CPMS Daniel H. Wagner Prize Competition Project Portfolio ... · ©Intel Corp CPMS Daniel H. Wagner Prize Competition . 1 . Siddhartha Sampath and Karl Kempf, Intel Corporation

1 CPMS Daniel H. Wagner Prize Competition © Intel Corp

Siddhartha Sampath and Karl Kempf, Intel Corporation Prof. Esma Gel, Fulton Schools of Engineering, ASU Prof. John Fowler, Carey School of Business, ASU

Project Portfolio Planning at Intel Corporation

CPMS Daniel H. Wagner Prize Competition

Agenda 1.Company Background 2.The Business Problem 3.Novel Mathematical Solutions 4.The Decision Process 5.Growing Business Impact


Founded July 18th, 1968

6


Predictable SILICON TRACK RECORD Executing to Moore’s Law Since

1965 Enabling new devices with higher functionality and complexity while controlling power, cost and size.


Desktops Laptops Embedded Smartphones Tablets Entertainment

Since 15 November 1971 when Intel introduced the world’s first

microprocessor, we have developed a variety of products that are sold into an

ever-increasing number of markets. 4004 with ~2.3x103

transistors

Servers

with ~5.0x109

transistors

with ~4.7x107

transistors


The Business Problem - There are products the company wants to launch over time. - Each product require multiple substantial engineering projects. - The products all bring a benefit to the company. - The projects are interrelated in a variety of ways. - The projects all cost something.


- There are products the company wants to launch over time. - Each product require multiple substantial engineering projects. - The products all bring a benefit to the company. - The projects are interrelated in a variety of ways. - The projects all cost something.

- There is not enough budget to fund all the projects! - How should the budget be allocated to maximize benefit? - How can various qualitative & quantitative criteria be included?


The Business Problem

- There are products the company wants to launch over time. - Each product require multiple substantial engineering projects. - The products all bring a benefit to the company. - The projects are interrelated in a variety of ways. - The projects all cost something.

- There is not enough budget to fund all the projects! - How should the budget be allocated to maximize benefit? - How can various qualitative & quantitative criteria be included?

- What portfolio of engineering projects should be funded?



At a scale relevant to INTEL Corporation … - the problem is too complex to solve well

using only business intuition … - there are more business details than can be

analytically modelled …



At a scale relevant to INTEL Corporation … - the problem is too complex to solve well

with business intuition … - there are more business details than can be

analytically modelled …

The solution process must include: - analytics informing intuition - intuition informing analytics



THE ANALYTICS

Our Mathematical Solution Components


Mapping: To visualize projects, products, and relations

Enumeration: To efficiently enumerate decision alternatives

Valuation: To quantify business decisions and impacts

Optimization: To generate a feasible set of alternatives

What-Ifs: To enable the intuition to inform the analytics

1) MAPPING:


Essential to: Help analysts visualize the business and capture project and product input in a systematic fashion ready for analysis.

Difficult Because: The relevant information and knowledge is distributed across the organization, and there are a variety of complex interactions between projects and products.

Our Contribution : Education and application of the principals of Decision Analysis as well as a review board to increase input data quality.


projects impacting markets

projects impacting platforms

projects impacting

silicon

Product-1 Product-3

Project-a

Project-b Project-d Project-c

Project-y Project-z Project-x

Market-1 Market-2 Market-3

Product-2

Prospect 1,1 2,2 2,3 3,3

MAPPING: Entities, Interactions and Values

required optional Influence

AND, ORs, SEQ

NPV

TAM SAM

Eng. Cost

VOL ASP

Mfg. Cost

DATA




projects impacting

silicon

Product-1 Product-3

Project-a




Product-2

Prospect 1,1 2,2 2,3 3,3



AND, ORs, SEQ

NPV

TAM SAM

Eng. Cost

VOL ASP

Mfg. Cost

DATA

NPV




projects impacting

silicon

Product-1 Product-3

Project-a




Product-2

Prospect 1,1 2,2 2,3 3,3

TAM SAM

Eng. Cost

VOL ASP

Mfg. Cost

DATA



AND, ORs, SEQ

2) Enumeration:


Essential to: Identify and generate the list of all feasible decision alternatives.

