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Statistics 802 Quantitative Methods Spring 2008

Statistics 802 Quantitative Methods Spring 2008. 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 802 Quantitative Methods Spring 2008

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  1. Statistics 802 Quantitative Methods Spring 2008 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 The Ombudsman: Reaping Benefits from Management Research: Lessons from the forecasting principles project. Forecasting Software in Practice: Use, Satisfaction, and Performance Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting Contract Optimization at Texas Children's Hospital Using Organizational Control Mechanisms to Enhance Procurement Efficiency: How GlaxoSmithKline Improved the Effectiveness of E-Procurement Optimization of the Production Planning and Trade of Lily Flowers at Jan de Wit Company Improving Volunteer Scheduling for Edmonton Folk Festival Optimizing Highway Transportation at United States Postal Service Staffing a Centralized Appointment Scheduling Department in Lourdes Hospital Building Marketing Models that Make Money An Analysis of the Applications of Neural Networks in Finance Improving Customer Service Operations at Amazon.com Dell Uses a New Production-Scheduling Algorithm to Accommodate Increased Product Variety A Novel Problem for a Vintage Technique: Using Mixed-Integer Programming to Match Wineries and Distributors A Marketing-Decision-Support Model for Evaluating and Selecting Concepts for New Products Developing a Customized Decision-Support System for Brand Managers Improve Their Use of Management Judgment in Forecasting

  5. Companies in Interfaces presentations How Bayer Makes Decisions to Develop New Drugs Improving Supply-Chain-Reconfiguration Decisions at IBM Ranking US Army Generals of the 20th Century: A Group Decision-Making Application of the Analytic Hierarchy Process PLATO Helps Athens Win Gold: Olympic Games Knowledge Modeling for Organizational Change and Resource Management Research and Development Project Valuation and Licensing Negotiations at Phytopharm, PLC Pricing Analysis for Merrill Lynch Integrated Choice A Multimethod Approach for Creating New Business Models: The General Motors OnStar Project Chrysler Leverages Its Suppliers' Improvement Suggestions Improving Car Body Production at PSA Peugeot Citroën Managing Credit Lines and Prices for Bank One Credit Cards Applying Quantitative Marketing Techniques to the Internet Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines Nestlé Improves Its Financial Reporting with Management Science Subject: Pricing for Environmental Compliance in the Auto Industry Achieving Breakthrough Service Delivery through Dynamic Asset Deployment Strategies The Kellogg Company Optimizes Production, Inventory, and Distribution Responding to Emergencies: Lessons Learned and the Need for Analysis Development of a Codeshare Flight-Profitability System at Delta Airlines Travelocity Becomes a Travel Retailer

  6. 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)

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

  8. 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) • Capital budgeting - integer program

  9. 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

  10. 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

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

  12. Bonus Models - Sports • Baseball • Assignment of pitchers - linear programming • 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

  13. ModelsIn Some Cases There Is One Specific Goal • Linear programming • Transportation • Assignment • Integer programming

  14. ModelsIn Some Cases There Is One Specific Goal • Networks • Spanning trees • Shortest path • Maximal flow • Traveling salesperson problem • Chinese postman problem • Analytic Hierarchy Process (AHP)

  15. 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)

  16. 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 • AHP • Zero or constant sum games • Flip a coin!!! –

  17. 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

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

  19. Probabilistic vs. Deterministic Models • Other models are completely deterministic • Linear programming • Transportation • Assignment • Integer programming • Networks • AHP

  20. 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

  21. 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

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

  23. 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)

  24. Data Table With a Decision Tree

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

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

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

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

  29. Computer Skills • Microsoft office • Word • Excel • PowerPoint • Blackboard • Listserv • Software • Download • Installation

  30. Less important computer skills (but skills nonetheless) • QM (POM-QM) for Windows • Will be used in MSOM 5806 – Operations Mgt in Fall • Excel OM • Available for use in MSOM 5806

  31. SURVEY/EVALUATION RESULTSCLASS OF 2009

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

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

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

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

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

  37. Group Take home exam • Workload • Too much time – 2/2/6 • Just right – 24/16/17 • Too little time – 3/0/0 • Value • High – 22/16/21 • Medium – 7/3/2 • Low – 0/0/0 • Conclusion: Next year’s is already posted!

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

  39. Guest Lecture • Repeat next year – 18/13/13 • Do not repeat – 9/6/9 • Conclusion: Continue

  40. Overall Course Workload • Compared to Econ, Elective • Above average – 13/7/15 • Average – 16/11/8 • Below average – 0/0/0 • Compared to Stat 5800 • Higher – 13/3/6 • Same – 14/14/16 • Lower – 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!

  43. Student Grade Sheet

  44. Statistics 5802Spring 2008 The End

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