Computer-Based Applications: Decision Support Systems Version 4.0 - 10/18/99

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Computer-Based ApplicationsDecision Support
SystemsVersion 4.0 - 10/18/99
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Note to the Student

• Previous lectures have dealt with the theoretical
  background of decision-making - both at the
  individual and group levels.
• This lecture begins to look at the actual
  building of application systems for decision
  support so-called decision support systems.
• Decision Support Systems are abbreviated as DSS.

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Quick Reviews

• Simons Model of Decision Making
• Models

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Simons ModelFlowchart of Decision Process
Intelligence
Design
Choice
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Intelligence Phase

• Organizational Objectives
• Search and SCANNING Procedures
• Data Collection
• Problem Identification
• Problem Classification
• Problem Statement

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Design Phase

• Formulate a Model
• Set Criteria for Choice
• Search for Alternatives
• Predict and Measure Outcomes

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Choice Phase

• Solution to the Model
• Sensitivity Analysis
• Selection of best (good) alternative(s)
• Plan for implementation (action)
• Design of a control system

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The role of models in decision-making

• A major characteristic of decision-making is the
  use of models.
• A model is a simplified representation or
  abstraction of reality.
• It is usually simplified because reality is too
  complex to copy.
• Basis idea is that analysis is performed on a
  model rather than on reality itself.

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Pounds Categories of Models - Expectations
against which reality is measured

• Historical - expectation based on extrapolation
  of past experience.
• Planning - the plan is the expectation
• Inter-organizational - Models of other people in
  the organization (e.g. superiors, subordinates,
  other departments, etc.)
• Extra-organizational - models where the
  expectations are derived from competition,
  customers, professional organizations, etc.

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Another classification of models

• Iconic Models
• Analog Models
• Mathematical Models
• Mental Models

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Iconic and Analog Models

• Iconic (scale) models - the least abstract model,
  is a physical replica of a system, usually based
  on a different scale from the original. Iconic
  models can scale in two or three dimensions.
• Analog Models - Does not look like the real
  system, but behaves like it. Usually
  two-dimensional charts or diagrams. Examples
  organizational charts depict structure,
  authority, and responsibility relationships maps
  where different colors represent water or
  mountains stock market charts blueprints of a
  machine speedometer thermometer

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Mathematical Models

• Mathematical (quantitative) models - the
  complexity of relationships sometimes can not be
  represented iconically or analogically, or such
  representations may be cumbersome or time
  consuming.A more abstract model is built with
  mathematics.
• Note recent advances in computer graphics use
  iconic and analog models to complement
  mathematical modeling.
• Visual simulation combines the three types of
  models.

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Mental Models

• People often use a behavioral mental model.
• A mental model is an unworded description of how
  people think about a situation.
• The model can use the beliefs, assumptions,
  relationships, and flows of work as perceived by
  an individual.
• Mental models are a conceptual, internal
  representation, used to generate descriptions of
  problem structure, and make future predications
  of future related variables.
• Support for mental models are an important aspect
  of Executive Information Systems. We will discuss
  this in depth later.

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Decision (Cognitive) Styles

• Analytic - planned, sequential approach learn by
  analyzing less emphasis on feedback formal
• Heuristic - learn more by acting than analyzing
  situations extensive feedback intuition, common
  sense trial and error.
• Autocratic vs. democratic

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The Origins of DSS

• The DSS movement grew out of dissatisfaction with
  two earlier and very successful applications of
  technology to management
• Operations Research and Management Science
  (OR/MS)
• Management Information Systems (MIS)
• By 1970 both technologies were viewed as too
  limited to
• meet growing demand of managers for more
  effective decision support
• make proper use of the expanding capabilities of
  information processing technology

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Origins of DSS Problems with OR/MS

• The problem with OR/MS was that it was directed
  to the construction of decision models and to the
  development of model solution techniques (e.g. in
  mathematical programming and stochastic
  processes).
• There was insufficient attention paid to the
  implementation of these models.
• No attention paid to the on-going use of models
  by practicing managers.

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Origins of DSS Problems with MIS

• MIS focused too much on support for structured
  decision processes, rather than semi-structured
  or unstructured processes.
• MIS technology generally used byproduct
  information from transaction processing systems
  to provide summary reports for repetitive
  decision processes.

