This chapter introduces spreadsheet modeling and decision analysis as a field of management science that uses computers, statistics, and mathematics to solve business problems. It discusses how spreadsheet models represent real-world phenomena with mathematical relationships and can help analyze decisions by evaluating potential outcomes. Examples are given of companies that achieved significant cost savings and efficiency gains by developing spreadsheet and other mathematical models to optimize areas like procurement, logistics, inventory management, and operations. The chapter also covers characteristics of models, benefits of modeling approaches, categories of mathematical models, and cognitive biases that can influence decision-making.

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