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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
Soc
ial
Cho
ice—
Ove
rvie
w
Zer
o S
um G
ames
Mor
e E
qual
Tha
nO
ther
s
Jugg
ling
Mac
hine
s
Juic
y P
robl
ems
Man
agem
ent
Sci
ence
—O
verv
iew
Pri
sone
r's D
ilem
ma
Str
eet
Sm
arts
The
Im
poss
ible
Dre
am
Tra
ins,
Pla
nes
and
Cri
tical
Pat
hs
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