Difficult Because: The large number of decisions and interrelationships between various quantitative metrics.

Our Contribution: Enumeration algorithm that automates generation of decision units.


Enumerating Decisions: The NPV (and other attributes) of each prospect is a function of different variables that depend on the various projects that impact it as well as the other products that are entering a given market.

M1

MKTG P1 P2

ENG1 ENG2

ENG3

Portfolio Unit

1st Year Spending Remaining Spending

ENG1 12 12

ENG2 15 22

MKTG 11 16

ENG3 9 11

Decision Unit Portfolio Unit & Scenario

NPV/Discounted Cash Flow

C1 +Neighborhood = C1* 166

C1* - C2 75

C1* - MKTG 125

C1* -C2, MKTG 42

C1* -ENG3 55

C1* -C2, ENG3 59

C1* -MKTG, ENG3 23

C1* -C2, MKTG, ENG3 30



M1

MKTG P1 P2

ENG1 ENG2

ENG3

Portfolio Unit


ENG1 12 12

ENG2 15 22

MKTG 11 16

ENG3 9 11


Decision Unit (Portfolio Unit & Scenario)

NPV/Discounted Cash Flow


C1* - C2 75

C1* - MKTG 125

C1* -C2, MKTG 42

C1* -ENG3 55

C1* -C2, ENG3 59

C1* -MKTG, ENG3 23

C1* -C2, MKTG, ENG3 30


C2* -C1 19

C2* -ENG3 20

C2* -C1, ENG3 5


M1

MKTG P1 P2

ENG1 ENG2

ENG3

Portfolio Unit


ENG1 12 12

ENG2 15 22

MKTG 11 16

ENG3 9 11

Generating All Possible Decision Units

Input: A portfolio unit, its neighborhood and relationships




Output: All possible decision units of a given portfolio unit.




Call Solver

Output: All possible decision units of a given portfolio unit.

Does solution x’ exist?


M1: Maximize:∑ 𝑥𝑥𝑗𝑗𝑚𝑚𝑗𝑗=1 (1)

Subject to: 𝑥𝑥𝑗𝑗 = 1, ∀𝑗𝑗 ∈ 𝐼𝐼𝑖𝑖 (2) 𝑥𝑥𝑗𝑗 = 0, ∀𝑗𝑗 ∈ 𝐸𝐸𝑖𝑖 (3) ∑ 𝑥𝑥𝑗𝑗𝑗𝑗 𝜖𝜖𝑀𝑀𝑖𝑖

= 1, (4) ∑ 𝑥𝑥𝑗𝑗 ≤ 1𝑗𝑗𝜖𝜖𝑄𝑄𝑖𝑖 , (5) Let x’ be the solution to M1

∑ 𝑥𝑥𝑖𝑖 ≤ ∑ 𝑥𝑥𝑖𝑖′𝑚𝑚𝑖𝑖=1𝑖𝑖 𝜖𝜖{𝑖𝑖:𝑥𝑥𝑖𝑖

′=1} − 1 𝑥𝑥𝑥 is the previous scenario

Set Up Model

No: Stop

3) Valuation:


• Essential to: Value decision units in preparation for portfolio optimization.

• Difficult Because: Large number of correlated variables and scenarios that need to be valued.

• Our Contribution : Combination of user estimates and curve-fitting forecasts all values for the enumerated model.


