MIS470-Chp2_FoundationsTechforDM.pdf

Chapter 2: Foundations and Technologies for

Decision Making

Learning Objectives
• Understand the conceptual foundations of decision

making

• Understand Simon’s four phases of decision making:
intelligence, design, choice, and implementation

• Understand the essential definition of decision
support systems (DSS)

• Understand different types of DSS classifications

(Continued…)
MIS470: Decision Support & Business Intelligence 2

Learning Objectives
• Learn the capabilities and limitations of DSS in

supporting managerial decisions

• Learn how DSS support for decision making can be
provided in practice

• Understand DSS components and how they integrate

MIS470: Decision Support & Business Intelligence 3

Opening Vignette

Decision Modeling at HP Using
Spreadsheets

• Background

• Problem description

• Proposed solution

• Results

• Answer & discuss the case questions…

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Characteristics of Decision Making

• Groupthink

• Evaluating what-if scenarios

• Experimentation with a real system!

• Changes in the decision-making environment may
occur continuously

• Time pressure on the decision maker

• Analyzing a problem takes time/money

• Insufficient or too much information

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Characteristics of Decision Making
Decision Support Systems (DSS)

Dissecting DSS into
its main concepts


Building successful
DSS requires a
thorough
understanding of
these concepts

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Decision Making

• 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 – Simon (1977)

• Example: Planning
• What should be done? When? Where? Why? How? By

whom?

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Decision-Making Disciplines
• 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

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Decision-Making Disciplines

• Better decisions
• Tradeoff: accuracy versus speed

• Fast decision may be detrimental

• Many areas suffer from fast decisions

• Effectiveness versus Efficiency

• Effectiveness  “goodness”, “accuracy”

• Efficiency  “speed”, “less resources”

A fine balance is what is needed!

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Decision Style

• 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 step

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Decision Style

• Personality temperament tests are often used to determine decision
styles

• There are many such tests
• Meyers/Briggs,

• True Colors (Birkman), https://www.youtube.com/watch?v=wHPq7TZiMKo

• Keirsey Temperament Theory,

• Various tests measure somewhat different aspects of personality.

They cannot be equated!

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Decision Style

• 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)

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Decision Makers

• 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, etc.

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Phases of Decision-Making Process

• Humans consciously or subconsciously follow a systematic decision-
making process – Simon (1977)

1) Intelligence

2) Design

3) Choice

4) Implementation

5) (?) Monitoring (a part of intelligence?)

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Simon’s Decision-Making Process

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Decision Making: Intelligence Phase

• Scan the environment, either intermittently or
continuously

• Identify problem situations or opportunities

• Monitor the results of the implementation

• Problem is the difference between what people desire
(or expect) and what is actually occurring
• Symptom versus Problem

• Timely identification of opportunities is as important as
identification of problems

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Decision Making: Intelligence Phase

• Potential issues in data/information collection and estimation
• Lack of data

• Cost of data collection

• Inaccurate and/or imprecise data

• Data estimation is often subjective

• Data may be insecure

• Key data may be qualitative

• Data change over time (time-dependence)

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Application Case 2.1

Making Elevators Go Faster!

• Background

• Problem description

• Proposed solution

• Results

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Decision Making: Intelligence Phase

• Problem Classification
• Classification of problems according to the degree of structuredness

• 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 Ownership

• Outcome of intelligence phase 
A Formal
Problem
Statement

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Web and the
Decision-
Making
Process

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Decision Making: The Design Phase

• Finding/developing and analyzing possible courses of actions

• A model of the decision-making problem is constructed,
tested, and validated

• Modeling: conceptualizing a problem and abstracting it into a
quantitative and/or qualitative form (i.e., using
symbols/variables)
• Abstraction: making assumptions for simplification

• Tradeoff (cost/benefit): more or less abstraction

• Modeling: both an art and a science

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Decision Making: The 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!
• See Technology Insight 2.1

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Decision Making: The Design Phase
• 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 or preference that enables them to rank the
desirability of all consequences

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Decision Making: The Design Phase
• 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

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Decision Making: The Design Phase

• Descriptive models
• Describe things as they are or as they are believed to be

(mathematically based)

• They do not provide a solution but information that may lead
to a solution

• Simulation – most common descriptive modeling method
(mathematical depiction of systems in a computer
environment)

