Simulation page 165

Simulation is the appearance of reality

A technique for conducting experiments (eg. what-if analysis) with a computer on a model of a management system.

One of the most commonly used methods DSS methods

What-if analysis page 160

Sensitivity, what-if, and goal seeking analyses page 159

Sensitivity analysis

• Assesses the impact of change in inputs or parameters on solutions
• Allows for adaptability and flexibility
• Eliminates or reduces variables
• Can be automatic or trial and error

What-if analysis

• Assesses solutions based on changes in variables or assumptions

Goal seeking analysis

• Backwards approach, starts with goal
• Determines values of inputs needed to achieve goal
• E.g. break-even point determination

Modeling with spreadsheets page 145

see Modelling and Analysis.ppt

Spreadsheets are a computerised mathematical and data management tool.

• An end-user modeling tool.
• Flexible and ‘easy’ to use.
• Supports complex mathematical methods.
• Can be used to implement linear programming techniques.
• Supports complex statistical and regression analysis methods.
• Provides services like what-if analysis, data-base management, automation macros.
• Can be used for static and dynamic modeling.
• risk analysis can be incorporated
  

Models can be developed and implemented in a variey of programming languages and systems.

Spreadsheets include extensive forecatsing, statistical and other modeling and databse management capabilities, functions and routines. As spreadsheet packages eveolved, add-ins were develpoed for structuring and solving specific model classes.

DSS related add-ins

Most popular end user modeling tool, incorporates many power financial statistical, mathematical and other functions.

Spreadsheets can perform model solution tasks such as:

1. Linear programming
Informally, linear programming determines the way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model and given some list of requirements represented as linear equations.
from wiki: http://en.wikipedia.org/wiki/Linear_programming

2. Regression analysis
regression analysis refers to techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
from wiki: http://en.wikipedia.org/wiki/Regression_analysis

Other features:

• what-if analysis

http://office.microsoft.com/en-au/excel/HA102431641033.aspx
http://office.microsoft.com/en-us/excel/CH010004551033.aspx
http://www.informit.com/podcasts/episode.aspx?e=B4C8DE7D-3AC1-45CD-8F4A-1F31714AA61A
http://www.sskkii.gu.se/publications/Documents/html/dynwhatif/

• goal-seeking: Indicating a taget cell,its desired value and changing a cell.
  
• datamanagement
• programmability (macros)

Most OLAP systems ahev the look and feel of advance spreadsheet software.

Most spreadsheet packages provide seemless integration beciase they read and write common file structures and easily interface with databases.

Why use models?

• simplify exploration by allowing an emphasis on some aspects (of reality) while ignoring others.

• possible to compress years of data into a single model,

• help to better understand complex real situations,

• cheaper to play with and experiment with model than using the real thing,

• Experimenting with ‘reality’ may not be reversible,

• Simulation models are suited to ‘what-if?’ scenarios,

• can be re-run with different states and data,

• economical ‘trial and error’ testing is facilitated

Choosing to do nothing!

One option that is always present, but often overlooked, is the choice to do nothing.

It should be placed on the list of ‘what-if’ scenarios, and evaluated along with the intentional alternatives as one possible choice.