Input: Set of Decision Units from previous stage

Valuating a Decision Unit



Output: List of metrics associated with each decision unit (NPV, Revenue by Year, Spending by Year …)


Generating Values for a Decision Unit


Data

(ASP

, VO

L., M

FG$,

EN

G$)

90th %ile realization

at time t

10th %ile realization at time t

Forecast Historical

Time

Data = ASP, Volume, Manufacturing Cost, Engineering Cost


Capture expert estimates for simulation variables

Capture historical realization of simulation

variables

Historical curve fitting: non-linear regression

and time series analysis using ARIMA models

Custom Monte Carlo Simulation valuating

decision unit


Output: List of metrics associated with each decision unit (NPV, Revenue by Year, Spending by Year …)

Project Planning Records

Forecast Variables


4) OPTIMIZATION:


Essential to: Generating an ordered set of non-dominated portfolios.

Difficult Because: Large number of possible combinations and complex relationships between decision units.

Our Contribution : Optimization algorithm quickly generates efficient frontier of non-dominated portfolios.


Step 1: We generate the first portfolio including all projects without any spending or headcount constraints.

Port

folio

NPV

Portfolio Spending

Algorithm to Generate Non-Dominated Solutions


Step 1: We generate the first portfolio including all projects without any spending or headcount constraints. Step2: To ensure that the next portfolio is a non-dominated portfolio, we add three cuts.

Algorithm to Generate Non-Dominated Solutions Po

rtfo

lio N

PV

Portfolio Spending


Step 1: We generate the first portfolio including all projects without any spending or headcount constraints. Step2: To ensure that the next portfolio is a non-dominated portfolio, we add three cuts. Step 3: We continue adding cuts and repeating Step 2, until there are no more feasible portfolios.

Port

folio

NPV

Portfolio Spending



Input: All decision units and associated attributes




Output: Ordered Set of non-dominated portfolios.





Setup Model


M2: Maximize: ∑ ∑ 𝑝𝑝𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗(𝑖𝑖)𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1 (9)

𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬 𝐬𝐬𝐭𝐭: Spending Constraints 𝐵𝐵min ≤ ∑ ∑ 𝑠𝑠𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝐵𝐵𝑚𝑚𝑚𝑚𝑥𝑥 𝐷𝐷(𝑖𝑖)

𝑗𝑗=1𝑁𝑁𝑖𝑖=1 (10)

One Valuation/Unit Constraint ∑ 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝑦𝑦𝑖𝑖 𝐷𝐷(𝑖𝑖)𝑗𝑗=1 ∀ 𝑖𝑖 = 1, … ,𝑁𝑁 (11)

Inclusion Constraint ∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 − ∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘

𝑁𝑁𝑘𝑘=1 𝑦𝑦𝑘𝑘 ≤ 0 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖 (12)

Exclusion Constraint ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 + ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘

𝑁𝑁𝑘𝑘=1 𝑦𝑦𝑘𝑘 ≤ ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘

𝑁𝑁𝑘𝑘=1 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖 (13)

Let y’ be the solution to M2




Setup Model

Calculate convex hull of generated portfolios


M2: Maximize: ∑ ∑ 𝑝𝑝𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗(𝑖𝑖)𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1 (9)

𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬 𝐬𝐬𝐭𝐭: Spending Constraints 𝐵𝐵min ≤ ∑ ∑ 𝑠𝑠𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝐵𝐵𝑚𝑚𝑚𝑚𝑥𝑥 𝐷𝐷(𝑖𝑖)

𝑗𝑗=1𝑁𝑁𝑖𝑖=1 (10)

One Valuation/Unit Constraint ∑ 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝑦𝑦𝑖𝑖 𝐷𝐷(𝑖𝑖)𝑗𝑗=1 ∀ 𝑖𝑖 = 1, … ,𝑁𝑁 (11)

Inclusion Constraint ∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 − ∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘

𝑁𝑁𝑘𝑘=1 𝑦𝑦𝑘𝑘 ≤ 0 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖 (12)

Exclusion Constraint ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 + ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘


𝑁𝑁𝑘𝑘=1 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖 (13)


Yes

Add Cut

Does y’ exist?

