MGS 351 Exam 2 Review Chapter 4 Flashcards
Four Phases of Decision Making
(1) Intelligence Phase -What is the Problem “Find what to fix” “Stop and Think”
(2) Design Phase- Create the solution “Find fix”
(3) Choice Phase- “Pick a Fix”
(4) Implementation Phase- “Apply the fix”
Structured Decision
Either Right or Wrong answers
-Entry employees do this
Unstructured Decision
Infinite answers with better answers available
-Higher employees
Semi-Structured Decision
Some right answers, Some wrong answers
Retail Example: There is a return policy, you need to have a receipt to return–> Customer is trying to return something but does not have the receipt —> The employee talks to the manager—> The manager decides to accept the return but only for store credit.
Recurring Decision
Regular timed basis decisions
-Reports in advanced are helpful
Ad-Hoc
Random “out of the blue” decisions
-Most of the decisions are from this
Decision Support System (DSS)
- **Highly “flexible and interactive” IT system that is designed to “support unstructured and semi-structured decision-making”
- Expertise and judgment are needed to make a decision- The answer is not just given
- Different level management have different info/decision making needs
Components of Decision support Systems (DSS)
- User
- Database
- TPS - Transaction processing system (online)
- External data (e.g. financial reports)
- Software and models
- OLAP and data mining
Model Management
Consists both the DSS model and the DSS model management system
-How are the models stored and used?
Data management
Performs the function of storing and maintaining the info that you want the DSS to use
-Store data
User Interface Management
Allows you to communicate with the DSS
-For users
Sensitivity Analysis DSS
The study of the effect that changes in one or more parts of a model have on other parts of models
-Ripple effect
What -If- Analysis
Checks the impact of a change in the assumptions or other input data on the proposed solution
- Monitoring GPA
- Changing values to see your overall GPA
- Spreadsheet models
- Excell
Goal-Seeking analysis
Find the value of the inputs necessary to achieve a desired level of output
-Start with what you want to see - The ideal solutions
-All possible combinations to get you to the goal
giving you combinations of grades to get you a specific GPA
Collaboration Systems=Group decision Support System
Interactive computer-based system that facilitates the “solution of semi-structured and unstructured problems by groups of decision makers”
- Tools it includes: Electronic questionnaires, Electronic brainstorming tools, idea organizers, anonymous voting, etc…
- Mainly used for entities in differing geographic locations (electronic communication)
Benefits of Collaboration systems (GDS)
- improved preplanning
- Increased participation
- Open, collaborative meeting atmosphere
- “Criticism-free idea generation”
- Evaluation objectivity
- Idea organization and evaluation
- Setting priorities and making decisions
- Documentation of meetings
- Access to external information
Geographic Information Systems (GIS)
Computer system that can analyze and display data using digitized maps. Enables display and analysis of spatial information
Example: Location analysis (opening Mighty taco), Law enforcement (track crimes), identifying efficient delivery routes, etc…
Artificial Intelligence system (AIS)
Branch of Computer science that deals with ways of representing knowledge, using symbols rather than numbers, and heuristics, or rules of thumb, rather than algorithms for processing information
-“If a machine does or exhibits something that we would consider intelligent”—–> AIS goes about it a different way
Objectives of AIS
- Make machines smarter
- Understand what intelligence is
- Make machines more useful
- —–Difficult to build——
Expert Systems (Commercial AIS)
AIS gathers information to solve very specific problems.
These systems utilize expert knowledge and can replace an expert in the decision making process
-Developed to retain the information and to use it when ever it is needed — not to replace the expert
-Good for diagnostic (Whats wrong?) and prescriptive (what to do?) problems
-A knowledge engineer works with an expert to map out the thought process and mimic it
-Deals with very specific problems
-How do you code in a gut feeling
Example: “What whale did I see”
Focuses on the problem– Replaces the expert because he is no longer needed
Components of Expert Systems
- Knowledge base (Database)
- Knowledge acquisition
- Inference engine (Electronic brain- applies logic to data and works with knowledge base)
- User interface
- Explanation module
