Transcript Computer-Based Applications: Decision Support Systems
Computer-Based Applications: Decision Support Systems
Version 4.0 - 10/18/99 CIS 465 - Decision Support Systems - Fall 1999 1
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.
CIS 465 - Decision Support Systems - Fall 1999 2
Quick Reviews
• Simon’s Model of Decision Making • Models
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Simon’s Model Flowchart 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 CIS 465 - Decision Support Systems - Fall 1999 11
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 CIS 465 - Decision Support Systems - Fall 1999 15
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 1960’s 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 DSS’s. 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 couldn’t 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 1970’s 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. DSS’s 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 manager’s 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 manager’s 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 manager’s 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 maker’s 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 1970’s, the relational model in the 1980’s).
• Much early work attempted to incorporate
decision models and user interfaces into data management systems (See Alter’s 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. CIS 465 - Decision Support Systems - Fall 1999 43
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
management’s 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 organization’s 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 DSS’s 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. CIS 465 - Decision Support Systems - Fall 1999 50
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 DSS’s. 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 organization’s 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
group’s interactions for organizational memory.
• Support for role of facilitator
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