Exclude Previous Solution Cut ∑ 𝑦𝑦𝑖𝑖 − ∑ 𝑦𝑦𝑖𝑖𝑖𝑖 𝜖𝜖{𝑦𝑦𝑖𝑖

′=0} ≤ ∑ 𝑦𝑦𝑖𝑖′𝑁𝑁𝑖𝑖=1𝑖𝑖 𝜖𝜖{𝑖𝑖:𝑦𝑦𝑖𝑖

′=1} − 1 Spending Cut ∑ ∑ 𝑠𝑠𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝐵𝐵𝑦𝑦′

𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1

5) PREPARING for WHAT-IFS:


Essential to: Generating analytic solutions to respond to intuitive what-ifs.

Difficult Because: Even apparently simple what-ifs require analysis since forcing a project in or out may change valuations of other units.

Our Contribution : Efficient IP’s quickly generate alternate portfolios. A fractional binary integer program helps generate feasible portfolios. Both provide analytical responses to intuitive what-ifs.


- Dollar budget - Headcount budget

- Revenue - Volume

- Individual projects - Groups of projects

PREPARING for “Intuitive” WHAT-IFS

Spending Durations (1yr vs. 3yrs)


Different Three Year Spending

Comparable One Year Spending

PO

RTF

OLI

O N

PV

PO

RTF

OLI

O N

PV

PORTFOLIO SPENDING NEXT 1 YEAR

PORTFOLIO SPENDING NEXT 3 YEARS

100m 300m


Input:

Maximize: 𝑼𝑼𝑇𝑇𝒘𝒘+𝛼𝛼

𝑹𝑹𝑇𝑇𝒘𝒘+𝛽𝛽 (22)

Subject to: 𝐴𝐴𝒘𝒘 ≤ 𝑏𝑏, (23)

𝐖𝐖𝐖𝐖𝐬𝐬𝐖𝐖𝐬𝐬: 𝒘𝒘𝜖𝜖 0,1 𝑞𝑞 (24)

Spending Durations

Output: Set of additional portfolios


Input:





Spending Durations

Does y’ exist? Solve

Need more solutions?

Output: Set of additional portfolios

No


Input:





Under the assumption that the feasible region is non-empty and bounded 𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧 = 1

∑ ∑ 𝑅𝑅𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧𝐷𝐷 𝑖𝑖

𝑗𝑗=1𝑁𝑁𝑖𝑖=1 +𝛽𝛽

. 𝑥𝑥𝑗𝑗 𝑖𝑖 (38)

𝑦𝑦𝑖𝑖𝑧𝑧 = 1∑ ∑ 𝑅𝑅𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖

𝑧𝑧𝐷𝐷 𝑖𝑖𝑗𝑗=1

𝑁𝑁𝑖𝑖=1 +𝛽𝛽

.𝑦𝑦𝑖𝑖 (39)

𝑡𝑡 = 1∑ ∑ 𝑅𝑅𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗 𝑖𝑖

𝑧𝑧𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1 +𝛽𝛽

(40)

M3: Maximize∑ ∑ 𝑈𝑈𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧𝐷𝐷(𝑖𝑖)

𝑗𝑗=1𝑁𝑁𝑖𝑖=1 + 𝛼𝛼 (25)

𝐒𝐒𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬 𝐬𝐬𝐭𝐭: (10), (11), (12), (13)

∑ ∑ 𝑅𝑅𝑗𝑗 𝑖𝑖 𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧𝐷𝐷 𝑖𝑖

𝑗𝑗=1𝑁𝑁𝑖𝑖=1 + 𝛽𝛽𝑡𝑡 = 1, (26)

−𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧 + M𝑥𝑥𝑗𝑗 𝑖𝑖 + 𝑡𝑡 ≤ M,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 , 𝑗𝑗𝜖𝜖{1, … ,𝐷𝐷 𝑖𝑖 } (27)

