Modelling and Analysis

Sean O'Sullivan: Modelling - building a model of the problem that will generate the alternatives. Sep 29, 2009 1:00:53 PM EST
Sean O'Sullivan: Analysis - incorporating the functions and process that will evalueate each alternative. Sep 29, 2009 1:01:55 PM EST
Sean O'Sullivan: 'processes that will evaluate' Sep 29, 2009 1:02:16 PM EST
Sean O'Sullivan: And then we strike a BIG problem! Sep 29, 2009 1:02:58 PM EST
Sean O'Sullivan: which is -- many problems are very different from each other. for example ' what is the best roster for next month?' or 'how shoudl I spend my marketing budget for the year?' Sep 29, 2009 1:04:23 PM EST
Sean O'Sullivan: Each of these two problems require different modelling and analysis, and each will probably need a different DSS. Sep 29, 2009 1:05:23 PM EST

Knowledge Clasification Page 143

Decision situations are often clasified on the basis of what the decision maker knows or believes about the forecasted results.

• Certainty
• Risk
• Uncertainty

When models are used, any of these conditions can occur and different kinds of models are appropriate for each case.

qualitative models

http://www.indiana.edu/~socpsy/papers/QualEncyclo.htm

Quantitative models

Quantitative models

http://openlearn.open.ac.uk/mod/resource/view.php?id=209082

http://en.wikipedia.org/wiki/Quantitative_analyst

A DSS can include multiple models

sometimes dozens, each of which represents a different part of the decision making problem.

Each model may either be native to the DSS or integrated, interfaced

Forecasting / Predictive Analysis

Essential for construction and manipulating models because when a deciion in implemeneted, the results usually occur in the future.

Ecommerce has created an immense need for forecasting and an abundance of available information for performing it.

Many orgs have accurately predicted demand for products and services using a variety of quantitive and qualitive methods.

CRM and revenue management systems rely heavily on forecasting techniques / predictive analysis

Variable Identification

Identification of a models variables

• decision
• result
• uncontrollable

Environmental Scanning and analysis page 138

Monitoring, scanning and interpreatation of collected data.

From wiki:
Environmental scanning is a process of gathering, analyzing, and dispensing information for tactical or strategic purposes. The environmental scanning process entails obtaining both factual and subjective information on the business environments in which a company is operating or considering entering. more...

OLAP - Online analytical processing

Activities performed by end users in online systems

• Specific, open-ended query generation

1. SQL

• Ad hoc reports
• Statistical analysis
• Building DSS applications

Modeling and visualization capabilities

Special class of tools

• DSS/BI/BA front ends
• Data access front ends
• Database front ends
• Visual information access systems

Model-based management systems

Provide MSS and DSS based upon models of the organisation and its components.

Makes use of software that allows model description and organization with transparent data processing.
Capabilities

• MSS / DSS user has control
• Flexible in design
• Gives feedback
• GUI based
• Reduction of redundancy
• Increase in consistency
• Communication between combined models

Relational model base management systems

• Virtual file
• Virtual relationship

Object-oriented model base management system

• Logical independence

Database and MIS design model systems

• Data diagram, ERD diagrams managed by CASE tools

Simulations page 165

• Imitation of reality
• Allows for experimentation and time compression
• Descriptive, not normative
• Can include complexities, but requires special skills
• Handles unstructured problems
• Optimal solution not guaranteed

• Methodology

1. Problem definition
2. Construction of model
3. Testing and validation
4. Design of experiment
5. Experimentation
6. Evaluation
7. Implementation
8. Explore problem at hand
9. Identify alternative solutions
10. Can be object-oriented
11. Enhances decision making
12. View impacts of decision alternatives

Find-by-search approaches

These are analytical techniques (algorithms) for finding possible outcomes for structured problems

• A general step-by-step search for solutions that eventually obtains (finds) an optimal solution.

Blind searching
Complete enumeration search
All alternatives explored
Incomplete search
Partial search

• Achieves satysficing of particular goal
• May obtain optimal goal

Heurisitic searching
Repeated, step-by-step searches
Rule-based, so used for specific situations
Will find a “good enough” solution, but eventually will obtain optimal outcome
Examples of heuristics
Tabu search
Remembers and directs toward higher quality choices
Genetic algorithms
Randomly examines pairs of solutions, chooses best solutions so far and mutates them to find even better solutions

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

Satisfaction of multiple goals page 150

• One decision-making process must try to meet more that one goal.
• Simultaneous goals are often conflicting – achieving one goal makes it harder to achieve the other(s).
• Try to determine a single measure of effectiveness that judges the achievement of all goals.
• Sometimes very difficult to do.

A decision situation in which alternatives are evaluated with several sometimes conflicting goals.

Methods:

• Utility theory.
• Goal programming.
• Linear programming with goals as constraints.
• Point allocation systems.

Mathematical programming page 153

(Not to be confused with computer programming).

Use of defined and verified mathematical processes (algorithms techniques etc.) to determine best outcomes.

• Linear programming
• MinMax techniques
• Game theory

Is a family of tools designed to solve managerial problems in which the decision maker must alloate scarce resources among competing activities to optimise a measurable goal.

• Tools for solving managerial problems
• Useful when decision-maker must allocate resources amongst competing activities.
• Allocation of resources to achieve optimised specific goals.

Mathematical models

Use mathematical techniques to link decision variables, uncontrollable variables, parameters, and result variables together:

• Decision Variables have values that describe choices.

• Uncontrollable Variables have values that are outside the decision-maker’s control.

• Parameters are fixed factors (constants).

• Intermediate Result Variables are intermediate outcomes.

• Result Variables are outcomes dependent on chosen decision variables, uncontrollable variables, parameters and intermediate results.

Quantitative models

• Quantitative relationships.
• Variables involved in the model have numerical values.
• Outcomes are numerical.

“if the sales this year are $400,000 and they increase by 10% next year, the sales will be $440,000”

Nonquantitative models (qualitative models)

• Qualitative relationships.
• Variables involved in the model have qualitative values that are represented within the model by numerical values.
• Outcomes are qualitative / numerical.

“indicate your happiness with our product on a scale of 1 (very unhappy) to 5 (very happy). …”

Results (outcomes) = f(decision variables, uncontrollable variables, parameters, intermediate results)

Modeling with decision trees

A graphical representation of a decision.

• A graphical representation of relationships between variables, values and outcomes.

• Each pathway along the ‘branches’ of the tree represents a particular sequence of criteria leading to a particular outcome.

• A multiple criteria approach.

• Can model probabilistic decision-making.

• Cumbersome if there are many alternative outcomes.

Modeling with decision tables

A tabular representation of a decision.

wiki: http://en.wikipedia.org/wiki/Decision_table

Can model multiple criteria decisions

Features include:

• Decision variables (alternatives)
• Uncontrollable variables
• Result variables

Applies principles of certainty, uncertainty, and risk

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.

Modeling with influence diagrams

A graphical representation of a decision.

• Shows the various variables in a decision-making problem, their types and the relationships between them.
• Provides an understanding of the relationships (dependencies) between variables involved in a decision.
• Can be developed to any level of detail
• Shows the effect of changes in controlled variables (but not the size of the effect).

Uses a standardised set of symbols to represent variables and dependencies.

Shapes indicate variables and their types