Download Info Systems

Information Systems in Organizations
–
Decision Making
February 24, 2016
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Mintzberg’s five 5 organizational parts
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Types of Organizations
• An organization is an administrative and functional
structure where people work toward a specific goal.
• Understanding the organization’s IT needs requires a
good understanding of its administrative and
functional structure.
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–
–
–
–
Hierarchical
Flat
Matrix
Holacratic
Other
Source: http://www.forbes.com/sites/jacobmorgan/2015/07/06/the-5-types-of-organizationalstructures-part-1-the-hierarchy/#788b78c43853
Source: https://www.ics.uci.edu/~corps/phaseii/Mintzberg-StructureIn5s-MgmtSci.pdf
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IS & Hierarchical Organizational structure
• .
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IS & Hierarchical Organizational structure
• .
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Administrative Information Systems
• Transaction Processing Systems (TPS)
– Basic business system that serves the operational level
(including analysts) in organizations
– Capture & process data generated during day-to-day activities
• Office Automation Systems (OAS)
– Systems designed to help office workers in doing their job.
• Decision Support Systems (DSS)
– Systems designed to support middle managers and business
professionals during the decision-making process
• Executive Information Systems (EIS) or Executive
Support Systems (ESS)
– Specialized DSS that help senior level executives make decisions.
• GDSS: computer-based systems that facilitate solving
of unstructured problems by set of decision makers
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Organization & IS: another view
Types of Information Systems:
Top
Management
Office
workers
Office
workers
- Transaction Processing Systems
- Office Automation Systems
- Knowledge Worker Systems
- Management Information Systems
- Decision Support Systems
- Executive Information Systems
Middle Management
Office
workers
Knowledge
workers
Questions
Office
workers
Lower Management
Operational workers
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Decision Making process
Simon’s decision-making
process model




Intelligence
Design
Choice
(Implementation)
Simon, H. (1955), A Behavioral Model of Rational
Choice, Quarterly Journal of Economics, vol. 69, 99–
188
Drucker Peter (1954), The Practice of Management, Harper & Brothers, New York
Newell, A., and Simon, H. A. (1972). Human
problem solving Englewood Cliffs, Prentice-Hall,
New Jersey.
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Intelligence Phase
• Scan the environment for
a problem.
• Determine if problem is
real, important enough,
solvable
• Determine if problem
within their scope of
influence?
• Fully define the problem
by gathering more
information.
Data source
Scan Environment for
problem to be solved
or decision to be made
Problem ?
No
Organizational
IS & external
data
END
Yes
Problem within
scope of influence?
No
END
Yes
Gather more information
about the problem
Internal &
External
data
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Design Phase
• Develop a model of
the problem.
– Determine type of
model.
• Verify model.
• Develop and
analyze potential
solutions.
Develop a model of
problem to be solved
Verify that the
model is accurate
Develop potential
solutions
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Choice Phase
• Evaluate solutions and select the solution
to implement.
– More detailed analysis of selected solution
might be needed.
– Verify initial conditions.
– Analyze proposed solution against real-world
constraints.
Questions
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DSS structure
Systems designed to help middle
managers make decisions
Major components
– Data management subsystem
• Internal and external data sources
– Analysis subsystem
User
Interface
Analysis
- Sensitivity Analysis
- What-if Analysis
- Goal-seeking Analysis
-Data-driven tools
-> Data mining
-> OLAP*
• Typically mathematical in nature
– User interface
• How the people interact with the DSS
• Data visualization is the key
– Text
– Graphs
– Charts
Data Management
-
Transactional Data
Data warehouse
Business partners data
Economic data
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* OLAP: OnLine Analytical Processing
DSS Analysis Tools
Simulation is used to examine proposed solutions
and their impact
– Sensitivity analysis
– Determine how changes in one part of the model influence other
parts of the model
– What-if analysis
– Manipulate variables to see what would happen in given scenarios
– Goal-seeking analysis
– Work backward from desired outcome
Determine monthly payment given various
interest rates.
14to
Works backward from a given monthly payment
determine various loans that would give that payment.
Executive Information Systems
 Specialized DSS that supports senior level
executives within the organization
 Most EISs offer the following capabilities:
 Consolidation – involves the aggregation of
information and features simple roll-ups to complex
groupings of interrelated information
 Drill-down – enables users to get details, and
details of details, of information
 Slice-and-dice – looks at information from different
perspectives
 Digital dashboards are common features
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Artificial Intelligence (AI) systems
Common categories of AI systems:
1.
Expert system – computerized advisory programs that imitate
the reasoning processes of experts in solving difficult
problems
Neural Network – attempts to emulate the way the human
brain works
2.
–
–
3.
4.
