Decision Support
Systems

Model-oriented DSS

Data-oriented DSS

data

models

user interface

knowledge

Degree of Structured

Highly structured

Semi-structured

Highly unstructured

Types of Control

Strategic planning (top-level, long-range)

Management control (tactical planning)

Operational control

Classical Definitions of DSS

Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems

Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems

Definition

Work system: a system in which human participants and/or machines perform a business process, using information, technology, and other resources, to produce products and/or services for internal or external customers

Elements of a Work System

1- Business process. Variations in the process rationale, sequence of steps, or methods used for performing particular steps

2- Participants. Better training, better skills, higher levels of commitment, or better real-time or delayed feedback

3- Product and services. Better ways to evaluate potential decisions

4- Customers. Better ways to involve customers in the decision process and to obtain greater clarity about their needs

5- Infrastructure. More effective use of shared infrastructure, which might lead to improvements

6- Environment. Better methods for incorporating concerns from the surrounding environment

7- Strategy. A fundamentally different operational strategy for the work system

8- Information. Better information quality, information availability, or information presentation

9- Technology. Better data storage and retrieval, models, algorithms, statistical or graphical capabilities, or computer interaction

Data Management Subsystem

Includes the database that contains the data

Database management system (DBMS)

Can be connected to a data warehouse

Model Management Subsystem

Model base management system (MBMS)

User Interface Subsystem

Knowledgebase Management Subsystem

Organizational knowledge base

Easy access to data/models/knowledge

Proper management of organizational experiences and knowledge

Easy to use, adaptive and flexible GUI

Timely, correct, concise, consistent support for decision making

Support for all who needs it, where and when he/she needs it

Solve semi-structured and unstructured problems

Support managers at all levels

Support individuals and groups

Interdependence and sequence of decisions

Support Intelligence, Design, Choice

Human control of the process

Ease of development by end user

Modeling and analysis

Data access

Standalone and web-based integration

Support varieties of decision processes

Support varieties of decision trees

Quick response

Adaptable and flexible

Interactive and ease of use

Interactive and efficiency

Speedy computations

Improved communication and collaboration

Increased productivity of group members

Improved data management

Overcoming cognitive limits

Quality support; agility support

Using Web; anywhere, anytime support

In a narrow sense DSS refers to a process for building customized applications for unstructured or semi-structured problems

1- Inputs: resources

2- Output: attainment of goals

3- Measure of success: outputs / inputs

Define the problem

Classify the problem into a standard category

Construct a model that describes the real-world problem

Identify possible solutions to the modeled problem and evaluate the solutions

Compare, choose, and recommend a potential solution to the problem

Interpersonal

1- Figurehead

2- Leader

3- Liaison

Informational

4- Monitor

5- Disseminator

6- Spokesperson

Decisional

7- Entrepreneur

8- Disturbance handler

9- Resource allocator

10- Negotiator

The term BI was coined by the Gartner Group in the mid

However, the concept is much older

MIS reporting - static/periodic reports

Executive Information Systems (EIS)

- OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term “BI”

Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards

report delivery and alerting

enterprise reporting (using dashboards and scorecards)

cube analysis (also known as slice-and-dice analysis)

ad-hoc queries

statistics and data mining

Faster, more accurate reporting (81%)

Improved decision making (78%)

Improved customer service (56%)

Increased revenue (49%)

Four major components

a data warehouse

business analytics

business performance management

a user interface

A significant part of many DSS and BI systems

A model is a simplified representation (or abstraction) of reality

Often, reality is too complex to describe

Much of the complexity is actually irrelevant in solving a specific problem

Models can represent systems/problems at various degrees of abstraction

Models can be classified based on their degree of abstraction

Iconic models (scale models)

Analog models

Mental Models

Mathematical (quantitative) models

The Benefits of Models

Ease of manipulation

Compression of time

Lower cost of analysis on models

Cost of making mistakes on experiments

Inclusion of risk/uncertainty

Evaluation of many alternatives

Reinforce learning and training

Web is source and a destination for it

Small organizations

Individuals

Conflicting objectives

Medium-to-large organizations

Groups

Different styles, backgrounds, expectations

Conflicting objectives

Consensus is often difficult to reach

Help: Computer support, GSS, …

Intelligence phase

Problem Classification

Classification of problems according to the degree of structuredness

Problem Decomposition

Information/data can improve the structuredness of a problem situation

Often solving the simpler subproblems may help in solving a complex problem

Problem Ownership

Outcome of intelligence phase:

A Formal Problem
Statement

Design phase

Selection of a Principle of Choice

It is a criterion that describes the acceptability of a solution approach

Reflection of decision-making objective(s)

In a model, it is the result variable

Choosing and validating against

High-risk versus low-risk

Optimize versus satisfice

Criterion is not a constraint

Normative models (= optimization)

the chosen alternative is demonstrably the best of all possible alternatives

Assumptions of rational decision makers

Humans are economic beings whose objective is to maximize the attainment of goals

For a decision-making situation, all alternative courses of action and consequences are known

Decision makers have an order or preference that enables them to rank the desirability of all consequences

Heuristic models (= suboptimization)

the chosen alternative is the best of only a subset of possible alternatives

Often, it is not feasible to optimize realistic (size/complexity) problems

Suboptimization may also help relax unrealistic assumptions in models

Help reach a good enough solution faster

Choice phase

The actual decision and the commitment to follow a certain course of action are made here

The boundary between the design and choice is often unclear (partially overlapping phases)

Generate alternatives while performing evaluations

Includes the search, evaluation, and recommendation of an appropriate solution to the model

Solving the model versus solving the problem!

Search approaches

Analytic techniques (solving with a formula)

Algorithms (step-by-step procedures)

Heuristics (rule of thumb)

Blind search (truly random search)

Additional activities

Sensitivity analysis

What-if analysis

Goal seeking

Implementation phase

Solution to a problem = Change

Change management?

Implementation: putting a recommended solution to work

The manner by which decision makers think and react to problems

perceive a problem

cognitive response

values and beliefs

When making decisions, people…

follow different steps/sequence

give different emphasis, time allotment, and priority to each steps

Personality temperament tests are often used to determine decision styles

There are many such tests

Meyers/Briggs,

True Colors (Birkman),

Keirsey Temperament Theory

Various tests measure somewhat different aspects of personality

They cannot be equated!

Decision-making styles

Heuristic versus Analytic

Autocratic versus Democratic

Consultative (with individuals or groups)

A successful computerized system should fit the decision style and the decision situation

Should be flexible and adaptable to different users (individuals vs. groups)

Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology

Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics

Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions

Managerial decision making is synonymous with the entire management process

A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)

Define the problem (or opportunity)

Construct a model that describes the real-world problem

Identify possible solutions to the modeled problem and evaluate the solutions

Compare, choose, and recommend a potential solution to the problem