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