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Decision Support Systems

Decision Making

Decision Making Process

Define the problem (or opportunity)

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

Managerial decision making is synonymous with the entire management process

Decision-Making Disciplines

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

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

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

Decision Style

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

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

Decision-making styles

Consultative (with individuals or groups)

Autocratic versus Democratic

Heuristic versus Analytic

They cannot be equated!

Various tests measure somewhat different aspects of personality

There are many such tests

Keirsey Temperament Theory

True Colors (Birkman),

Meyers/Briggs,

Personality temperament tests are often used to determine decision styles

When making decisions, people…

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

follow different steps/sequence

The manner by which decision makers think and react to problems

values and beliefs

cognitive response

perceive a problem

Phases of Decision-Making Process

Implementation phase

Implementation: putting a recommended solution to work

Change management?

Solution to a problem = Change

Choice phase

Additional activities

Goal seeking

What-if analysis

Sensitivity analysis

Search approaches

Blind search (truly random search)

Heuristics (rule of thumb)

Algorithms (step-by-step procedures)

Analytic techniques (solving with a formula)

Solving the model versus solving the problem!

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

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

Generate alternatives while performing evaluations

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

Design phase

Heuristic models (= suboptimization)

Help reach a good enough solution faster

Suboptimization may also help relax unrealistic assumptions in models

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

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

Normative models (= optimization)

Assumptions of rational decision makers

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

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

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

the chosen alternative is demonstrably the best of all possible alternatives

Selection of a Principle of Choice

Criterion is not a constraint

Choosing and validating against

Optimize versus satisfice

High-risk versus low-risk

In a model, it is the result variable

Reflection of decision-making objective(s)

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

Intelligence phase

Outcome of intelligence phase:

A Formal Problem Statement

Problem Ownership

Problem Decomposition

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

Information/data can improve the structuredness of a problem situation

Problem Classification

Classification of problems according to the degree of structuredness

Decision Makers

Medium-to-large organizations

Help: Computer support, GSS, …

Consensus is often difficult to reach

Different styles, backgrounds, expectations

Groups

Small organizations

Conflicting objectives

Individuals

Model

The Benefits of Models

Web is source and a destination for it

Reinforce learning and training

Evaluation of many alternatives

Inclusion of risk/uncertainty

Cost of making mistakes on experiments

Lower cost of analysis on models

Compression of time

Ease of manipulation

Models can be classified based on their degree of abstraction

Mathematical (quantitative) models

Mental Models

Analog models

Iconic models (scale models)

Models can represent systems/problems at various degrees of abstraction

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

Often, reality is too complex to describe

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

A significant part of many DSS and BI systems

Business Intelligence (BI)

The Architecture of BI

Four major components

a user interface

business performance management

business analytics

a data warehouse

The Benefits of BI

Increased revenue (49%)

Improved customer service (56%)

Improved decision making (78%)

Faster, more accurate reporting (81%)

Styles of BI

statistics and data mining

ad-hoc queries

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

enterprise reporting (using dashboards and scorecards)

report delivery and alerting

A Brief History of BI

However, the concept is much older

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

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

Executive Information Systems (EIS)

MIS reporting - static/periodic reports

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

Managerial Decision Making

Mintzberg's 10 Managerial Roles

Decisional

10- Negotiator

9- Resource allocator

8- Disturbance handler

7- Entrepreneur

Informational

6- Spokesperson

5- Disseminator

4- Monitor

Interpersonal

3- Liaison

2- Leader

1- Figurehead

Management Science Approach

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

Identify possible solutions to the modeled problem and evaluate the solutions

Construct a model that describes the real-world problem

Classify the problem into a standard category

Define the problem

component

3- Measure of success: outputs / inputs

2- Output: attainment of goals

1- Inputs: resources

Dss

as a Specific Application

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

DSS can facilitate decision via:

Using Web; anywhere, anytime support

Quality support; agility support

Overcoming cognitive limits

Improved data management

Increased productivity of group members

Improved communication and collaboration

Speedy computations

DSS Characteristics and capabilities

Interactive and efficiency

Interactive and ease of use

Adaptable and flexible

Quick response

Support varieties of decision trees

Support varieties of decision processes

Standalone and web-based integration

Data access

Modeling and analysis

Ease of development by end user

Human control of the process

Support Intelligence, Design, Choice

Interdependence and sequence of decisions

Support individuals and groups

Support managers at all levels

Solve semi-structured and unstructured problems

Overall Capabilities of DSS

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

Timely, correct, concise, consistent support for decision making

Easy to use, adaptive and flexible GUI

Proper management of organizational experiences and knowledge

Easy access to data/models/knowledge

Components of DSS

Knowledgebase Management Subsystem

Organizational knowledge base

User Interface Subsystem

Model Management Subsystem

Model base management system (MBMS)

Data Management Subsystem

Can be connected to a data warehouse

Database management system (DBMS)

Includes the database that contains the data

A Work System View of Decision Support

Elements of a Work System

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

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

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

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

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

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

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

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

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

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

Concept of Decision Support Systems

Classical Definitions of DSS

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

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

Decision Support Systems (DSS) are designed to aid in the decision-making process for managers at various levels. They are characterized by their adaptability, flexibility, and capacity to handle semi-structured and unstructured problems.