Transcript Spreadsheet Modeling & Decision Analysis:

Spreadsheet Modeling
& Decision Analysis
A Practical Introduction to
Management Science
5th edition
Cliff T. Ragsdale
Chapter 1
Introduction to Modeling
& Problem Solving
Introduction
 We face numerous decisions in life
and professional settings.
 We can use computers to analyze the
potential outcomes of decision
alternatives.
 Spreadsheets are often the tool of
choice for today’s problem-solvers.
What is Operations Research?
 A field of study that uses computers,
statistics, and mathematics to solve
problems in a variety of settings.
 Also known as:
– Management Science
– Decision science
Home Runs
in Operations Research
 Motorola
– Procurement of goods and services
account for 50% of its costs
– Developed an Internet-based auction
system for negotiations with suppliers
– The system optimized multi-product, multivendor contract awards
– Benefits:
$600 million in savings
Home Runs
in Operations Research
 Waste Management
– Leading waste collection company in North
America
– 26,000 vehicles service 20 million residential &
2 million commercial customers
– Developed vehicle routing optimization system
– Benefits:
Eliminated 1,000 routes
Annual savings of $44 million
Home Runs
in Operations Research
 Hong Kong International Terminals
– Busiest container terminal in the world
– 122 yard cranes serve 125 ships per week
– Thousands of trucks move containers in &
out of storage yard
– Used OR to optimize operational decisions
involving trucks, cranes & storage locations
– Benefits:
35% reduction in container handling costs
50% increase in throughput
30% improvement in vessel turnaround time
Home Runs in
Operations Research
 John Deere Company
– 2500 dealers sell lawn equipment &
tractors with support of 5 warehouses
– Each dealer stocks 100 products, creating
250,000 product-stocking locations
– Demand is highly seasonal and erratic
– Developed inventory system to optimize
stocking levels over a 26-week horizon
– Benefits:
 $1 billion in reduced inventory
 Improved customer-service levels
What is a “Computer Model”?
 A set of mathematical relationships and
logical assumptions implemented in a
computer as an abstract representation of
a real-world object of phenomenon.
 Spreadsheets provide the most convenient
way for business people to build computer
models.
The Modeling Approach
to Problem Solving
 Everyone uses models to make
decisions.
 Types of models:
– Mental (arranging furniture)
– Visual (blueprints, road maps)
– Physical/Scale (aerodynamics, buildings)
– Mathematical (what we’ll be studying)
Characteristics of Models
 Models are usually simplified versions of
the things they represent
 A valid model accurately represents the
relevant characteristics of the object or
decision being studied
Benefits of Modeling
 Economy - It is often less costly to
analyze decision problems using
models.
 Timeliness - Models often deliver
needed information more quickly than
their real-world counterparts.
 Feasibility - Models can be used to do
things that would be impossible.
 Models give us insight & understanding
that improves decision making.
Example of a Mathematical Model
Profit = Revenue - Expenses
or
Profit = f(Revenue, Expenses)
or
Y = f(X1, X2)
A Generic Mathematical Model
Y = f(X1, X2, …, Xn)
Where:
Y = dependent (response) variable
(aka bottom-line performance measure)
Xi = independent (explanatory) variables
(inputs having an impact on Y)
f(.) = function defining the relationship between the Xi & Y
Mathematical Models & Spreadsheets
 Most spreadsheet models are very similar
to our generic mathematical model:
Y = f(X1, X2, …, Xn)
 Most spreadsheets have input cells
(representing Xi) to which mathematical
functions ( f(.)) are applied to compute a
bottom-line performance measure (or Y).
Categories of Mathematical Models
Model
Category
Prescriptive
Form of f(.)
Independent
Variables
OR/MS
Techniques
known,
well-defined
known or under
decision maker’s
control
LP, Networks, IP,
CPM, EOQ, NLP,
GP, MOLP
Predictive
unknown,
ill-defined
known or under
decision maker’s
control
Regression Analysis,
Time Series Analysis,
Discriminant Analysis
Descriptive
known,
well-defined
unknown or
uncertain
Simulation, PERT,
Queueing,
Inventory Models
The Problem Solving Process
Identify
Problem
Formulate &
Implement
Model
Analyze
Model
unsatisfactory
results
Test
Results
Implement
Solution
The Psychology of Decision Making
 Models can be used for technical
aspects of decision problems.
 Other aspects cannot be modeled
easily, requiring intuition and judgment.
 Caution: Human judgment and intuition
is not always rational!
Anchoring Effects
 Arise when trivial factors influence initial
thinking about a problem.
 Decision-makers usually under-adjust
from their initial “anchor”.
 Example:
– What is 1x2x3x4x5x6x7x8 ?
Median answer 512
– What is 8x7x6x5x4x3x2x1 ?
Median answer 2,250
– 8! = 40,320
Framing Effects
 Refers to how decision-makers view
alternatives in a problem, often from a
win-loss perspective.
 The way a problem is framed often
influences choices in irrational ways…
 Suppose you’ve been given $1000 and
must choose between:
– A. Receive $500 more immediately
– B. Flip a coin and receive $1000 more if heads
occurs or $0 more if tails occurs
Framing Effects (Example)
 Now suppose you’ve been given $2000
and must choose between:
– A. Give back $500 immediately
– B. Flip a coin and give back $0 if heads occurs
or give back $1000 if tails occurs
A Decision Tree for Both Examples
Payoffs
$1,500
Alternative A
Initial state
Heads (50%)
Alternative B
(Flip coin)
Tails (50%)
$2,000
$1,000
Good Decisions vs. Good Outcomes
 Good decisions do not always lead to good
outcomes...
 A structured, modeling approach to
decision making helps us make good
decisions, but can’t guarantee good
outcomes.
End of Chapter 1