Transcript ACS 1803 Lecture Outline 9

ACS 1803
Lecture Outline Decision Support
Systems
Decision Support Systems *L
-designed to help management make semistructured (or unstructured) decisions
- but such systems do not make decisions (why?)
- they focus more on what might happen rather
than what has happened
- typically include: *L
a) a data base, perhaps a "data warehouse", extracted
from a "live“ database,
b) a model base*** that uses the data base [a model is a
structured representation of some aspect of reality; it is
because of the model that we can examine effects of
decisions; but, a model always has assumptions e.g.,
inflation rate, net earnings level over 5 years; cost
increases]
c) a user-friendly interface (dialog), often involving
graphics
Three Fundamental DSS Components
-a DSS may be developed by people outside of the
Information Systems Department
- a DSS also can have capability for ad hoc reporting
from the data base (warehouse)
examples of decision support: *L
- should we buy out a company? should we expand into
another product line? [why semi-structured?I]
- classic illustration: Houston Oil and Mineral Co
Houston Oil and Minerals Example
• HOM was interested in a proposed joint venture
but required a risk analysis
• They built a DSS using a specialized planning
language (4GL named IFPS)
• Results from DSS model suggested the project be
accepted
• The Executive VP using his intuition, experience
and judgment asked that yet another possible but
improbable condition be examined by the DSS
Houston Oil and Minerals Example ct’d
• The DSS was flexible and responsive enough
to allow managerial judgment and intuition to
be incorporated into the analysis within less
than 1 hour
• Using the results that came back, the
executive reversed the decision and rejected
the joint venture proposal
• Ultimately this proved to be the right decision!
DSS Examples *L
• A more primitive example of a DSS is a
spreadsheet used for “what-if” analysis
• There are Excel templates built for certain
types of decisions [terms: template, model;
explain these]
• A more complex DSS is a simulation model of
a hospital’s surgical scheduling or other health
care structures (as in You Tube videos)
Also see paper handout
for DSS examples
Model-Driven Ex. – Loan Calculator
Variables to be Analyzed
Analysis Results
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Loan Calculator Model
Medical Clinic Simulation Model
• HC-Simulation Software to Optimize Healthcare
Processes https://www.youtube.com/watch?v=neBCg7N1UyM
• Flexsim Heathcare Urgent Care Tutorial
– Video 1
– Video 2
– Video 3
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More DSS Examples
• Airline industry: DSS helps to find proper
pricing to maximize overall revenue from
selling seats for each flight
– Mgr enters depart. pt, arr. pt, no of stops, times of
dep and arr, # days in advance for res, # persons,
size of plane, utilized capacity on similar previous
flights etc.
– System suggests variable ticket prices
DSS development and use
• Many DSS are not developed by computer
professionals (at least not alone)
– E.g., power sales support system at Manitoba
Hydro (engineer with MBA degree uses IFPS
system in the Finance department)
• DSS are used largely by middle and higher
managers
A Comparison of DSS and MIS
• DSS differs from an MIS in numerous ways,
including:
– The type of problems solved
– The support given to users
– The decision emphasis and approach
– The type, speed, output, and development of the
system used
– See comparison of DSS with MIS p. 292
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Expert Systems *MC
• p. 328-334
• such systems are different than traditional reporting
or DSS systems
• they apply artificial intelligence to situations where
many facts and complex decision rules are involved,
such that only a few people can solve such problems
well
• an expert system mimics the thinking of an expert
Expert Systems
• Expert system manipulate knowledge and not
just information
• e.g what drug and in what dose to give for
particular types of cancer
– Many factors involved
– Many questions must be asked
– Many IF … THEN rules
• A rule is a way of encoding knowledge
- an ES should be able to explain its reasoning to
the user
Expert Systems
• ***why develop them? *L
- to retain expert's knowledge if he retires or dies
- to pool expertise from several experts
- to clone the expert's knowledge and have it
available in many places at once (e.g., cancer
treatment in remote Manitoba areas)
• they can be developed through detailed
programming or through an "expert system shell"
such as VP Expert
Expert System structure *L
• Knowledge base
– Facts and rules
• Inference engine
– Software that takes user input and “sifts through”
the knowledge base mimicking the mind of an
expert
– See paper handout eg. of ES program
• This is artificial intelligence
Expert System Development *MC
• A knowledge engineer has special expertise in
eliciting information and expertise from
experts
• He / she translates the expert’s knowledge
into a set of (if .. then) rules
Expert Systems Examples *MC
• ES at California State U to advise students on
class selection
• Complex machine repair
• Cancer treatment in remote areas
• Computer user help desk
• See paper handout for HR example
Knowledge Management
• An expert system works on a knowledge base
– It is part of a larger area called ‘knowledge
management’
Knowledge Management
Knowledge Management
Definitions
*MC
The process an organization uses to gain the greatest value
from its knowledge assets
Knowledge Assets
All underlying skills routines, practices, principles, formulae,
methods, heuristics, and intuitions whether explicit or tacit
Explicit Knowledge
Anything that can be documented, archived, or codified
often with the help of information systems
Tacit Knowledge
The processes and procedures on how to effectively
perform a particular task stored in a person’s mind
Knowledge Management System (KMS) *MC
Best Practices
Procedures and processes that are widely accepted as
being among the most effective and/or efficient
Primary Objective
How to recognize, generate, store, share, manage this tacit
knowledge (Best Practices) for deployment and use
Technology
Generally not a single technology but rather a collection
of tools that include communication technologies (e.g.
e-mail, groupware, instant messaging), and information
storage and retrieval systems (e.g. database
management system) to meet the Primary Objective