Copyright

by

José Ernesto Alas

2011

The Thesis Committee for José Ernesto Alas

Certifies that this is the approved version of the following thesis:

Organizational Decision Making:

The Fuzzy Front End & Organizational Decision Making

APPROVED BY

SUPERVISING COMMITTEE:

Kyle Lewis

Bruce McCann

Supervisor:

Organizational Decision Making:

The Fuzzy Front End & Organizational Decision Making

by

José Ernesto Alas, BS EE

Thesis

Presented to the Faculty of the Graduate School of Engineering

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science in Engineering

The University of Texas at Austin

December 2011

Dedication

To my loving wife Monica and to my adorable kids Maya and Emilio who have

supported me with plenty of love, patience and encouragement throughout the last two

years. I would also like to express my love for my mother and father Chencho and Gladis

who instilled in me, my brother Eduardo and my sister Veronica the desire to continue

learning.

v

Acknowledgements

I would like to acknowledge Dr. Kyle Lewis and Dr. Bruce McCann for their help

in crafting this thesis. I would also like to thank the respondents who will remain

nameless for their candid conversations and attempts to make the Fuzzy Front End of

Innovation less fuzzy.

November 30th, 2011

vi

Abstract

Organizational Decision Making:

The Fuzzy Front End & Organizational Decision Making

José Ernesto Alas, M.S.E.

The University of Texas at Austin, 2011

Supervisor: Kyle Lewis

Decision-makers have many defined and widely accepted tools in place to

manage projects and programs. However, can the same be said for the very early stages

of projects? This research investigates what researchers are now referring to as the Fuzzy

Front End of Innovation, which is defined as the territory leading up to organizational-

level absorption and commercialization of the innovation process.

Despite all of the actions in establishing new operational efficiencies and project

management guidelines to improve New Product Development (NPD), a formalized

model does not exist for the screening and filtering of the most exceptional opportunities.

The ALAS Front End of Innovation Process Model© is proposed to help manage the

vii

innovation process. This model is based on an in-depth literature review and respondent

interview data.

As a secondary topic this thesis will look to understand and propose the

organizational structure required to support pre-phase Fuzzy Front End activities,

governance and management’s role. This will not be a discussion on organizational types

within development or engineering organizations (i.e.: matrix, product, platform

organizational structures) but rather from the findings propose a structure that helps

define who the key stake holders are in approving or rejecting development efforts.

viii

Table of Contents

List of Tables ......................................................................................................... ix 

List of Figures ..........................................................................................................x 

CHAPTER ONE Engines of Growth & The Fuzzy Front End ...............................1 

CHAPTER TWO Review of the Literature .............................................................9 

CHAPTER THREE Screening, Filtering, Management & Governance ...............28 

CHAPTER FOUR Research Methodology ...........................................................56 

CHAPTER FIVE Findings, Limitations & Further Research, and Conclusion ....61 

APPENDIX Research Questions ...........................................................................77 

References ..............................................................................................................82 

ix

List of Tables

Table 1: FEI vs NPD .............................................................................................12 

Table 2: Summary FFE Model Analysis ...............................................................26 

Table 3: Hypothetical opportunities for investment .............................................32 

Table 4: Legend – FFE Elements Respondent Answers ........................................71 

Table 5: Fuzzy Front End Key Elements versus Respondent Answers .................72 

x

List of Figures

Figure 1: The Five Levers Model ...........................................................................5 

Figure 2: The Front End of Innovation .................................................................11 

Figure 3: Traditional Stage Gate ...........................................................................12 

Figure 4: Conceptual Framework ..........................................................................14 

Figure 5: Risk, Uncertainty & Profit .....................................................................15 

Figure 6: A Stylized Model of the Front End of NPD ..........................................18 

Figure 7: The New Concept Development Model ................................................20 

Figure 8: Glassman Control Model .......................................................................24 

Figure 9: Glassman Concept Model – Idea Management & Idea Banks ..............29 

Figure 10: Innovation Tournaments ......................................................................31 

Figure 11: Innovation Return Curve .....................................................................33 

Figure 12: Innovation Horizons ............................................................................34 

Figure 13: A Broad Universe of Innovation Types ..............................................35 

Figure 14: Example format Mini-Business Plan ....................................................43 

Figure 15: ALAS Front End of Innovation Process Model© ................................48 

Figure 16: Organizational Configurations for the Innovation Process ..................50 

Figure 17: Seasonal Sales Product Mapping Model .............................................67 

Figure 18: Modified ALAS Front End of Innovation Process Model© ................76 

1

CHAPTER ONE Engines of Growth & The Fuzzy Front End

“Nothing is more dangerous than a new idea, when it’s the only one you have.”

-Emile Chartier

Introduction

“How can we innovate forever?” is a question Geoffrey Moore (2005) poses and

is a question decision-makers are facing daily in making complex and often business

critical program selections. Deciding what programs become products and more

importantly “engines of growth” is the crux of the problem for technology companies.

