Decision Making & Decision Support

1. Decision Making & Decision Support MIS 320 Kraig Pencil Summer 2013

2. Game Plan • Decisions and IS support • Decision making process • Decision support systems (DSS) • Expert Systems

3. A. Decisions and IS Support • Business people encounter many types of decisions  Follow a decision making process  Variety of information systems to support decision making From Haag, et al, MIS for the Information Age, 3rd Edition, 2002.

4. This gentleman won a Nobel Prize! B. Decision Making Process 1. Simon’s decision making model • Simple, yet enduring • Decision process modeled as a “flow” of events • Can proceed in linear or iterative fashion 2. Information systems can support each phase of process

5. Business Scenario • Scenario: Orcas Kayaks Inc. is a company that makes sea kayaks. A new Operations Manager has been hired to promote smooth and efficient manufacturing operations. • Note: The previous Ops Mgr got fired … • Frequent problems with factory equipment • Much factory downtime • Poor decision making • Desired: • Better decision making process • Support for mgmt decision making

6. B. Decision Making Process (cont.) 3a. Intelligence phase • Gather data that may be used for “intelligence” purposes • Does there seem to be a problem(s) or opportunity(s)? • Define the problem or opportunity • “Operations Mgr” scenario: • Gather data • Review of production log reveals significant equipment downtime • Problem: Poor maintenance? (Or could it be something else?) • Can IS help? Which type(s) of IS could be useful for this phase?

7. B. Decision Making Process (cont.) 3b. Design phase • Identify key variables • Create model to aid decision making • Validate model • Establish criteria to be used to make a choice • Identify alternative solutions

8. B. Decision Making Process (cont.) 3b. Design phase “Operations Mgt” scenario: • Variable: e.g., • current maintenance schedule, • age of equipment, • cost of maintenance, • cost of lost productivity ... • Model(s): e.g., • statistical regression, • cost-benefit forecast model • Establish criteria: e.g., $ total 5-year cost • Alternatives: e.g., • repair as needed • purchase new equipment • change maintenance schedule • Can IS help? How?

9. Example: Statistical Model – Factory Equipment Maintenance Cost vs. Age Should we be concerned about model? Costs The model sounds good Age

10. B. Decision Making Process (cont.) 3c. Choice phase • Evaluate potential solutions using model(s) developed earlier • e.g., “What if” analysis, Sensitivity analysis • Use criteria to choose the preferred solution • “Operations Mgr” scenario: • Tasks: See above • Make it easy to explore different scenarios of interest • Convey useful information • Can IS help? How?

11. B. Decision Making Process (cont.) 3d. Implementation phase • Implement the decision • Monitor • Make adjustments • “Operations Mgr” scenario: • Tasks: See above • How could an IS provide support for the above?

12. C. Decision Support Systems • Used for decisions that are • Complex, “Messy” • Non routine, Non structured • Include models (financial, prediction, etc.) • e.g., Allow decision maker to manipulate inputs, Support “what if” analysis • Particularly helpful for ______ and/or _______ phases of decision process • Example: “Cow culling” DSS to support cattle management: http://ag.arizona.edu/AREC/cull/culling.html design choice

13. DSS for Cattle Management DSS results in this case are displayed using a graphical output Flexible: can evaluate multiple cases or scenarios. Does the DSS “make” the decision??? No, the user may need some expertise to interpret the results or to consider other factors.

14. C. Decision Support Systems (cont.) 4. Three primary components of DSS include (see figure) • User interface • Model management • Data management

15. D. DSS Components

16. C. Decision Support Systems (cont.) 5. Excel-based DSS Example: (next slide)Break-even analysis for a manufacturing business • Decision: What sales price will lead to “breakeven” after 1,000 units sold? • DSS components and features • Interface • Graphical output • Advanced Excel feature: User input form • Model management • Break-even model • Data management • Assumption data table (very simple) • Easy access to the workbook throughthe WWW

17. Break-Even Analysis Model

18. D. Introduction to Expert Systems (ES) • Expert systems • Replicate the thought processes of human “experts” • Follow a structured set of rules • “Knowledge-based” systems • Whale watcher demo: http://www.aiinc.ca/demos/whale.shtml • Other ES examples (from exsys.com) http://www.exsys.com/demomain.html • The Cs: Create and Convey Image: www.tubecad.com/2006/10/05/Genius.png

20. D. Introduction to Expert Systems (ES) Expert Systems tend to use branching logic, like this one. bare Figure: http://www.generation5.org/content/2004/bdt-implementation.asp

22. D. Introduction to Expert Systems (ES) • Knowledge Base – as Expert System • Use indexes and search technology (key word searches) to catalog and retrieve relevant information recorded by “experts”.

23. D. Introduction to Expert Systems (cont.) 2. How is an ES different from a DSS? • DSS • Allows decision maker a platform for exploring and evaluating the options • ES • Delivers advice based on answers to a set of questions • Where does the “knowledge” reside for DSS? For ES? 3. Business examples of ES? • ? ? ? 4. Would it be easier to develop a set of “expert rules” for an ES for …a) a narrow, well-defined application?b) a wide-ranging, poorly-defined application? • What are the implications for ES applications?