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Statistics 5802 Quantitative Methods Spring 2009

Statistics 5802 Quantitative Methods Spring 2009. Final Thoughts. Goal (Syllabus). To provide students with a description of the advanced quantitative techniques which are routinely used for managerial decision making. Goal (Syllabus).

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Statistics 5802 Quantitative Methods Spring 2009

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  1. Statistics 5802 Quantitative MethodsSpring 2009 Final Thoughts

  2. Goal (Syllabus) • To provide students with a description of the advanced quantitative techniques which are routinely used for managerial decision making

  3. Goal (Syllabus) • To provide students with examples of the application of these models • Interfaces • Forecasting Project • AHP Guest Lecture

  4. Companies in Interfaces presentations Applying Quantitative Marketing Techniques to the Internet Structuring and sustaining excellence in management science at Merrill Lynch People Skills: The need to change –Problem or Opportunity? A new approach to performance management and goal setting Against Your Better Judgement? How Organizations Can Improve Their Use of Management Judgement in Forecasting Contract Optimization at the Texas Children's Hospital, Warner Robins Air Logistics Center Streamlines. Aircraft Repair and Overhaul NBC-Universal Uses a Novel Qualitative Forecasting Technique to Predict Advertising Demand. Integrating Excel, Access and Visual Basic to Deploy Performance Measurement and Evaluation at the American Red Cross. The “Killer Application” of Revenue Management:  Harrah’s Cherokee Casino & Hotel Optimally Stationing Army Forces Determinants of Success of German Venture Capital Investments Investment Analysis and Budget Allocation at Catholic Relief Services Spreadsheet Model Helps to Assign Medical Residents at the University of Vermont's College of Medicine. Ranking US Army Generals of the 20th Century: A Group Decision-Making Application of the Analytic Hierarchy ProcessDecision analysis; multiple criteria; military; personnel. Designing the Response to an Anthrax Attack Managing Credit Lines and Prices for Bank One Credit Cards Spreadsheet Models for Inventory Target Setting at Procter & Gamble. Analyzing Supply Chains at HP Using Spreadsheet Models Supply Chain Management: Technology, Globalization, and Policy at a Crossroads The Peoples Gas Light and Coke Company Plans Gas Supply

  5. Samples of Models(From Lectures, Text, Homework, Greatest Hits and Exams) • Market share • Brand loyalty (Markov chain) • Advertising (Game) • Scheduling • 1 to 1 (Assignment) • 1 or many to many • Transportation • Integer Program (Set covering)

  6. Samples of Models • Advertising • Media selection (linear programming) • Competitive • Game/Market Share/$ • Game/Price Guarantees – Guarantees guarantee HIGH prices!

  7. Samples of Models • Inventory planning • Newsboy problem (single period inventory model – greeting cards example) • Decision table • Simulation • Production planning - linear programming • Bidding • Simulation (in notes, we did not get to it except for one team) • Capital budgeting - integer program

  8. Samples of Models • Enrollment management/forecasting - Markov chain • Public services • Mail delivery, street cleaning/plowing • School bussing – transportation • Finance/accounting • Cost/volume - simulation • Portfolio selection – linear/integer programming

  9. Samples of Models • Production • Product mix/resource allocation - linear programming • Blending - linear programming • Employee scheduling- related problems • Workforce scheduling • Workforce training • Assignment • Health • Diet problem • Disease Progression

  10. Samples of Models • Location – game theory • Agricultural planning • Noncompetitive - linear programming • Competitive - non zero sum game

  11. Bonus Models - Sports • Baseball • Assignment of pitchers - linear programming • States in a Markov chain • Football • Fourth and goal - decision tree • Optimal sequential decisions and the content of the fourth-and goal • Desperation - decision analysis - maximax • Ice hockey • Pull the goalie sooner • Desperation - decision analysis - maximax • Basketball • Desperation - decision analysis - maximax

  12. ModelsIn Some Cases There Is One Specific Goal (maximize or minimize) • Linear programming • Transportation • Assignment • Integer programming • Networks • Spanning tree • Shortest Path • Maximal Flow • Traveling Salesperson • Chinese Postman Problem

  13. ModelsIn Other Cases There May Be More Than One Specific Goal/Measurement • Decision analysis • Expected (monetary) value • Maximin (conservative, pessimistic) • Maximax (optimistic, desperate) • Maximin regret (conservative, pessimistic) • Forecasting • Error measurement (technique evaluation) • Mad • Mean squared error (standard error) • Mean absolute percent error (MAPE) • Games • Maximin/minimax • Expected Value

  14. ModelsIn Some Cases We are trying to rate or order • Analytic Hierarchy Process (AHP) • Data Envelopment Analysis

  15. Prescriptive Vs. Descriptive Models • Some models PRESCRIBE what action to take • Linear programming based • Transportation, assignment, integer programming, goal programming, game theory • Network based • Shortest path, maximal flow, minimum spanning tree, traveling salesperson, Chinese postman • Zero or constant sum games • Flip a coin!!! –

  16. Prescriptive Vs. Descriptive Models • Some models DESCRIBE the consequences of actions taken • Decision analysis • Forecasting • Markov chains • Simulation • Non zero sum games • Matching lowest price leads to high prices ! • Competition leads to low prices • Ranking • AHP • DEA

  17. Probabilistic vs. Deterministic Models • Some models include probabilities • Markov Chains • Decision Analysis • Decision tables • Decision trees • Games • Forecast Ranges • Simulation

