1. Spreadsheet Modeling
& Decision Analysis
A Practical Introduction to
Management Science
5th
edition
Cliff T. Ragsdale

2. Introduction to Modeling
& Problem Solving
Chapter 1

3. Introduction
 We face numerous decisions in life &
business.
 We can use computers to analyze the
potential outcomes of decision
alternatives.
 Spreadsheets are the tool of choice for
today’s managers.

4. What is Management Science?
 A field of study that uses computers,
statistics, and mathematics to solve
business problems.
 Also known as:
– Operations research
– Decision science

5. Home Runs
in Management Science
 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, multi-
vendor contract awards
– Benefits:
$600 million in savings

6. Home Runs
in Management Science
 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

7. Home Runs
in Management Science
 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 DSS 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

8. Home Runs in
Management Science
 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

9. 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.

10. The Modeling Approach
to Decision Making
 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)

11. 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

12. 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.

13. Example of a Mathematical Model
Profit = Revenue - Expenses
or
Profit = f(Revenue, Expenses)
or
Y = f(X1, X2)

14. A Generic Mathematical Model
Y = f(X1, X2, …, Xn)
Y = dependent variable
(aka bottom-line performance measure)
Xi = independent variables (inputs having an impact on Y)
f(.) = function defining the relationship between the Xi & Y
Where:

15. 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).

16. Categories of Mathematical Models
Prescriptive known, known or under LP, Networks, IP,
well-defined decision maker’s CPM, EOQ, NLP,
control GP, MOLP
Predictive unknown, known or under Regression Analysis,
ill-defined decision maker’s Time Series Analysis,
control Discriminant Analysis
Descriptive known, unknown or Simulation, PERT,
well-defined uncertain Queueing,
Inventory Models
Model Independent OR/MS
Category Form of f(.) Variables Techniques

17. The Problem Solving Process
Identify
Problem
Formulate &
Implement
Model
Analyze
Model
Test
Results
Implement
Solution
unsatisfactory
results

18. The Psychology of Decision Making
 Models can be used for structurable
aspects of decision problems.
 Other aspects cannot be structured
easily, requiring intuition and judgment.
 Caution: Human judgment and intuition
is not always rational!

19. 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 ?
– What is 8x7x6x5x4x3x2x1 ?

20. Framing Effects
 Refers to how decision-makers view a
problem 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

21. 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

22. A Decision Tree for Both Examples
Initial state
$1,500
Heads (50%)
Tails (50%)
$2,000
$1,000
Alternative A
Alternative B
(Flip coin)
Payoffs

23. 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.

24. End of Chapter 1