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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). - PowerPoint PPT Presentation
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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)To provide students with examples of the
application of these modelsInterfaces Forecasting ProjectAHP Guest Lecture
Companies in Interfaces presentations
The Ombudsman: Reaping Benefits from Management Research: Lessons from the forecasting principles project.Forecasting Software in Practice: Use, Satisfaction, and PerformanceAgainst Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in ForecastingContract Optimization at Texas Children's HospitalUsing Organizational Control Mechanisms to Enhance Procurement Efficiency: How GlaxoSmithKline Improved the Effectiveness of E-ProcurementOptimization of the Production Planning and Trade of Lily Flowers at Jan de Wit CompanyImproving Volunteer Scheduling for Edmonton Folk FestivalOptimizing Highway Transportation at United States Postal ServiceStaffing a Centralized Appointment Scheduling Department in Lourdes HospitalBuilding Marketing Models that Make MoneyAn Analysis of the Applications of Neural Networks in FinanceImproving Customer Service Operations at Amazon.comDell Uses a New Production-Scheduling Algorithm to Accommodate Increased Product VarietyA Novel Problem for a Vintage Technique: Using Mixed-Integer Programming to Match Wineries and DistributorsA Marketing-Decision-Support Model for Evaluating and Selecting Concepts for New ProductsDeveloping a Customized Decision-Support System for Brand Managers Improve Their Use of Management Judgment in Forecasting
Companies in Interfaces presentationsHow Bayer Makes Decisions to Develop New DrugsImproving Supply-Chain-Reconfiguration Decisions at IBMRanking US Army Generals of the 20th Century: A Group Decision-Making Application of the Analytic Hierarchy ProcessPLATO Helps Athens Win Gold: Olympic Games Knowledge Modeling for Organizational Change and Resource ManagementResearch and Development Project Valuation and Licensing Negotiations at Phytopharm, PLCPricing Analysis for Merrill Lynch Integrated ChoiceA Multimethod Approach for Creating New Business Models: The General Motors OnStar ProjectChrysler Leverages Its Suppliers' Improvement SuggestionsImproving Car Body Production at PSA Peugeot CitroënManaging Credit Lines and Prices for Bank One Credit CardsApplying Quantitative Marketing Techniques to the InternetMerrill Lynch Improves Liquidity Risk Management for Revolving Credit LinesNestlé Improves Its Financial Reporting with Management ScienceSubject: Pricing for Environmental Compliance in the Auto IndustryAchieving Breakthrough Service Delivery through Dynamic Asset Deployment StrategiesThe Kellogg Company Optimizes Production, Inventory, and DistributionResponding to Emergencies: Lessons Learned and the Need for AnalysisDevelopment of a Codeshare Flight-Profitability System at Delta AirlinesTravelocity Becomes a Travel Retailer
Samples of Models (From Lectures, Text, Homework, Greatest Hits and Exams)
Market share Brand loyalty (Markov
chain)Advertising (Game)
Scheduling1 to 1 (Assignment)1 or many to many
Transportation Integer Program (Set
covering)
Samples of Models
AdvertisingMedia selection (linear programming)Competitive
Game/Market Share/$ Game/Price Guarantees – Guarantees
guarantee HIGH prices!
Samples of ModelsInventory planning
Newsboy problem (single period inventory model – greeting cards example) Decision table Simulation
Production planning - linear programmingBidding
Simulation (in notes, we did not get to it)Capital budgeting - integer program
Samples of ModelsEnrollment management/forecasting -
Markov chainPublic services
Mail delivery, street cleaning/plowingSchool bussing – transportation
Finance/accountingCost/volume - simulationPortfolio selection – linear/integer programming
Samples of ModelsProduction
Product mix/resource allocation - linear programming
Blending - linear programmingEmployee scheduling- related problems
Workforce schedulingWorkforce trainingAssignment
HealthDiet problem
Samples of ModelsLocation – game theoryAgricultural planning
Noncompetitive - linear programmingCompetitive - non zero sum game
Bonus Models - SportsBaseball
Assignment of pitchers - linear programmingFootball
Fourth and goal - decision tree Optimal sequential decisions and the content of the fourth-and goal
Desperation - decision analysis - maximaxIce hockey
Pull the goalie soonerDesperation - decision analysis - maximax
Basketball Desperation - decision analysis - maximax
