Statistics 802 Quantitative Methods

Spring 2007

Final Thoughts

Goal (Syllabus)

To provide students with a description of the advanced quantitative models which are routinely used for managerial decision making

Goal (Syllabus)

To provide students with examples of the application of these models• Interfaces • Forecasting Project• Videos (web problem)• AHP Guest Lecture

Companies in Interfaces presentationsPartnerships in Training

People Skills: Change Management Tools-Leading Teams

The Fifth Column: Homage to Doc Savage 3, or on the Optimum Siting of an Airstrip in the Jungle, or I

Applying Quantitative Marketing Techniques to the Internet

Responding to Emergencies: Lessons Learned the Need for Analysis

Citibank Models Credit Risk on Hybrid Mortgage Loans in Taiwan

Contract Optimization at Texas Children's Hospital

Applying Operations Research Techniques to Financial Markets

Improving customer service at Amazon.com

Management Science and Productivity Improvement in Irish Milk Cooperatives

Phasing of Income-Producing Real Estate by Peiser and Andrus

Ranking Sports Teams : A Customizable Quadratic Assignment Approach

Supply Chain Collaboration Through Shared Capacity Models

Analyzing and Development Strategy for Apimoxin

Investment Analysis and Budget Allocation at Caltholic Relief Services.

The Operation was a Success but the Patient Died:Aider Priorities Influence decision Aid Usefulness

How Effective Is Security Screening of Airline Passengers?

The United States and Russia Evaluate Plutonium Disposition Options with Multiattribute Utility Theory

Equipping Students to Reduce Lead Times: The Role of Queuing-Theory-Based Modeling

Diagnosis Related Groups: Understanding Hospital Performance.

Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines

CODELCO, Chile Programs Its Copper-Smelting Operations

Achieving Breakthrough Service Delivery Through Dynamic Asset Deployment Strategies

Achieving Success in Large Projects: Implications from a Study of ERP Implementations.

Changing the Game in Strategic Sourcing at Procter & Gamble: Expressive Competition Enabled by Optimization

Early Detection of High-Risk Claims at the Workers' Compensation Board of British Columbia

Samples of Models (From Lectures, Text, Homework 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)

Samples of Models (From Lectures, Text, Homework and Exams) Advertising

• Media selection (linear programming)• Competitive

• Game/Market Share/$

• Game/Price Guarantees – Guarantees guarantee HIGH prices!

Samples of Models Price Guarantees

$1000, no guarantee

$1000 guarantee

$800 no guarantee

$800 guarantee

$1000, no guarantee

$1000 guarantee

$800 no guarantee

$800 guarantee

Dealer B

Dea

ler

A

Samples of Models Price Guarantees

$1000, no guarantee

$1000 guarantee

$800 no guarantee

$800 guarantee

$1000, no guarantee $500, $500 $0, $1000 0, $800 0, $800

$1000 guarantee $1000, 0 $500, $500 $780, 0 $780, 0

$800 no guarantee $800, 0 0, $780 $400, $400 0, $800

$800 guarantee $800, 0 0, $780 $800, 0 $400, $400

Dealer B

De

ale

r A

Samples of Models

Inventory planning• Newsboy problem (single period inventory model)

• Decision table (in notes, we did not get to it)• Simulation (in notes, we did not get to it)

– (we did more on decision trees than I have done in the past)

• Production planning - linear programming Bidding

• Simulation (in notes, we did not get to it)• (we did the in-class simulation exercise)

Capital budgeting - integer program

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

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

Samples of Models

Location• Game theory• Transportation

Agricultural planning• Noncompetitive - linear programming• Competitive - non zero sum game

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

• Desperation - decision analysis - maximax Ice hockey

• Pull the goalie sooner• Desperation - decision analysis - maximax

Basketball • Desperation - decision analysis - maximax

ModelsIn Some Cases There Is One Specific Goal

• Linear programming• Transportation• Assignment

• Integer programming

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)

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)

– Mean absolute deviation (MAD)– Mean squared error (standard error)– Mean absolute percent error (MAPE)

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 – sort of

• Zero or constant sum games• Flip a coin!!! –

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

Probabilistic vs. Deterministic Models Some models include probabilities

• Markov Chains• Decision Analysis

• Decision tables

• Decision trees

• Games• Forecasting

Probabilistic vs. Deterministic Models Other models are completely deterministic

• Linear programming• Transportation

• Assignment

• Integer programming• Networks• AHP

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

A Notion of Fair

Game videos• Splitting a piece of cake

• In two– Statistician– Game theorist

• In more than two

Team work division• Splitting work for projects

ModelsTradeoffs Ease of use vs. flexibility

• 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

ModelsTradeoffs

Model exactness vs. Flexibility• Analytical method vs. Simulation

Development cost/time vs. Exactness• Analytical method vs. Simulation

Model Sensitivity

Forecasting & simulation• Standard error/standard deviation

Linear programming• Dual values/ranging table

Decision tables/decision trees• Data table (letting probabilities vary)

Data Table With a Decision Tree

Solving Backwards

Decision tree Game tree (sequential decisions) Let’s make a deal

Models – Number of Decision Makers One

• Most models More than one

• Games• Let’s make a deal !!