As CEO of Applied Materials, Mike Splinter, stated, “What engines of growth are we

going to use to reach escape velocity? What are some of the underlying principles in the

design of these engines?” Mr. Splinter is referring to two critical items. The first he

describes as the escape velocity needed to increase revenues that are heavily influenced

by cyclicality in the semiconductor capital equipment business, robust competition,

inflation in costs, and the influence of the global economy. All of these external forces

combine to bound revenue to an upper limit. When revenue is bounded, spending is

bounded. If spending is bounded, the ability to develop new products or improve the cost

of producing existing products is bounded. For the second he describes the engines as

new products – innovative, differentiated products and services that enable market share

gains and growth in profitability. Achieving the “escape velocity” ultimately refers to

real growth that surpasses previous historical company performance and continues to

bring value to shareholders.

2

Therefore two critical questions remain to be answered.

Question (1):

How do decision makers identify either incremental and/or discontinuous

innovations to distinguish themselves from the competition and drive engines of growth?

Question (2):

What organizational structure and toolset is required to support front end

innovation activities?

The importance of understanding the decision making process cannot be over

emphasized as it relates to program selection. One could easily argue the point that

Project Management has existed since the beginning of early human civilization. We

often hear the GEICO commercials “So easy a caveman could do it!” The reality is basic

planning needed to take place even in humankind’s early stages. For example:

Prepare food:

o Gather firewood

o Find rocks to make sparks

o Prepare and cook meal

Make spear:

o Cut branch

o Strip leaves

o Add decoration

Projects were primarily managed on an ad hoc basis as part of normal business

practices prior to the 1950s within the United States. Modern Project Management

3

became recognized as a distinct discipline in the 1950s (Cleland and Roland, 1994).

During this time formal techniques and tools were developed for defining project

schedules such as Critical Path Method (CPM) and Program Evaluation and Review

Technique (PERT). Additional tools were created such as project cost estimating, cost

management and engineering economics. As recently as 2006 the Association for the

Advancement of Cost Engineering (AACE) released the first integrated process for

portfolio, program and project management known as Total Cost Management

Framework (Hollman, 2006).

The Project Management discipline can also be described with other formal

techniques such as PRojects IN Controlled Environments or better known as PRINCE.

PRINCE was created in 1989 by the Office of Government Commerce (OGC) in the

United Kingdom as a formal way of structuring IT projects (OGC website). Since then it

has been modified to be a generic approach to managing projects and continues to evolve

(PRINCE2) and be used internationally.

From this very brief overview of project management origins, decision-makers

have many defined and widely accepted tools in place to manage projects and programs.

However, can the same be said for the very early stages of projects? This thesis aims to

understand what researchers are now referring to as the Fuzzy Front End of projects.

What is the Fuzzy Front End of projects? Koen defines the front end as those

activities that come before the formal and well-structured New Product and Process

Development process (Koen, 2001). A secondary definition provided to this concept is

to highlight the fuzzy front end as that territory leading up to organizational-level

absorption of the innovation process (Cohen and Levinthal, 1990). This can imply that

the structured organization may not be made aware of the activities at the front end of

4

innovation that are often perceived and subsequently understood as chaotic, unpredictable

and unstructured in comparison to established New Product Development processes. In

contrast, this phase can be considered complete once organizations and more importantly

decision-makers formulate product concepts or ideas and chose to either invest resources

to further develop the idea, store them for future consideration, or discard them entirely

(Khurana and Rosenthal, 1998).

To be clear this thesis does not focus on the fundamental act of idea generation

nor is it researched as any part of this document. However, to understand the decision-

making process three perspectives regarding the environment, the individual and the

organization are investigated to understand what roles these individuals and groups have

during the Fuzzy Front End of New Product Innovation. The underlying assumption is

that ideas and company visions already exist. The objective is to understand the

necessary front-end structure or process just before the point the remaining organization

is brought in to engineer and to commercialize said idea.

Why This Study

Technology companies have become effective and efficient in developing new

products through well-defined and practiced project management guidelines (PMBOK®

Guide). An example of this would be the Stage Gate or Product Lifecycle processes that

companies use to develop and introduce their products to market. They also have

focused heavily on improving manufacturing process efficiencies, as well as, reducing

Selling, General & Administrative (SG&A) expenses to increase value to shareholders.

As an example to this Geoffrey Moore (2005) helps define this activity as The Five

Levers (see Figure 1, The Five Levers Model) “where sequences of management actions

5

strive to optimize mission-critical workloads to remove risk so that in turn one can then

extract resources for new projects or to simply meet end of quarter financials.”