−𝑦𝑦𝑗𝑗𝑧𝑧 + M𝑦𝑦𝑖𝑖 + 𝑡𝑡 ≤ M,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 (28) 𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧 + M𝑥𝑥𝑗𝑗 𝑖𝑖 − 𝑡𝑡 ≤ M,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 , 𝑗𝑗𝜖𝜖 1, … ,𝐷𝐷 𝑖𝑖 (29) 𝑦𝑦𝑗𝑗𝑧𝑧 + M𝑦𝑦𝑖𝑖 − 𝑡𝑡 ≤ M,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 (30) 𝑥𝑥𝑗𝑗 𝑖𝑖𝑧𝑧 − M𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 0,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 , 𝑗𝑗𝜖𝜖{1, … ,𝐷𝐷 𝑖𝑖 } (31) 𝑦𝑦𝑗𝑗𝑧𝑧 − M𝑦𝑦𝑖𝑖 ≤ 0,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 (32) −M𝑥𝑥𝑗𝑗 𝑖𝑖

𝑧𝑧 + 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 0,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 , 𝑗𝑗𝜖𝜖{1, … ,𝐷𝐷 𝑖𝑖 } (33) −M𝑦𝑦𝑗𝑗𝑧𝑧 + 𝑦𝑦𝑖𝑖 ≤ 0,∀𝑖𝑖𝜖𝜖 1, . . ,𝑁𝑁 (34) −𝑡𝑡 ≤ 0 (35)

𝟏𝟏∑ ∑ 𝑅𝑅𝑗𝑗(𝑖𝑖)


𝑁𝑁𝑖𝑖=1

≤ 𝑡𝑡 ≤ 1min

j(i)={j(i):Rj(i)>0,𝑖𝑖𝑖𝑖 1,…,𝑁𝑁 ,𝑗𝑗𝑖𝑖 1,…,𝐷𝐷 𝑖𝑖 }(𝑅𝑅𝑗𝑗(𝑖𝑖))

(36)

𝑀𝑀 = 1min

j(i)={j(i):Rj(i)>0,𝑖𝑖𝑖𝑖 1,…,𝑁𝑁 ,𝑗𝑗𝑖𝑖 1,…,𝐷𝐷 𝑖𝑖 }(𝑅𝑅𝑗𝑗(𝑖𝑖))

(37)


Transform

Record the solution y’ Set up the following cut: ∑ 𝑦𝑦𝑖𝑖 − ∑ 𝑦𝑦𝑖𝑖𝑖𝑖 𝜖𝜖{𝑦𝑦𝑖𝑖

′=0} ≤ ∑ 𝑦𝑦𝑖𝑖′𝑁𝑁𝑖𝑖=1𝑖𝑖 𝜖𝜖{𝑖𝑖:𝑦𝑦𝑖𝑖

′=1} − 1

Set up model

No

Spending Durations


-4,564 Drop Unit A

-1,985 Drop Unit C -7,269 Drop Unit D -2,885 Drop Unit E

PORTFOLIO #1 TOTAL NPV = 51,326

Drop Unit B

An incremental value report showing the

consequence of dropping a unit from a portfolio under consideration.

Incremental Value Report

2,142


Drop Unit D -1,844



Devalued Unit B -5,425

DELTA - 7,269

A more detailed report showing the

consequences of dropping unit D

when unit B depends on the

presence of unit D.

-4,564 Drop Unit A

-1,985 Drop Unit C -7,269 Drop Unit D -2,885 Drop Unit E


Drop Unit B 2,142

Incremental Value Report An incremental value report showing the

consequence of dropping a unit from a portfolio under consideration.

Input: Starting portfolio 𝑦𝑦𝑚𝑚, Desired Metric 𝑍𝑍𝑗𝑗 𝑖𝑖


Incremental Value Report Algorithm


Output: List of changes in value 𝛾𝛾𝑖𝑖𝑇𝑇for all remaining units in the portfolio when portfolio unit 𝑖𝑖 is dropped and the overall incremental value of the unit 𝜁𝜁𝑖𝑖.




