BA 341 14th Edition Lecture 14 Outline of Last Lecture I. Break- Even AnalysisII. Example #1III. Example #2IV. Multiproduct Case Example #1V. Multiproduct Case Example #2Outline of Current Lecture VI. DecisionsVII. Decision TreesVIII. Decision making under uncertaintyIX. Decision Making under RiskCurrent LectureDecision Making ToolsWhy are Decisions so hard?- The problems involved are complex- It often involves uncertainty and risks- May involve multiple, conflicting objectives (difficult to convince other parties)- May involve multiple stakeholders with different views and preferences- Different perspectives may lead to different conclusionsWhat constitutes a good decision?- Structure the problemo Gather all the data and identify stakeholderso Understand problem objectives and key factorso Identify available resources, costs, constraints- Analyze the problemo Develop measures of performanceo Develop a model that connects resources and actions to objectivesThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.o Carefully examine each alternative based on logico Implement and monitor solution successDecision Trees- Popular tool for decision analysis- Very flexible, applies to a variety of decisions- A graphical display of the decision process, indicating decision alternatives, states of nature and their probabilities, and outcomes/ payoffs- Symbols commonly usedo square - decision node from which different alternatives can be selected (decision point)o circle - state-of-nature (chance or uncertain) node An Example:A company seeks to introduce a new product family. This requires the construction of either a large or a small manufacturing plant. The market for the product can be either favorable or unfavorable. Decision making under uncertainty - Assume a decision environment characterized by complete uncertainty (cannot assess probabilities of uncertain outcomes, e.g., introduction of a new, innovative product)- Some possible decision methods:o MaxiMax (Optimistic) – maximizes the maximum outcome for every alternativeo MaxiMin (Pessimistic) – maximizes the minimum outcome for every alternative o Equally likely – assumes that each state of nature is equally likely to occur; maximizes the average outcome for every alternativeFrom the earlier example:Let’s construct the payoff table associated with this decision:How do we apply the MaxiMax method?Choose the maximum number in each column- For Maximax approach: $200,000- For Maximin approach: 0- For Equally Likely approach: $40,000Decision Making under Risk- Assume that information on the probabilities of uncertain outcomes is available - In this case, decisional alternatives can be assessed based on the expected monetary value (EMV):o EMV (alternative i) = (payoff for 1st state of nature) x (probability 1st state of nature) + …+ (payoff for last state of nature) x (probability last state of nature)- Alternative with max (EMV) is bestBack to the example:- Assume the following payoff table:- What are the EMVs of the 2 alternatives?o A1: (200000 x .65) + (-180000 x .35) = 67,000 o A2: (100000x .65) + (-20000 x .35) = 58,000- What is the best option?o The best option is A1Putting it all together….- The corresponding decision tree
View Full Document
Unlocking...