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Origins of DSS Early Work

• The origin of Decision Support Systems (DSS) as
  a domain of study can be traced back to the late
  1960s at the Sloan School of management at MIT
  where they studied ill-structured problems.
• At the time, general ledger systems, financial
  planning models, programming languages, databases
  with query capabilities all came to be referred
  to as DSSs. Was DSS just another buzzword?
• Contradictory claims and observations abounded on
  this new concept.

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Characteristics of Ill-Structured Problems

• The preferences, judgments, and experiences of
  the decision maker are essential.
• The search for a solution implies a mixture of
• search for information
• formalization, or problem definition and
  structuring (system modeling)
• computation
• data manipulation
• The sequence of the above operations is not known
  in advance since
• it can be a function of data
• it can be modified, given partial results
• it can be a function of user preferences

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Characteristics of Ill-Structured Problems - 2

• Criteria for the decision are numerous, in
  conflict, and highly dependent on the perception
  of the user (user modeling).
• The solution must be achieved in limited time.
• The problem evolves rapidly.
• Ill-structured problems have many of the same
  characteristics of the semi-structured or
  unstructured problems discussed earlier.

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Decision Support System Origins Just Another
Buzzword

• First there were bookkeeping systems, which made
  it easy to keep track of things and to generate
  financial statements.
• With the commercial computer came EDP Systems
  which automated many bookkeeping functions.
• Then came Management Information Systems
  (Management Reporting Systems) which proved so
  cumbersome and inflexible that management
  couldnt use them.
• The next panacea of buzzwords came to be known as
  decision support systems.

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Contradictory Claims and Observations about DSS

• DSS are interactive systems used directly by
  managers vs. DSS are typically used by staff.
• DSS require special computer terminals and
  languages vs. DSS can be installed almost
  anywhere.
• DSS projects require careful analysis by highly
  skilled designers vs. Initial versions of DSS can
  be built and installed for 10,000.
• DSS must be tailored to information needs and
  personal style of individual managers vs. DSS can
  be installed to coordinate the efforts of many
  departments across a corporation.

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Origins of DSS The first DSS

• In 1971, under the idea of management decision
  systems, Michael Scott-Morton implemented a model
  of the production/distribution network of a major
  manufacturing company.
• The system was the first to do sensitivity (what
  if) analyses of possible changes in production,
  distribution, and marketing.
• It had two important concepts
• A convenient interactive graphics interface for
  users.
• The collective use of the system by individual
  managers improved over all organizational
  effectiveness - the aggregate performance of
  integrated operations within the firm.

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Scott-Morton Management Decision Systems

• The concepts of DSS were first articulated in the
  early 1970s by Michael Scott-Morton under the
  term management decision systems. He defined
  such systems as ... interactive computer-based
  systems, which help decision makers utilize data
  and models to solve unstructured problems....
  (Scott-Morton, 1971).

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Keen and Scott Morton DSS

• Keen and Scott-Morton published a seminal book on
  DSS in 1978.
• Their classic definition
• 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
  semi-structured problems (Keen and
  Scott-Morton, 1978).

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Keen and Scott-Morton Three Purposes of a DSS

• Assist managers in their semi-structured tasks.
• Accomplished by providing interactive access to
  stored data and decision models with a convenient
  user interface.
• Support, rather than replace managerial judgment.
• interactive capabilities and convenient user
  interface allow managers to exert more control
  over the application of technology
• Improve the effectiveness of decision making,
  rather than efficiency
• extend the range and capability of manager
  decision processes by means of user-friendly
  interfaces to rapid analyses of decision problems.

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DSS Current Definitions

• A DSS is an interactive system that helps people
  make decisions, use judgment, and work in areas
  where no one knows exactly how the task should be
  done in all cases. DSSs support decision making
  in semi-structured and unstructured domains, and
  provide information, models, or tools for
  manipulating data (Alter, 1995).

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DSS Current Definitions - 2

• A computer program that provides information in a
  given domain of application by means of
  analytical decision models and access to
  databases, in order to support a decision maker
  in making decisions effectively in complex and
  ill-structured (non-programmable) tasks (Klein
  and Methlie, 1995).

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The Role of MIS

• Management Information Systems
• impact on structured tasks where standard
  operating procedures, decision rules, and
  information flows can be readily defined.
• Main payoff in improving efficiency by reducing
  costs, turnaround time, and so on by replacing
  clerical personnel.
• Relevance for managers decision making has been
  mainly indirect, (e.g. providing reports and
  access to data).