ModelsIn Some Cases There Is One Specific Goal
Linear programmingTransportationAssignment
Integer programming
ModelsIn Some Cases There Is One Specific Goal
NetworksSpanning treesShortest pathMaximal flowTraveling salesperson problemChinese postman problem
Analytic Hierarchy Process (AHP)
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)
Prescriptive Vs. Descriptive ModelsSome models PRESCRIBE what action to
takeLinear programming based
Transportation, assignment, integer programming, goal programming, game theory
Network based Shortest path, maximal flow, minimum spanning tree,
traveling salesperson, Chinese postman
AHPZero or constant sum games
Flip a coin!!! –
Prescriptive Vs. Descriptive ModelsSome models DESCRIBE the consequences
of actions takenDecision analysisForecastingMarkov chainsSimulationNon zero sum games
Matching lowest price leads to high prices ! Competition leads to low prices
Probabilistic vs. Deterministic ModelsSome models include probabilities
Markov ChainsDecision Analysis
Decision tables Decision trees
GamesForecast Ranges
Probabilistic vs. Deterministic ModelsOther models are completely deterministic
Linear programming Transportation Assignment
Integer programmingNetworksAHP
Long RunSome models/measures require steady state
(long run) in order for the results to be usefulGamesDecision analysis
Expected value Expected value of perfect information
ModelsTradeoffsEase 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. solvabilityInteger programming/linear
programming
ModelsTradeoffs
Model Exactness vs. FlexibilityAnalytical method vs. Simulation
Development Cost/Time vs. ExactnessAnalytical method vs. Simulation
Model SensitivityForecasting & Simulation
Standard error/standard deviation
Linear ProgrammingDual values/ranging table
Integer ProgrammingChange values 1 unit at a time
Decision Tables/Decision TreesData table (letting probabilities vary)
Solving BackwardsDecision treeGame tree (sequential
decisions)Let’s make a deal
Models – Number of Decision MakersOne
Most modelsMore than one
GamesLet’s make a deal !!
Excel AddinsSolver
Linear & integer programsNetworks (shortest path & maximal flow)Zero sum games Decision trees
Crystal ballSimulation/risk analysisWill be used in your Fall Finance course
Excel ToolsData analysis
ForecastingSimulation
Can be used for generating random numbers
ScenariosData tables
SimulationDecision tablesDecision trees
Computer SkillsMicrosoft office
WordExcelPowerPoint
Blackboard ListservSoftware
DownloadInstallation
Less important computer skills (but skills nonetheless)QM (POM-QM) for Windows
Will be used in MSOM 5806 – Operations Mgt in Fall
Excel OMAvailable for use in MSOM 5806
SURVEY/EVALUATION RESULTSCLASS OF 2009
Survey Results – ForecastingClass of 2008/Class of 2007/Class of 2006Workload
Too much time – 3/1/5Just right – 25/17/18Too little time – 1/0/0
ValueHigh – 22/18/17Medium – 6/1/6Low – 1/0/0
Conclusion: Maintain project as is.
Interfaces presentationsWorkload
Too much time – 2/1/2Just right – 26/18/20Too little time – 0/0/1
Value of reading; listeningHigh – 12;10/10;6/7; 6Medium – 14;10/7;6/14; 11Low – 3;3/1;1/2; 1
Interfaces optionsDiscontinue – 3/2/17Continue as is– 10/10/1Continue w Power point – 12/10/na
Conclusion: Continue, but consider students using ppt
LP interpretations selfWorkload
Too much time – 1/0/2Just right – 26/18/20Too little time – 2/0/0
Value High – 10/13/14Medium – 10/6/8Low – 0/0/0
Conclusion: Continue as is
LP interpretations teamWorkload
Too much time – 2/1/7Just right – 26/17/16Too little time – 1/0/0
Value High – 10/11/12Medium – 17/5/8Low – 1/3/3
Conclusion: Continue as is
Decision Tree - TeamWorkload
Too much time – 3Just right – 23Too little time – 2
Value High – 14Medium – 12Low – 3
Conclusion: Continue as is
Group Take home examWorkload
Too much time – 2/2/6Just right – 24/16/17Too little time – 3/0/0
Value High – 22/16/21Medium – 7/3/2Low – 0/0/0
Conclusion: Next year’s is already posted!
Homework/ExamWorkload
Too much time – 5/2/14Just right – 18/12/8Too little time – 6/4/1
Value High – 15/12/14Medium – 13/7/7Low – 1/0/2
Conclusion: Continue as is
Guest LectureRepeat next year – 18/13/13Do not repeat – 9/6/9Conclusion: Continue
Overall Course Workload
Compared to Econ, ElectiveAbove average – 13/7/15Average – 16/11/8Below average – 0/0/0
Compared to Stat 5800Higher – 13/3/6Same – 14/14/16Lower – 2/1/1
Conclusion: Workload may be slightly high
THE FINAL EXAM & GRADES
Final ExamHoward, now is the time to return the exams!
base = 120 Pct (Cl 07/06)
Mean 93.78 76% (75%, 71%)
Median 96 78% (79%, 74%)
Max 120 95%
Student Grade Sheet
The End