Initialize: 𝜁𝜁𝑖𝑖 ← ∑ ∑ 𝑍𝑍𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗 𝑖𝑖

𝑚𝑚𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1

For each i ∈ {𝑦𝑦𝑚𝑚 = 1}

Output: List of changes in value 𝛾𝛾𝑖𝑖𝑇𝑇for all remaining units in the portfolio when portfolio unit 𝑖𝑖 is dropped and the overall incremental value of the unit 𝜁𝜁𝑖𝑖.

End of list?

Record Solution, update 𝛾𝛾𝑇𝑇 , 𝜁𝜁𝑖𝑖



1. 𝜁𝜁𝑖𝑖 = 𝜁𝜁𝑖𝑖 − ∑ ∑ 𝑍𝑍𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗(𝑖𝑖)𝐷𝐷(𝑖𝑖)𝑗𝑗=1

𝑁𝑁𝑖𝑖=1

2. L1: for 𝒕𝒕𝜖𝜖{1,2, … ,𝑁𝑁} do 3. 𝛾𝛾𝑖𝑖𝑡𝑡 ← 0 4. L2: for 𝑢𝑢𝜖𝜖{1,2, …𝐷𝐷 𝑡𝑡 } do 5. 𝛾𝛾𝑖𝑖𝑡𝑡 = 𝛾𝛾𝑖𝑖𝑡𝑡 + ∑ 𝑍𝑍𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗 𝑖𝑖

𝑚𝑚𝐷𝐷(𝑖𝑖)𝑗𝑗=1

6. 𝛾𝛾𝑖𝑖𝑡𝑡 = 𝛾𝛾𝑖𝑖𝑡𝑡 − ∑ 𝑍𝑍𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗 𝑖𝑖𝐷𝐷(𝑖𝑖)𝑗𝑗=1

7. end L2:for 8. end L1:for

Solve M4: Minimize∑ 𝑦𝑦𝑖𝑖𝑖𝑖={𝑖𝑖:𝑦𝑦𝑖𝑖𝑎𝑎=1}

Subject to:

∑ 𝑥𝑥𝑗𝑗 𝑖𝑖 ≤ 𝑦𝑦𝑖𝑖 𝐷𝐷(𝑖𝑖)𝑗𝑗=1 ∀ 𝑖𝑖 = 1, … ,𝑁𝑁

∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 − ∑ 𝐶𝐶𝑗𝑗 𝑖𝑖 ,𝑘𝑘

𝑁𝑁𝑘𝑘=1 𝑦𝑦𝑘𝑘 ≤ 0 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖

∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘𝑁𝑁𝑘𝑘=1 𝑥𝑥𝑗𝑗 𝑖𝑖 + ∑ 𝐸𝐸𝑗𝑗 𝑖𝑖 ,𝑘𝑘


𝑁𝑁𝑘𝑘=1 ∀ 𝑗𝑗 𝑖𝑖 = 1, … ,𝐷𝐷 𝑖𝑖

𝑦𝑦𝑖𝑖 = 0

Let 𝑦𝑦′ be the solution to M4. Yes

Set up model


Drop Unit M -475

+532 Add Unit W

Drop Unit R Drop Unit Q

Drop Unit P -75 Drop Unit O

-78 Drop Unit N

+119 Add Unit V +78 Add Unit U

+50 Add Unit T +41 Add Unit S

PORTFOLIO #3 TOTAL NPV + 69,429

DELTA +192


Waterfall Report A detailed trajectory of moving from one portfolio to another.