• Allows experimentation with the descriptive model of a
system

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Decision Making: The Design Phase

• Good Enough, or Satisficing
“something less than the best”
• A form of suboptimization
• Seeking to achieve a desired level of performance

as opposed to the “best”
• Benefit: time saving

• Simon’s idea of bounded rationality

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Decision Making: The Design Phase
• Developing (Generating) Alternatives

• In optimization models (such as linear programming), the
alternatives may be generated automatically

• In most MSS situations, however, it is necessary to
generate alternatives manually

• Use of GSS helps generate alternatives

• Measuring/ranking the outcomes
• Using the principle of choice

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Decision Making: The Design Phase
• Risk

• Lack of precise knowledge (uncertainty)

• Risk can be measured with probability

• Scenario (what-if case)
• A statement of assumptions about the operating

environment (variables) of a particular system at a given
time

• Possible scenarios: best, worst, most likely, average (and
custom intervals)

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Decision Making: The 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!

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Decision Making: The Choice Phase
• 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

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Decision Making: The Implementation Phase
“Nothing more difficult to carry out, nor more doubtful of success,
nor more dangerous to handle, than to initiate a new of
things.”

– The Prince, Machiavelli 1500s

• Solution to a problem  Change

• Change management ?

Implementation: putting a recommended solution to work

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How Decisions are Supported

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How Decisions are Supported
• Support for the Intelligence Phase

• Enabling continuous scanning of external and internal
information sources to identify problems and/or opportunities

• Resources/technologies: Web; ES, OLAP, data warehousing,
data/text/Web mining, EIS/Dashboards, KMS, GSS, GIS,…

• Business activity monitoring (BAM)

• Business process management (BPM)

• Product life-cycle management (PLM)

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How Decisions are Supported
• Support for the Design Phase

• Enabling generating alternative courses of action, determining
the criteria for choice

• Generating alternatives
• Structured/simple problems: standard and/or special models

• Unstructured/complex problems: human experts, ES, KMS,
brainstorming/GSS, OLAP, data/text mining

• A good “criteria for choice” is critical!

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How Decisions are Supported
• Support for the Choice Phase

• Enabling selection of the best alternative given a complex
constraint structure

• Use sensitivity analyses, what-if analyses, goal seeking

• Resources
• KMS

• CRM, ERP, and SCM

• Simulation and other descriptive models

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How Decisions are Supported
• Support for the Implementation Phase

• Enabling implementation/deployment of the selected
solution to the system

• Decision communication, explanation and justification to
reduce resistance to change

• Resources
• Corporate portals, Web 2.0/Wikis

• Brainstorming/GSS

• KMS, ES

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DSS Capabilities
• DSS early definition: it is a system intended to support

managerial decisions in semistructured and unstructured
decision situations

• DSS were meant to be adjuncts to decision makers 
extending their capabilities

• They are computer based and would operate interactively
online, and preferably would have graphical output
capabilities

• Nowadays, simplified via Web browsers and mobile
devices

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DSS Capabilities

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DSS Classifications

• AIS SIGDSS Classification
1. Communication-driven and group DSS
2. Data-driven DSS
3. Document-driven DSS
4. Knowledge-driven DSS
5. Model-driven DSS

• Often DSS is a hybrid of many classes

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DSS Classifications

• Other DSS Categories
• Institutional and ad-hoc DSS
• Custom-made systems versus ready-made systems
• Personal, group, and organizational support
• Individual support system versus group support

system (GSS)…

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Components of DSS

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Components of DSS
1. Data Management Subsystem

• Includes the database that contains the data

• Database management system (DBMS)

• Can be connected to a data warehouse

2. Model Management Subsystem
• Model base management system (MBMS)

3. User Interface Subsystem

4. Knowledgebase Management Subsystem
• Organizational knowledge base

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DSS Components: Data Management
Subsystem

• DSS database

• DBMS

• Data directory

• Query facility

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DSS Components: Model Management
Subsystem

• Model base

• MBMS

• Modeling language

• Model directory

• Model execution,
integration, and
command
processor

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DSS Components: User Interface Subsystem

• Interface
• Application interface

• User Interface (GUI?)

• DSS User Interface
• Portal

• Graphical icons
• Dashboard

• Color coding

• Interfacing with PDAs, cell
phones, etc.
• See Technology Insight 2.2

for next gen devices

45MIS470: Decision Support & Business Intelligence