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The Role of OR/MS

• Operations Research/Management Science
• Impact has been mostly on structured problems
  (rather than tasks) where the objective data, and
  constraints can be pre-specified.
• The payoff has been in generating better
  solutions for given types of problems.
• Relevance for managers has been the provision of
  detailed recommendations and new methodologies
  for handling complex problems.

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The Role of DSS in the context of MIS and OR/MS

• Decision Support Systems
• Impact is on decisions where there is sufficient
  structure for computer and analytic aids to be of
  value but where managers judgment is essential.
• Payoff is in extending the range and capability
  of computerized managers decision process to
  help them improve effectiveness.
• Relevance is the creation of a supportive tool,
  under managers own control, that does not
  attempt to automate the decision process,
  predefine objectives, or impose solutions.

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DSS Working Definition

• A DSS is an interactive, flexible, and adaptable
  computer-based information system that utilizes
  decision rules, models, and model base coupled
  with a comprehensive database and the decision
  makers own insights, leading to specific,
  implementabale decisions in solving problems that
  would not be amenable to management science
  optimization models per se. Thus, a DSS supports
  complex decision making and increases its
  effectiveness.

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Examples of Problem solving with DSS

• Firestone Rubber Tire Company
• Houston Minerals Corporation
• Portfolio Management
• Police-beat allocation in San Jose, California
• Mississippi River traffic management
• (examples all read/discussed in class)

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Idealized Characteristics and Capabilities of a
DSS

• Provide support in semi-structured and
  unstructured situations by bringing together
  human judgment and computerized information.
• Support is provided for various management levels
  ranging from top management to line managers.
• Support is provided to individuals as well as
  groups.
• Supports several independent and/or sequential
  decisions.

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Idealized Characteristics and Capabilities of a
DSS - 2

• Supports all phases of the decision-making
  process Intelligence, Design, Choice
• Supports a variety of decision-making processes
  and styles, e.g. a fit between the DSS and
  attributes of the decision makers.
• DSS must be adaptive over time
• DSS must be easy to use.
• DSS attempts to improve the effectiveness of the
  decision rather than efficiency.

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Idealized Characteristics and Capabilities of a
DSS - 3

• Decision maker has complete control over all
  steps of the process. It supports, not replaces
  the decision maker.
• DSS leads to learning, which leads to new
  demands, and the refinement of the system.
• DSS should be easy to construct.

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Sensitivity Analysis

• SensitivityAnalysis - study of the impact that
  changes in one (or more) parts of a model have on
  other parts. Generally looks at what impacts
  changes in input variables have on output
  variables.
• Enables flexibility and adaptation to changing
  conditions.
• Applicability to different situations
• better understanding of the model and the problem
  it supports.
• What-If Analysis and Goal Seeking

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What-If Analysis

• Model maker makes predictions and assumptions
  regarding the input data.
• When a model is solved, the future depends on
  this data.
• What If the cost of carrying inventory increases
  15?
• What will be the market share if advertising
  budget increases by 5?

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Goal Seeking

• Attempts to find the value of inputs necessary to
  achieve a desired output level.
• Represents a backwards solution
• If an initial analysis yields profits of 2
  million, what sales volume is necessary for a
  profit of 2.2 million?

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DSS Components

• Data Management
• DSS database
• Database Management System
• Data Directory
• Query facility
• Model Management
• Model Base
• Model base management system
• Model Directory
• Model execution, integration, and command
• Communication (dialogue) subsystem.

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DSS Early Research

• Much of the early research on DSS was influenced
  by the progress in data management (e.g.
  commercial implementations of hierarchical and
  network models in the 1970s, the relational
  model in the 1980s).
• Much early work attempted to incorporate decision
  models and user interfaces into data management
  systems (See Alters Classification Schema).
• However, later research has seen the emphasis on
  model management. Data management and dialogue
  management have many applications outside of DSS.

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Model Management

• Research on model management began with the
  suggestion that decision models, like data, are
  an important organizational resource and that
  software systems, called model management
  systems, should be constructed to assist in
  organizing and utilizing this resource.
• The purpose of a model management system is to
  make the organization and processing of models
  transparent to the DSS user, just as the purpose
  of a data management system is to make the
  organization and processing of stored data
  transparent to those who wish to maintain it.

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Model Management

• Model management became viewed as an extension of
  data management with the result that some
  information sources were algorithms rather than
  files.
• Current research on relational model management
  systems includes instances where the output of
  one model is the inputs of another model.