Input: Starting portfolio 𝑦𝑦𝑚𝑚, Ending portfolio 𝑦𝑦𝑏𝑏, Desired Metric 𝑍𝑍𝑗𝑗(𝑖𝑖)

Waterfall Report Algorithm


Output: Migration plan from A to B with a feasible portfolio at every step, i.e. An ordered set of Portfolios Y




Determine 𝛽𝛽 the list of portfolio units such that 𝑦𝑦𝑖𝑖𝑚𝑚 = 0 and 𝑦𝑦𝑖𝑖𝑏𝑏 = 0. Determine 𝜈𝜈 the list of portfolio units such that 𝑦𝑦𝑖𝑖𝑚𝑚 = 1 and 𝑦𝑦𝑖𝑖𝑏𝑏 = 1. 𝑦𝑦𝑐𝑐 ← 𝑦𝑦𝑚𝑚 ,

Output: Migration plan from A to B with a feasible portfolio at every step, i.e. An ordered set of Portfolios Y

Does y’ exist?

End of list?

record the solution add to Y, update 𝑦𝑦𝑐𝑐with 𝑦𝑦𝑥 Update 𝛿𝛿 and 𝛼𝛼

Do nothing since no solution exists

Yes

Determine 𝛿𝛿 the list of portfolio units such that 𝑦𝑦𝑖𝑖𝑐𝑐 = 1 and 𝑦𝑦𝑖𝑖𝑏𝑏 = 0 Determine 𝛼𝛼 the list of portfolio units such that 𝑦𝑦𝑖𝑖𝑐𝑐 = 0 and 𝑦𝑦𝑖𝑖𝑏𝑏 = 1 𝑡𝑡 ← 1



for 𝑡𝑡 < 𝛿𝛿 + |𝛼𝛼| do Solve M5: Minimize: ∑ ∑ 𝑍𝑍𝑗𝑗(𝑖𝑖)𝑥𝑥𝑗𝑗(𝑖𝑖)


𝑁𝑁𝑖𝑖=1

Subject to: (11), (12), (13) ∑ 𝑦𝑦𝑖𝑖𝑖𝑖𝜖𝜖𝛼𝛼 − ∑ 𝑦𝑦𝑖𝑖𝑐𝑐𝑖𝑖𝜖𝜖𝛼𝛼 + ∑ 𝑦𝑦𝑖𝑖𝑐𝑐𝑖𝑖𝜖𝜖𝑖𝑖 − ∑ 𝑦𝑦𝑖𝑖𝑖𝑖𝜖𝜖𝑖𝑖 = 𝑡𝑡 ∑ 𝑦𝑦𝑖𝑖𝑖𝑖𝜖𝜖𝛽𝛽 = 0 ∑ 𝑦𝑦𝑖𝑖𝑖𝑖𝜖𝜖𝑖𝑖 = 1 Let 𝑦𝑦𝑥 be the solution to M5

Yes

Solve

No

Port

folio

NPV

Portfolio Spending

Port

folio

NPV

Portfolio Spending


REAL TIME FORCE IN/OUT IMPACTS

-40m

-15m

Port

folio

NPV

Portfolio Spending

Port

folio

NPV

Portfolio Spending



+4m -4m

Constraint Cuts

Constraint Cuts

Obj

ectiv

e Cu

ts

Headcount

Volume

Port

folio

NPV

Portfolio Spending

Port

folio

NPV

Portfolio Spending


Reve

nue


-40m

-15m

+4m -4m

THE BUSINESS PROCESS Analytics Intuition

using “Elimination by Aspects”


Essential to: Allow the intuition to inform the analytics.

Difficult Because: Challenging to align qualitative and strategic objectives with quantitative objectives.

Breakthrough Math: “Elimination by Aspects” allows the combination of many objectives to narrow the generated feasible set to a few desirable portfolios. Supports decision maker in feeling satisfied with their final choice of a portfolio.

Using the Budget Aspect


Check Portfolios wrt Budget Portfolio spend within

+/- 10% of budget


Efficient Frontier

of non-dominated

portfolios

Budget

Strong portfolios within +/- 10% of the budget …

… checked over (multiple) year

budget.