Excel Addins

Solver• Linear & integer programs• Networks (shortest path & maximal flow)• Zero sum games • Decision trees

Crystal ball• Simulation/risk analysis

Excel Tools Data analysis

• Forecasting• Simulation

• Can be used for generating random numbers

Scenarios Data tables

• Simulation• Decision tables• Decision trees

Computer Skills

Microsoft office• Word• Excel• PowerPoint

Blackboard Listserv Software

• Download?• Installation

Less important computer skills (but skills nonetheless) QM (POM-QM) for Windows

• Will be used in MSOM 806 – Operations Mgt in Fall 2006

Excel 802• Available for use in MSOM806

SURVEY/EVALUATION RESULTSCLASS OF 2008

Consistency

Note the consistency between your evaluations and those of the previous 2 classes!!

Survey Results – ForecastingClass of 2008/2007/2006 Workload

• Too much time – 2/1/5• Just right – 22/17/18• Too little time – 2/0/0

Value• High – 16/18/17• Medium – 8/1/6• Low – 0/0/0

Conclusion: keep as a requirement

Interfaces presentations

Workload• Too much time – 2/1/2• Just right – 23/18/20• Too little time – 1/0/1

Value of reading; listening• High – 14;10 /10;6 /7; 6• Medium – 10;10 /7;6 /14; 11• Low – 1;2 /1;1 /2; 1

Interfaces presentations

Interfaces Question 4• Continue as is – 2/2/17• Discontinue – 20/10/1• Power point – 1//10

Conclusion: drop the Interfaces assignment• Bad news – will lose flavor of applications and large $ savings• Good news – more time for lecture in class• Comment – I don’t understand answer to this vis-à-vis previous

answers

LP interpretation - self

Workload• Too much time – 0/0/2• Just right – 26/18/20• Too little time –0/0/0

Value • High – 17/13/14• Medium – 6/6/8• Low – 3/0/0

Conclusion: Keep as a requirement

LP interpretations - team

Workload• Too much time – 1/1/7• Just right – 23/17/16• Too little time – 2/0/0

Value • High – 13/11/12• Medium – 9/5/8• Low – 3/3/3

Conclusion: Keep as a requirement

Decision tree (team)

Workload• Too much time – 4 • Just right – 22• Too little time – 0

Value • High – 15• Medium – 9 • Low – 1

Conclusion: Keep as a requirement• (keep anyway since it will be used in Finance)

Simulation (team) in class Workload

• Too much time – 1• Just right – 8• Too little time – 17

Value • High – 10• Medium – 4 • Low – 12

Conclusion: Keep as a requirement but do it outside of class as originally planned

Note: Simulation will be used in Finance in fall

Group Take home exam

Workload• Too much time – 3/2/6• Just right – 23/16/17• Too little time – 0/0/0

Value • High – 22/16/21• Medium – 3/3/2• Low – 1/0/0

Conclusion: Keep as a requirement

Homework/Exam

Workload• Too much time – 4/2/14• Just right – 17/12/8• Too little time – 5/4/1

Value • High – 15/12/14• Medium – 11/7/7• Low – 0/0/2

Conclusion: Keep as a requirement

Guest Lecture

Repeat next year – 22/13/13 Do not repeat – 3/6/9 Conclusion – repeat next year!

Overall Course Workload Compared to Econ, Elective

• Above average – 11/7/15• Average – 15/11/8• Below average – 0/0/0

Compared to Stat 800• Higher – 14/3/6• Same – 9/14/16• Lower – 3/1/1

Conclusion: Workload is slightly higher than other comparable courses; needs slight reduction which may happen with dropping of Interfaces assignment

Videos – sorted by score – Cl 2008Video Values

3.383.30

3.15 3.13 3.13 3.09 3.083.00

2.902.83

2.502.602.702.802.903.003.103.203.303.403.50

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Video ratings – comparing last 4 years

  2008 2007 2006 2005

Management Science—Overview 3.09 3.24 3.1 3.31

Street Smarts 3.00 3.18 2.74 2.76

Trains, Planes and Critical Paths 2.83 3.29 3 2.76

Juggling Machines 3.13 3 2.72 2.31

Juicy Problems 3.13 3 3.21 2.88

Average 1st five 3.03 3.14 2.95 2.81

Social Choice—Overview 3.38 3.31 2.76 2.88

The Impossible Dream 2.90 3.25 2.57 2.27

More Equal Than Others 3.15 3.27 2.6 2.4

Zero Sum Games 3.30 3.44 3.65 3

Prisoner's Dilemma 3.08 3.67 3.48 3

Average Game Videos 3.16 3.39 3.01 2.71

THE FINAL EXAM & GRADES

Howard, now is the time to return the exams!

Final Exam Statistics

CL (2008)

Base = 120

CL (2007)Base = 120

Mean 81 (68%) 85 (71%)

Median 83 (69%) 89 (74%)

Max 117 (98%) 114 (95%)

Student Grade Sheet

Final exam curved as if base was 100.• E.g., a raw score of 90/120 was treated as a score of

90/100 or 90%

Exam Comments

Teams

Team average - Individual Average

-20.00

-15.00

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

1 3 5 7 9 11 13 15 17 19 21 23 25

Team help possibilities Possible changes in course average due to team Interpretation ?

# students TeamHelped

# students TeamHurt

4 0

3 1

2 2

1 3

0 4

Team help CL 2008 Actual changes in course average due to team

• (adjustments made to hide teams)

Actual# students Team

Helped# students Team

Hurt

1 4 0

0 3 1

3 2 2

1 1 3

1 0 4

Final comments

Topics from this course should appear in future EMBA courses• Managerial Accounting (summer)

• Finance (fall)

• Operations Management (fall/spring)

Models are widely used on their own• Forecasting

• Interfaces articles

Statistics 802Spring 2007

The End