Modeling and Analysis

DSS modeling Issues DSS can be composed of mul8ple models Modeling Issues - Iden8ca8on of problems and environment analysis

Variable iden8ca8on Forecas8ng (predic8ve analysis)

DSS modeling Categories Op8misa8on of problems with few alterna8ves

Op8misa8on via algorithm Op8misa8on via analy8cal formula Simula8on Heuris8cs Predic8ve models Other Models

DSS modeling Categories

DSS modeling Trends Model libraries and solu8on techniques Using web tools perform modeling, op8misa8on, simula8on etc

Mul8dimensional analysis Model for model analysis

Classica8on of DSS Models Sta$c Analysis: Sta8c model takes a single snapshot of situa8on

Everything occurs in a single interval. E.g. Make or buy decision Stability of the relevant data is assumed.

Dynamic Analysis: Represents scenarios that change over 8me. E.g. 5-year prot and loss projec8on in which the input data, such as costs, prices, and quan88es, change from year to year.

Time dependent Important because they use, represent, or generate trends and paRerns over 8me.

Shows average per period, moving averages and compara8ve analysis.

Certainty, uncertainty, and risk Decision situa8ons are oSen classied on the basis of what the decision maker believes about

the forecasted results. The categories are:

Certainty Risk Uncertainty

Decision Making Under Certainty Complete knowledge is available Decision maker knows the outcome of each course of ac8on

Situa8on involve is oSen with structured problems with short 8me horizons

Certain models are rela8vely easy to develop and solve and they can yield op8mal solu8ons.

Decision making under uncertainty

Several outcomes for each course of ac8on. Decision maker does not know, or cannot es8mate the possible outcomes.

More dicult because of insucient informa8on.

Involves assessment of the decision makers aXtude towards risk.

Decision making under risk (Risk analysis)

Decision maker must consider several possible outcomes for each alterna8ve.

The decision maker can assess the degree of risk associated with each alterna8ve.

Risk analysis can be performed by calcula8ng the expected value for each alterna8ve and selec8ng the one with best expected value.

Decision analysis with decision tables and decision trees

Decision Table: Organize informa8on and knowledge in systema8c tabular manner

Decision Trees: Alterna8ve representa8on of the decision table

Shows the rela8onship of the problem graphically and handle complex situa8ons

Can be cumbersome if there are many alterna8ves or sta8c nature.

TreeAge Pro and Precision Tree: Powerful and sophis8cated decision tree analysis systems

Structure of mathema8cal models for decision support

Components of decision

support mathema8cal models: Result Variables Decision Variables Uncontrollable variables Intermediate result variables

Result Variables: reect the level of eec8veness of a system

Decision Variables: describes alterna8ve course of ac8on.

Uncontrollable Variables: Some factors that aect the result variables but not under the control of decision maker.

Intermediate result Variables: reect intermediate outcomes in mathema8cal models.

Mul8ple Goals

Sensi8vity Analysis ARempts to assess the impact of a change in input data

on proposed solu8on. Important because it allows exibility and adapta8on

to changing condi8ons Provides a beRer understanding of the model and the

decision making situa8on Used for: 1.Revising models to eliminate too-large sensi8vi8es. 2.Adding details about sensi8ve variables. 3.Obtainong beRer es8mate of sensi8ve external

variables. 4.Altering a real-world system to reduce actual

sensi8vi8es.

What-If-Analysis

What will happen to the solu8on if an input variables, an assump8on, or a parameter value is changed

With the appropriate user interface, it is easy for manager to ask a computer model dierent ques8ons and get the answers.

Common in expert systems. User get an opportunity to change their answers to some ques8ons.

Goal Analysis Calculates the values of the inputs necessary to achieve a desired level of output.

Represents a backward solu8on approach

Problem solving search methods

The choice phase of problem solving involves a search for an appropriate course of ac8on.

Search approaches are:

Analy8cal Techniques Algorithms Blind Searching Heuris8c Searching

Simula8on

Is a appearance of reality. A technique for conduc8ng experiments with computer on model of a management system

Characteris$cs: 1.Simula8on typically imita8ve. 2.Technique for conduc8ng experiments. 3.Descrip8ve rather than a norma8ve. 4.Used only when a problem is too complex to be treated using numerical op8mizing techniques.

Advantages of simula8on Theory is fairly straighcorward. Great 8me compression Descrip8ve rather than norma8ve. Built from the managers perspec8ve. Built for one par8cular problem and cannot solve any other problem.

A manager can experiment to determine which decision variables and which part of environment are really important, and with dierent alterna8ves.

Can handle an extremely wide variety of problem types, such as inventory and stang.

Can include the real complexi8es of problems. Automa8cally produce many important performance measures.

Rela8vely easy-to-use simula8on packages. OSen the only DSS modeling method that can readily handle rela8vely unstructured problem.

Disadvantages of simula8on

An op8mal solu8on cannot be guaranteed. Model construc8on can be a slow and costly process.

Solu8ons are not transferable to other problems

Easy to explain to managers that analy8c methods are overlooked.

Requires special skills because of the complexity of the formal solu8on method.

The Methodology of Simula8on

Test & validate the

model

Real world problem

Dene the problem

Construct simula8on model

Implement the result

Design the simula8on experiments

Conduct the experiments

Evaluates the results

Simula8on type

Probabilis8c Simula8on: One or more of the independent variables Follow certain probability distribu8ons namely 1.Discete distribu8on

2.Con8nuous distribu8on

Conducted with the aid of technique called Monte Carlo simula8on.

Time-Dependent Vs Time-Independent Simula8on:

Time-independent-not important to know the exact 8me of event

Time-dependent-In wai8ng line problems, it is important to know the precise 8me of arrival.

Object-Oriented Simula8on: SIMPROCESS is an object-oriented process modeling tool that allows user to create a simula8on model by using screen based object.

Unied Modeling Language(UML)- Designed for object-oriented and object based systems and applica8ons.

Java based simula8ons are essen8ally object oriented.

Visual Simula8on: Graphical display of computerized results Includes anima8ons Is one of the most successful development in computer-human interac8ons and problem solving.

Quan8ta8ve SoSware Packages Are preprogrammed models and op8miza8on systems. Serve as building blocks for other quan8ta8ve models A variety of these are available for inclusion in DSS as

major and minor modeling components. Revenue management systems focus on iden8fying

right product for right customer. Airlines have used such systems to determine right

price for each airline seat. System also available for retail opera8ons,

entertainment venues, and many other industries.