Using the Budget Aspect

Port

folio

NPV

Portfolio Spend


Check Products wrt Strategy Force selected products in / out and re-optimize


+/- 10% of budget

Using the Strategy Aspect


Efficient Frontier

of non-dominated

portfolios

Budget

Port

folio

NPV

Portfolio Spend (next year)

Projects/Products that are in ALL of

the portfolios selected

Projects/Products that are in NONE of


Projects/Products that are in SOME of


Force OUT ??

Force IN ??

Drill Down, Decide !!

Using the Strategy Aspect


Drop Unit D -1,844



Devalued Unit B -5,425

DELTA - 7,269

INCREMENTAL VALUE REPORT

Drop Unit M

-475

+532 Add Unit W

Drop Unit R Drop Unit Q

Drop Unit P -75 Drop Unit O

-78 Drop Unit N

+119 Add Unit V

+78 Add Unit U

+50 Add Unit T

+41 Add Unit S


DELTA +192


WATERFALL REPORT

Using the Strategy Aspect PRODUCT ROADMAP What products are

in (colored) and out (grey)

of the portfolio under inspection.


Check Skill Set Changes Consider the suggested skill changes over time



+/- 10% of budget

Using the Resources Aspect


Using the Resources Aspect

TIME

NU

MBE

R RE

QU

IRED

SKILL SET


Check Balance across Markets Consider budget allocation and return in all business segments




+/- 10% of budget

Using the Balance Aspect


Branches as business segments (enterprise, workstations, high performance, cloud, etc.) Leaves as … Projects sized as dollar budget Projects sized as headcount Products sized by NPV Products sized by volume Colors as … Projects in flight, new projects, strategic projects (forced in) Investment Efficiency (prod. NPV/proj. SPEND) Year a product enters market

Using the Balance Aspect


Senior Management Intuition Special knowledge from Sales & Marketing, Product Design, etc

Check Balance across Markets Consider budget allocation and return in all business segments




+/- 10% of budget

Using the Special Aspect

The “old process” = advocacy.


THE (SERVER) BUSINESS IMPACT


- Interviewed portfolio tool vendors and thought we needed stronger mathematics. - Interviewed companies using portfolio ideas and thought we needed higher quality data.

Launched … - Decision Analysis approach for data quality - Optimization approach for decision quality




- Interviewed portfolio tool vendors and thought we needed stronger mathematics. - Interviewed companies using portfolio ideas and thought we needed higher quality data.

Launched … - Decision Analysis approach for data quality - Optimization approach for decision quality

The “new process” = quality and transparency.



- 1st trial: partial SERVER business plan end 2011 - 2nd trial: full SERVER business plan mid 2012 - Process of choice (‘mission critical’) ever since




- Three dozen “analysts/mapping builders” for 2 weeks - Senior VP and staff (8) for 2 days - Approval with CEO/President/CFO for 2 hours




- Three dozen “analysts/mapping builders” for 2 weeks - Senior VP and staff (8) for 2 days - Approval with CEO/President/CFO for 2 hours

- Time and effort cut by more than half. - Value of portfolio up ~10% at constant budget. - Being adopted by other Intel business units.



The Project Portfolio Decision Support Suite is written in C# utilizing Microsoft Windows Presentation Foundation (WPF) and IBM’s Concert/CPLEX.

Running on an remote server with a quad-core Intel i7, 8 GB ram, and 100 GB disk … for problems with a few hundred projects and products … accessed through analysts’ laptops … ENUMERATION takes between 30 and 60 seconds VALUATION takes roughly 2 hrs. OPTIMIZATION takes between 30 and 60 seconds WHAT-IFs take between 5 and 30 seconds


IMPLEMENTATION


Project Portfolio Planning at Intel Corporation

CORPORATE VISION: This decade we will create

and extend computing technology to connect and enrich the lives of every person on earth.

Documents

CPMS Daniel H. Wagner Prize Competition Project Portfolio ... · ©Intel Corp CPMS Daniel H. Wagner Prize Competition . 1 . Siddhartha Sampath and Karl Kempf, Intel Corporation