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This course is about …

Management Science Class 2: Decision Analysis 1 MBA, Term 1, 2003/2004 Dr. Raf Jans Dr. Moritz Fleischmann office F2-53 office F1-38 phone 4082774 phone 4082277 e-mail rjans@fbk.eur.nl e-mail MFleischmann@fbk.eur.nl Rotterdam School of Management. This course is about ….

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This course is about …

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  1. Management ScienceClass 2: Decision Analysis 1 MBA, Term 1, 2003/2004Dr. Raf Jans Dr. Moritz Fleischmannoffice F2-53 office F1-38phone 4082774 phone 4082277 e-mail rjans@fbk.eur.nl e-mail MFleischmann@fbk.eur.nl Rotterdam School of Management

  2. This course is about … • Decision making in a structured way using quantitative modeling techniques • Excel is among the most powerful and versatile quantitative tools available to managers • Why are decisions hard? • What is a good decision? • Topic of today: Decision (Tree) Analysis

  3. Case discussion: the rules of the game • Prepare • Rearrange facts and interpret them • Come to the class with a point of view • Participate • Introduction of the problem and context • Balance between the focus and flow • Balance between overcontrol and chaos • Decision must be defended • Different views are valuable • Adapt • Listen to other arguments • Learn from each other

  4. Introducing the case • Freemark Abbey is located in the Napa valley, California. • Produces about 38.000 cases of premium wine • 1000 cases of Riesling wine • Dry (20% sugar) • Sweet (25% sugar) • Botrytis (up to 35% sugar) • Analyse Jaeger’s decision problem • How would you solve the problem? • What decision do you recommend?

  5. Objectives • Provide an introduction into the concepts and methodologies of Decision Analysis. • Develop analytical skills to structure and solve problems using decision trees and analyse the solutions. • Develop practical skills in solving these problems with decision support tools (Excel and Precision Tree). • Give an insight in the application of Decision Analysis to business problems.

  6. Decision Analysis • Framework and methodology for rational decision making under uncertainty • Structure overall problem as a sequence of decisions and events • Identify • Alternatives / options • Objective • Uncertainties : events and probabilities • Consequences • Sequence

  7. Decision Trees • Graphical tool for structuring and analyzing decision making under uncertainty • Describe decision problem by tree-like structurewith two types of nodes: end node event node decision node probability value value

  8. Decision Trees decision node TRUE / FALSE Decision 1 payoff 1 Name decision Value of optimal decision TRUE / FALSE Decision 2 payoff 2

  9. Decision Trees event node Probability 1 Event 1 payoff 1 Name uncertainty Expected value Probability 2 Event 2 payoff 2

  10. Decision Trees End node Chance of occurrence in optimal solution Cumulative payoff of path

  11. Decision Trees (contd.) “Roll-back” or “Fold-back” the tree to select decisions and evaluate the overall project: • evaluate each of the tree’s “leaves” as the sum of the values along the path leading to it • evaluate each event node as the expected value across all possible outcomes • evaluate each decision node by picking the best-valued alternative

  12. Limitations • Applicable for a moderate number of decisions and events • Otherwise the size of the tree ‘explodes’, making it cumbersome to handle and hard to capture • Specific techniques available for larger problem instances (dynamic programming, branch-and-bound,…)

  13. Applications • Product development • Power trading in electricity markets • Portfolio management (pharmaceuticals) • Location decisions (nuclear plant, airport,…) • Investment projects • Medical diagnosis • Oil exploration (drill or not?) • Marketing (new product introduction) • …

  14. The Value of Decision Analysis at Eastman Kodak Company Because of the one-time nature of typical decision-analysis projects, organizations often have difficulty identifying and documenting their value. Based on Eastman Kodak Company ’s records for 1990 to 1999, we estimated that decision analysis contributed around a billion dollars to the organization over this time. The data also reflect the many roles decision analysis can play. Aside from its monetary benefits, it promotes careful thinking about strategies and alternatives, improved understanding and appreciation of risk, and use of systematic decision-making principles. • Interfaces, Vol. 31 (5), Sep-Oct 2001, 74-92

  15. How Bayer Makes Decisions to Develop New Drugs Drug development is time consuming, resource intensive, risky, and heavily regulated. To ensure that it makes the best drug-development decisions, Bayer Pharmaceuticals (Pharma) uses a structured process based on the principles of decision analysis to evaluate the technical feasibility and market potential of its new drugs. In July 1999, the biological products leadership committee composed of the senior managers within Bayer Biological Products (BP), a business unit of Pharma, made its newly formed strategic-planning department responsible for the commercial evaluation of a new blood-clot-busting drug. … Pharma senior managers considered our recommendations relevant to their decision making. The project also institutionalized decision analysis at the business-unit level. • Interfaces, Vol. 32 (6), Nov-Dec 2002, 77-90

