Presentation about how to make business decisions with a quantitative framework - even for those who don't think quantitatively.

1. Quantitative Business Decision Making for
the Qualitative Minded
Mike Desilets, MBA

2. Σ
N
Average
Xn
n=1
N
Qualitative
Quantitative

3. Σ
N
Average
Xn
n=1
N
YES!!!
You are free to go…

6. 1
On a scale of 1 to 5, how much do you want tuna?
2
3
4
Not really
1
5
A Lot
On a scale of 1 to 5, how much do you want ham?
Not really
2
3
4
5
A Lot

9. 
You’ve been made 3 job offers in each of the following
cities:
◦ Denver
◦ San Francisco
◦ Boston

You decide that the most important factors to consider
where to move are (in order)
◦
◦
◦
◦

Weather (given the fact you aren’t a skier)
Salary
City’s cultural and historical value
Proximity to your family (who are in Denver)
Task: Use a quantitative decision matrix to help

11. 1
Not
Important
2
3
Neutral
4
5
Very
Important

12. 1
2
Very Bad
3
4
Bad
Neutral
5
Good
Very Good
Options
Family
Weather
Salary
City
Denver
Very Good
Neutral
Good
Neutral
San Fran
Good
Very Good
Neutral
Very Good
Boston
Bad
Bad
Very Good
Very Good

13. Family: 3
Weather: 5
Salary: 4
City: 4
Good: 4
Neutral: 3
Denver
Very Good: 5 Neutral: 3
San Fran
Good: 4
Very Good: 5 Neutral: 3
Boston
Bad: 2
Bad: 2
Very Good: 5
Very Good: 5 Very Good: 5

14. Family: 3
Weather: 5
Salary: 4
City: 4
Good: 4
(16)
Neutral: 3
(12)
Denver
Very Good: 5 Neutral: 3
(15)
(15)
San Fran
Good: 4
(12)
Very Good: 5 Neutral: 3
(25)
(12)
Boston
Bad: 2
(6)
Bad: 2
(10)
Very Good: 5
(20)
Very Good: 5 Very Good: 5
(20)
(20)

15. Family: 3
Weather: 5 Salary: 4
City: 4
3+5+4+4=16
Denver
Very
Good: 5
(15)
Neutral: 3
(15)
Good: 4
(16)
Neutral: 3
(12)
15+15+16+12
=58
San Fran
Good: 4
(12)
Very
Good: 5
(25)
Neutral: 3
(12)
Very
Good: 5
(20)
12+25+12+20
=69
Boston
Bad: 2
(6)
Bad: 2
(10)
Very
Good: 5
(20)
Very
Good: 5
(20)
6+10+20+20
=56
Family 12 + Weather 25 +
Salary 12 + City 20 = 69

16. Raw Score
Denver
58
San Fran
69
Boston
56

17. Raw Score 58 divided
by criteria sum 16
Equals 3.625
Raw Score
Ranking
Denver
58
58/16 = 3.265
San Fran
69
69/16 = 4.3125
Boston
56
56/16 = 3.5

18. Raw Score
Final Score
Denver
58
3.625
San Fran
69
4.3125
Boston
56
3.5
1
Very Bad
Denver
(3.625)
2
3
4
Bad
Neutral
Good
Boston (3.5)
San Fran
(4.3125)
5
Very Good

26. 








Overall Market Size
Growth Rate
Profit Margins
Competitive Intensity
Technology Requirements
Inflationary Vulnerability
Energy Requirements
Environmental Impact
Social/Political/Legal












Market Attractiveness
Market Share
Share Growth
Product Quality
Brand Reputation
Distribution Network
Promotional Effectiveness
Productive Capacity
Productive Efficiency
Unit Costs
Material Supplies
R&D Performance
Management
Business Strength

27. 





Very High: 5
High: 4
Neutral : 3
Low : 2
Very Low : 1
Not at all : 0
Importance to Evaluation





Very Positive : 5
Positive : 4
Neutral : 3
Negative : 2
Very Negative : 1
Overall Ranking
We can easily eliminate some options from
the evaluation by rating something as
“not important at all”

28. Project #1
Business Strength

29. Project #1

30. Project #1
Project #2

31. 
Porter’s 5 Forces to the market attractiveness

PESTLE Analysis to market attractiveness

Porter’s Value Chain to business strength

Day’s Risk Matrix to business strength


VRIO Model assessment of competitive advantages to
business strength
AND MORE!!!

33. 
Your model is only as good as the inputs

A canned model is never right. It…
◦ Reminds you what to look for
◦ Shows you which way you might want to go

The GE/McKinsey Matrix can be used to view
multiple projects and compare them.
◦ Use market size to add value (larger market means
larger plot marker)

34. Mike.Desilets.MBA@Gmail.com