  18. Probabilistic vs. Deterministic Models • Other models are completely deterministic • Linear programming • Transportation • Assignment • Data Envelopment Analysis • Integer programming • Networks • AHP

  19. Long Run • Some models/measures require steady state (long run) in order for the results to be useful • Games • Decision analysis • Expected value • Expected value of perfect information

  20. ModelsTradeoffs • Ease of use vs. flexibility/generality • Transportation (easier) vs. LP (more flexible) • Decision table (easier) vs. Decision tree (more flexible) • QM for windows (easier) vs. Excel (more flexible) • Model correctness vs. solvability • Integer programming/linear programming

  21. ModelsTradeoffs • Model Exactness vs. Flexibility • Analytical method vs. Simulation • Development Cost/Time vs. Exactness • Analytical method vs. Simulation

  22. Model Sensitivity • Forecasting & Simulation • Standard error/standard deviation • Linear Programming • Dual values/ranging table • Integer Programming • Change values 1 unit at a time • Decision Tables/Decision Trees • Data table (letting probabilities vary)

  23. Data Table With a Decision Tree

  24. Solving Backwards • Decision tree • Game tree (sequential decisions) • Let’s make a deal

  25. Models – Number of Decision Makers • One • Most models • More than one • Games • Let’s make a deal !! • AHP – sort of

  26. Excel Addins • Solver • Linear & integer programs • Networks (shortest path & maximal flow) • Zero sum games • Crystal ball • Simulation/risk analysis • Will be used in your Fall Finance course • Excel QM • Decision trees • Many other models

  27. Excel Tools • Data analysis • Forecasting • Simulation • Can be used for generating random numbers • Scenarios • Data tables • Simulation • Decision tables • Decision trees

  28. Computer Skills • Microsoft office • Word • Excel • Blackboard • Discussion Board • Listserv • Software • Download • Installation

  29. Less important computer skills (but skills nonetheless) • QM (POM-QM) for Windows • Will be used in MSOM 5806 – Operations Mgt in Fall • Change menu now • Excel OM • Available for use in MSOM 5806 • (requires new file rather than a menu change)

  30. SURVEY/EVALUATION RESULTSCLASS OF 2009

  31. Survey Results – ForecastingClass of 2009/2008/Class of 2007/Class of 2006 • Workload • Too much time – 2/3/1/5 • Just right – 12/25/17/18 • Too little time – 2/1/0/0 • Value • High – 13/22/18/17 • Medium – 2/6/1/6 • Low – 1/1/0/0 • Conclusion: Maintain project as is.

  32. Interfaces presentations • Workload • Too much time – 1/2/1/2 • Just right – 15/26/18/20 • Too little time – 0/0/0/1 • Value of reading; listening • High – 5;5/12;10/10;6/7; 6 • Medium – 8/6;14;10/7;6/14; 11 • Low – 3;2/3;3/1;1/2; 1 • Interfaces options • Discontinue – 2/3/2/17 • Continue as is– 6/10/10/1 • Continue w Power point – 5/12/10/na Conclusion: Continue, but consider students using ppt

  33. LP interpretations self • Workload • Too much time – 0/1/0/2 • Just right – 16/26/18/20 • Too little time – 0/2/0/0 • Value • High – 11/10/13/14 • Medium – 5/10/6/8 • Low – 0/0/0/0 • Conclusion: Continue as is

  34. LP interpretations team • Workload • Too much time – 3/2/1/7 • Just right – 13/26/17/16 • Too little time – 0/1/0/0 • Value • High – 10/10/11/12 • Medium – 5/17/5/8 • Low – 1/1/3/3 • Conclusion: Continue as is

  35. Decision Tree - Team • Workload • Too much time – 1/3 • Just right – 14/23 • Too little time – 1/2 • Value • High – 10/14 • Medium – 6/12 • Low – 0/3 • Conclusion: Continue as is

  36. Decision Simulation • Workload • Too much time – 0 • Just right – 13 • Too little time – 3 • Value • High – 6 • Medium – 8 • Low – 2 • Conclusion: Your evaluation results above say continue as is but your performance indicates that I need to make it more challenging

  37. Group Take home exam • Workload • Too much time – 2/2/2/6 • Just right – 14/24/16/17 • Too little time – 0/3/0/0 • Value • High – 13/22/16/21 • Medium – 3/7/3/2 • Low – 0/0/0/0 • Conclusion: Continue

  38. Homework/Exam • Workload • Too much time – 6/5/2/14 • Just right – 9/18/12/8 • Too little time – 1/6/4/1 • Value • High – 7/15/12/14 • Medium – 8/13/7/7 • Low – 1/1/0/2 • Conclusion: Continue as is

  39. Guest Lecture • Repeat next year – 10/18/13/13 • Do not repeat – 6/9/6/9 • Conclusion: Continue. Based on comments I will ask Bob to dive into the AHP program at 9:30.

  40. Overall Course Workload • Compared to Econ, Elective • Above average – 10/13/7/15 • Average – 6/16/11/8 • Below average – 0/0/0/0 • Compared to Stat 5800 • Higher – 8/13/3/6 • Same – 7/14/14/16 • Lower – 1/2/1/1 • Conclusion: Workload may be slightly high

  41. THE FINAL EXAM & GRADES

  42. Final Exam • Howard, now is the time to return the exams! • Exam this year had only 4 problems • Note: Overall performance was best I have seen!

  43. Student Grade Sheet

  44. Statistics 5802Spring 2009 The End

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