  16. Management and Application of Decision and Risk Analysis in Du Pont Decision and risk analysis (D&RA) enables Du Pont's business teams to develop creative strategy alternatives, evaluate them rigorously, select those with the greatest expected shareholder value, and design implementation plans the businesses can enthusiastically support. Du Pont organized internal and external resources to develop its D&RA capability and incorporated D&RA in several ongoing business processes. One Du Pont business utilized D&RA techniques to develop a business strategy that enhances value by $175 million.  • Interfaces, Vol. 32 (6), Nov-Dec 2002, 77-90

  17. Software • Student Version of DecisionTools Suite • PrecisionTree (decision analysis) • @Risk (simulation) • Install the software on your laptop withthe CD-rom: • Folder: Palisade DecisionTools • Setup.exe • Valid initially for 30 days • Get authorization code (via the web) to obtaina 1 year license

  18. Implementation in PrecisionTree • Demonstrate the implementation of the Freemark Abbey case using PrecisionTree • Advantages of PrecisionTree • Intuitive and easy to learn • Fully integrated within a spreadsheet model • Generate customized reports and graphs (risk profiles and sensitivity analysis)

  19. Building the tree

  20. Building the tree

  21. Building the tree

  22. Building the tree

  23. Building the tree

  24. Building the tree

  25. Building the tree

  26. Building the tree

  27. Building the tree

  28. Building the tree

  29. Risk Profiles • Each decision/strategy is linked to a set of potential results with associated probabilities => risk profile • Expected value alone provides limited information=> What if alternative decision leads to slightly lower expected payoff but at a much lower risk? • Expected value criterion assumes risk neutrality • Risk is associated with a decision/strategy,not with a problem

  30. Risk Profiles Freemark Abbey

  31. Summary – Decision Tree Methodology • Structure the problem as a sequence of decisions and events • Make sure overall sequence is correct • Associate probabilities with random outcomes • Associate payoffs with decisions and with random outcomes • Roll-back the tree to select decisions and evaluate the outcome • Events: compute expected value • Decisions: pick best option

  32. Summary – Decision Tree Methodology • Analyze risk profiles of different decisions • Look beyond expected values • Perform sensitivity analysis • When do decisions change? • Break-even analysis

  33. Key Insights • Uncertainty does not inhibit rational decision making • Structure the problem you want to analyze • Think carefully about: • Your objectives • Your options • Uncertainties: events and likelihoods • Consequences: costs or payoffs • Sequence • Decision trees provide intuitive tool for sequential decision making under uncertainty

  34. Key Insights • Each decision corresponds with a specific risk profile • Make a clear distinction between the quality of a decision and the quality of its outcome • Analyze impact of parameter changes on decisions and outcomes • Identify key drivers of a decision • DA is a way of communicating your reasoning and analysis in a structured way

  35. Outlook • Class 4: Freemark Abbey revisited,advanced topics in decision analysis • Preparation: • Browse through Chapter 10 ‘Decision making under uncertainty’ of Winston & Albright • Think about the following questions related to the Freemark Abbey case: • Should Jaeger buy the Botrytis spores? • Should Jaeger rent the Super Doppler?

  36. Information on the web • Decision Analysis Society homepage: http://faculty.fuqua.duke.edu/daweb • Software companies: • Palisade (http://www.palisade.com) • Consultancy companies: • Strategic Decisions Group (http://www.sdg.com) • Decision Strategies (http://decisionstrategies.com) • ...

  37. If you want to know more … • Application examples: • Various applications: Interfaces http://www.interfaces.smeal.psu.edu/issues/special.php • Volume 22, nr. 6, Nov-Dec 1992 • Volume 29, nr. 6, Nov-Dec 1999 • Medical decision making: (Interfaces, Vol.28, Nr.4)http://www.interfaces.smeal.psu.edu/issues/regular.php?article_id=v28n4a8 • Managing hydropower in Brazil: (OR/MS Today, April 2000) http://lionhrtpub.com/orms/orms-4-00/escudero.html • Software Survey: (OR/MS Today, June 2002) http://lionhrtpub.com/orms/surveys/das/das.html

  38. The end…

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