Designing Enterprise Decisions
Decisions … decisions
A model of a real company, ADI
Research goals
What can I control?
How?
By how much?
Enable executives to make high quality
decisions by effective exploration of the
decision and solution spaces using
engineering methods …
particularly, design of experiments (DOE)
What cannot be controlled?
What can I do about that?
Can I limit my down-side risks?
new product
development
competitors
market potential
and market share
new
products
orders
competitors’
price
prod. price
pricing
product price
shipments
Total
Quality
Management
unit cost
manufacturing
yield
management
accounting
costs
costs and expenses
industry growth
balance sheet,
I&E, flow of funds
financial
accounting
R&D spending
financial
stress
market value of the firm
stock market
9 experiments, 27 points  predictions
The problem in DOE normal form
9-step hill-climbing: a very effective process
1
1
1
2
3
1
1
1
1
price
2
2
2
2
2
3
1
3
3
COGS
yield
R&D
1
1
1
1
1
1
1
2
3
2
3
1
3
3
3
3
3
3
environment
=


=


=


BAU
worst best
815.7
865.1
740. 8
829.1
795.9
899.8
835.2
910.0
872.7
937.4
677.6
581.8
647.7
617.3
707.5
650.7
693.2
689.8
944.0
984.0
885.8
960.7
931.3
1003.
965.7
989.9
982.6
ind
compt
orders
output
799.1
842.3
736.1
812.5
781.5
870.1
817.2
864.4
848.4
1
2
3
2
3
1
3
1
2
price
uncontrollable
variables
1
2
3
1
2
3
1
2
3
COGS
controllable
variables
1
1
1
2
2
2
3
3
3
yield
outcomes
threat of hostile take-over
 firm value
 SG&A
 op. income
 stock price
 gross profit
 COGS
 R&D
 price
 IC yield
 industry growth  ADI orders
 competitors’ attractiveness
R&D
problem
1
2
3
3
1
2
2
3
1
environment
=


=


=


BAU
718.9
783.7
820.6
789.3
691.5
830.5
694.4
849.6
717.9
worst
553.0
607.3
641.9
613.1
511.5
653.5
532.9
670.1
566.9
best
852.1
912.5
954.1
927.4
748.3
952.7
813.3
969.9
862.6
indust
compt
orders
output
708.0
767.8
805.5
776.6
650.4
812.2
680.2
829.9
715.8
Predictions output
best factorlevels 1,3,1,3
In best
environment
In worst
environment
BAU
878.1
Configuration of controllable variables
can favorably impact a firm’s performance,
under good and bad environments
Indicated Market Value of the firm
1.215 B
3
3
906.6
3
1.091 B
R&D 1.7%
yield 14.8%
967.22 M
3
3
3
3
719.03 M
2
3
3
3
3
2
2
2
2
2
1
2
2
1
2
2
1 1
2
3 3
2
3
2
1
1 1
1
1
1
worst
1
1 1
1
source: experiment L27 including full factorial
1
3 2
2 1
1
BAU
2
2
3
2
2
2
0
Summary of key findings
2
2
2
3
3
3
843.13 M
594.94 M
price 59.5%
3
best
1
COGS 24%
2
2
3
761.9
% contribution
3
2
2
3
603.3
2
3
3
3
3
1
1
4
1
1
1
1
6
Indicated Market Value of the firm : 110L81-61
Indicated Market Value of the firm : 110L81-41
Indicated Market Value of the firm : 110L81-21
1
1 1
8
1
10
1
2
1
2
3
1
2
3
1
2
3
12
Time (Month)
1
2
3
1
2
3
1
2
3
14
1
2
3
16
1
2
3
1
2
3
1
2
3
18
1
2
3
1
2
3
20
1
2
3
1
2
3
22
1
2
3
1
2
3
24
1
2
3
1
2
3
3
Case studies underway
 Our study suggests that this engineering
method for designing decisions can be
applied to enterprise decisions.
 How to optimize client satisfaction for a
risky Web-based development project for
a global manufacturing company?
 Sparcity, hierarchy, and inheritance all properties of complex engineering
systems - are also exhibited by an enterprise
– a socio-technical system.
 How to raise profit level by $xx M in the
next six months for a global electronics
outsourcing company?
Research contact: Victor Tang (Graduate Student), Massachusetts Institute of Technology
Research Advisor, Prof. Warren P. Seering Mechanical Engineering and Engineering Systems Division
victang@mit.edu MIT E60-256, Center for Innovation in Product Development, 30 Memorial Drive, Cambridge, MA 02142