Analyses large quantities of info to establish patterns and
characteristics in situations where logic or rules are unknown
Uses Fuzzy logic – a mathematical method of handling imprecise or
subjective information
Genetic algorithm – an artificial intelligent system that mimics
the evolutionary, survival-of-the-fittest process to generate
increasingly better solutions to a problem
Intelligent agent – special-purposed knowledge-based
information system that accomplishes specific tasks on behalf
of its users
PBS Report on AI in today’s world
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Expert Systems
Artificial Intelligence systems that codify human
expertise in a computer system
– Main goal is to transfer knowledge from one person to
another
– Wide range of subject areas
•
•
•
•
Medical diagnosis
Computer purchasing
Finance
Accounting
(http://raw.rutgers.edu/MiklosVasarhelyi/Resume%20Articles/CHAPTERS%2
0IN%20BOOKS/C10.%20expert%20systems%20app%20in%20act.pdf )
– Knowledge engineer elicits the expertise from the expert
and encodes it in the expert system
– System engineer: IT professional who develops AI inference
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engine and other components of the Expert System.
Expert Systems Components
 Knowledge base: database of the expertise, often in IF THEN rules.
 Inference engine: derives recommendations from knowledge base and problem-specific
data
 User interface: controls the dialog between the user and the system
 Explanation system: Explain the how and why of recommendations
User
Domain
Expert
Expertise
Encoded
expertise
User
Interface
Inference
Engine
Knowledge
Engineer
Knowledge
base
Example of rules
Explanation
System
System
Engineer
IF
family is albatross AND
color is white
THEN
bird is laysan albatross.
IF
family is albatross AND
color is dark
THEN
bird is black footed albatross
- Knowledge engineer codify the human expert’s expertise into the systems’
knowledge base.
- System engineer is the IT professional who develop the user interface, the
inference engine, and the explanation system.
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Expert Systems: Pros and Cons
 Pros
 More reliable than humans, incorporating expertise
from many sources.
 Able to deduce rules that are not apparent, even to
experts
 Capable of being extended, as the system is
applied and knowledge grows
 Built in high-level computer languages (called Shell
– e.g. CLIPS, JESS, DROOLS) requiring few IT
skills
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Expert Systems: Pros and Cons
 Cons
 Often difficult to know a priori if experts who devise
the rules really do know all they claim to.
 Experts may sabotage the system with false
information, or withhold information, particularly if
their jobs are threatened.
 Rule following is not the best approach for all
situations.
 Built in high-level computer languages (and “Shells”
– e.g. CLIPS, JESS, DROOLS) that require few IT
skills
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Summary Questions
Notes
1) Compare Middle management and lower management tasks in terms of
their structure.
2) Compare Middle management and lower management tasks in terms of
their repetitiveness.
3) What is the difference between GDSS and DSS in terms of their target
users?
4) What is the difference between Decision Support Systems (DSS) and
Executive Information Systems (EIS) in terms of their target users.
6) Give an example of TPS.
7) (a) What are the major components in a DSS? (b) What is the function of
each?
8) What is an Expert System? What are the main components of an Expert
system? What is a knowledge engineer?
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Mintzberg’s 5 organizational parts
• Operating core
– Those who perform the basic work related directly to the production of
products and services.
• Strategic apex
– Charged with ensuring that the organization serve its mission in an effective
way, and also that it serve the needs of those people who control or
otherwise have power over the organization.
• Middle line
– Managers who stand in a direct line relationship between the strategic apex
and the operating core.
• Techostructure
– The analysts who serve the organization by affecting the work of others.
They may design it, plan it, change it, or train the people who do it, but they
do not do it themselves. Examples: engineers, accountants, planners,
researchers, etc.
• Support staff
– The specialists who provide support to the organization outside of its
operating workflow. Examples: food service, busing, legal consel, etc.
References
• Drucker Peter (1954), The Practice of Management, Harper &
Brothers, New York
• Hendry, J. (1997). Mintzberg’s theory of organization structure: a
critical assessment and extension. Retrieved from
http://johnhendry.co.uk/wp/wpcontent/uploads/2013/07/Mintzbergs-Theory-of-OrganizationStructure.pdf on June 3, 2015.
• Herbert S. (1955), A Behavioral Model of Rational Choice,
Quarterly Journal of Economics, vol. 69, 99–188
• Mintzberg, H. (1980). Structure in 5’s: A synthesis of the
research on organization design. Management Science, 26(3),
322-341. Available at:
https://www.ics.uci.edu/~corps/phaseii/Mintzberg-StructureIn5sMgmtSci.pdf
• Newell, A., and Simon, H. A. (1972). Human problem solving
Englewood Cliffs, Prentice-Hall, New Jersey.