (000's) Y Predicted Y

11 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…

12 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.

13 Causal relationships ?


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