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Model Base Management

• Conceptually, the DSS contains a Model Base
  Management System that manages models and
  analysis programs in much the same way that a
  database management system manages data. Besides
  providing access to a wide variety of models for
  flexible use, the MBMS should contain
• ability to catalog and maintain a wide variety of
  models.
• the ability to interrelate these models and link
  them to the database
• the ability to integrate model building blocks
• the ability to manage the model base with
  functions analogous to database management.

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Types of Models Strategic

• Strategic Models -use to support top managements
  strategic planning responsibilities
• tend to be broad in scope with many variables
  expressed in a compressed form. The models tend
  to be of a descriptive (simulation) rather than
  an optimization nature.
• Examples
• develop corporate objectives
• environmental impact analysis
• non-routine capital budgeting

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Types of Models Tactical

• Used by middle management in allocating and
  controlling the organizations resources.
• May be applicable only to one organizational unit
  or subsystem (e.g. accounting subsystem).
• Some are optimization while others are
  descriptive in nature.
• Examples
• labor requirement planning
• sales promotion planning
• plant layout determination
• routine capital budgeting

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Types of Models Operational

• Operational Models are used to support day to day
  working activities of the organization.
• Examples
• approving personal loans by a bank
• production scheduling
• inventory control
• maintenance planning and scheduling
• quality control

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Model Building Blocks

• In addition to strategic, tactical, and
  operational models, the model base could contain
  model building blocks and subroutines.
• Examples
• random number generators
• curveline fitting routines
• present-value computational routines
• regression analysis
• All of the above can be used individually for
  data analysis or combined as components of
  larger, more complex models.

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Communication Dialogue Subsystem

• Interface Modes
• Menu Interaction
• Command Language
• Question and Answer
• Form Interaction
• Natural Language
• Object Manipulation
• Interactive Display
• Color Graphics
• Report Writing

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New Directions

• Model management, along with data and dialogue
  management continue to be an important focus of
  DSS research. However all three are being
  influenced by developments in artificial
  intelligence and especially in expert or
  knowledge-based systems.
• Some DSSs contain knowledge bases and the
  inferential procedures needed to apply them to a
  specific decision problem. Examples have been
  developed for intelligent production scheduling,
  portfolio management, underwriting, financial
  statement analysis, diagnosis of equipment
  failures.

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New Directions - 2

• Another area is using artificial intelligence to
  improve the management of data, models, and
  dialogue in a DSS.
• Expert systems have been developed to help build
  the model itself. Specifically expert systems
  have been developed to help novice users develop
  linear programming models.
• ERGO is a system that explains anomalies in
  spreadsheet outputs. If a what-if query
  produces counterintuitive results, ERGO attempts
  to find a simple explanation.

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New Directions - 3

• Another area of research is the development of
  active DSSs. These systems adapt themselves to
  the needs of their users, e.g. intervening in the
  decision process when support is needed.

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Organizational Issues in DSS Development

• Much of the formative years of DSS research
  focused on the impact of individual behavioral
  characteristics (e.g. risk preference, cognitive
  style).
• Today it is felt that many variables, behavioral
  and technological affect the successful use of
  DSS. Isolating a few significant behavioral
  variables appears less promising that thought
  earlier.
• Today most behavioral research in DSS is being
  directed away from analysis of individual users
  and towards the use of DSS by groups and entire
  organizations.

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Group Decision Support Systems

• Designed to support Group Communication and
  Decision processes.
• Level I - facilitate communication among group
  members. Provide the technology necessary to
  communicate decision rooms, facilities for
  remote conferencing.
• Level II - contain communication features of
  Level I plus provide support for the decision
  making process. They furnish DSS modeling
  capabilities and software for activities such as
  brainstorming, the delphi technique, nominal
  group technique, or other group processes.

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Characteristics of GDSS

• Aside from database, model, and dialog component,
  they also contain a communication component that
  would interface with the organizations LAN or
  WAN, e-mail, etc. so that the GDSS can interface
  with other GroupWare.
• Features for prompting and summarizing votes and
  ideas of participants
• ability to have anonymous interactions to
  encourage participation by all group members.
• Expanded model base for models supporting group
  decision processes.

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Characteristics of GDSS - contd.

• Ability to have a protocol or transcript of the
  groups interactions for organizational memory.
• Support for role of facilitator