Figure 1: The Five Levers Model (Geoffrey Moore, 2005)

Despite all of the actions in establishing new operational efficiencies and project

management guidelines to improve New Product Development (NPD), companies should

take a second look at the starting point for all NPD processes to determine the direction

of any new product path to reduce ambiguity and therefore reduce fuzziness. Reinerstein

and Smith (1991) have identified in their consulting work that the greatest opportunities

for saving time are at the very beginning of the development cycle. They go on to assert

that taking actions at the Fuzzy Front End gives the greatest time savings for the least

expense. As we are so often reminded, time equals money. Despite decision-makers’

6

best efforts, this phase is often ignored possibly due to lack of levers that decision-makers

can utilize or the fact that it is perceived as a waste of company time and therefore

company resources.

As a secondary topic this thesis will look to understand and propose the

organizational structure required to support pre-phase Fuzzy Front End activities,

governance and Management’s role. This will not be a discussion on organizational

types within development or engineering organizations (i.e.: matrix, product, platform

organizational structures) but rather from the findings propose a structure that helps

define who the key stake holders are in approving or rejecting development efforts. This

will be defined as the governance required at the front end of innovation.

Technology companies will remain the primary focus of study for this thesis.

Overall transferability of results to other industrial sectors maybe limited.

The body of thesis will be organized into five chapters.

Introduction:

Chapter 1 describes the problem statement and motivation of the research into the

complex nature of the Fuzzy Front End and Organizational Decision Making. Two

research questions are proposed for investigation.

Literature Review:

Chapter 2 will focus on the current state of academic and business research and

how it applies to the front end of innovation. A brief overview of Technology &

Innovation Management (TIM) and New Product Development (NPD) literature will be

reviewed to understand if key concepts apply to the front end of product innovation. This

7

chapter will also review how ideas and product concepts are brought into the organization

as part of the Fuzzy Front End. The objective is to identify and understand which

individuals or organizations provide input within this very early stage of New Product

Development.

This chapter will also study proposed organizational structure to support the front

end of innovation, governance, as well as, management responsibilities. The objective

will be to understand if proposed models for organizational structure are successful in

reducing or even possibly creating ambiguity.

Screening, Filtering, Management & Governance:

Chapter 3 will review the research methodology and provide the ground work for

an innovation process model. The main research objective will be to seek out

information regarding the decision-making process for successful incremental or

discontinuous innovations. A secondary focus will be research aimed at capturing best

known methods used in the front end of innovation, as well as, understanding supporting

organizational structures.

Research Methodology:

Chapter 4 presents the results of the study and the analysis of the data. In

addition, observations will be provided as part of the interview summaries. Best practice

information will be defined and explained for decision-maker and organizational

consideration.

8

Results & Conclusion:

Chapter 5 will summarize the research conducted and discuss contributions,

limitations, and recommendations for future research in this area.

9

CHAPTER TWO Review of the Literature

The Fuzzy Front End of Innovation

It is common in the workplace to hear of projects failing or having a low success

of customer adoption due to poor planning. It is very rarely accepted that inefficient

resource execution or technical expertise led to the project failing as a whole. Despite

this unacceptably high failure rates have often been related to deficiencies during the very

early development stages. One could also state that accountability for project failure is

nowhere to be found. This is especially true at the executive management level. In an

extensive study conducted by Cooper and Kleinschmidt (1994), they show that “the

greatest differences between winners and losers were found in the quality of execution of

the pre-development activities”. They cited two contributing factors played a role in a

project’s success (Cooper and Kleinschmidt, 1990).

The quality of execution of pre-development activities.

A well-defined product and project prior to the development phase.

Cooper and Kleinshcmidt (1998) identify that pre-development activities receive

the least amount of attention compared to product development and commercialization

activities. They quantify these pre-development activities as 6% of the overall project

dollars spent and 16% of the man-days of the total project. In addition, as a result of this

study when successes are compared to failures, about twice as much money and time is

spent for the front end stages.

10

Very little attention has been given to pre-development activities and what

researchers call the Fuzzy Front End of Innovation. The Fuzzy Front End, a term first

popularized by Smith and Reinertsen (1991), is considered to be the first stage of the

New Product Development (NPD) process. This stage is defined roughly as the period

between idea generation and activity spent on an idea prior to the first official group

meeting to discuss it, or what it is commonly called “the start date of team alignment”

(Murphy and Kumar, 1997). A secondary definition provided to this concept is to

highlight the fuzzy front end as that territory leading up to organizational-level absorption

of the innovation process (Cohen and Levinthal, 1990). Authors on the subject clearly

identify early and late activities that define the fuzzy front end regardless of the type of

innovation. For instance authors speak of problem/opportunity structuring;

identification/recognition; information collection/exploration; and up-front homework

(Leifer, 2000; Urban and Hauser, 1993; Cooper, 1996). More recently several studies

have highlighted the importance of the fuzzy front end (Shenhar, 2002; Verwon, 2008).

The Front End of Innovation can best be described as an unknown development

stage where ideas or direction originate from what appears to be unstructured chaos.

There appear to be no rules, no framework and no real understanding (Figure 2).

11

Figure 2: The Front End of Innovation (Self)

“Engines of growth” come from this very early stage in the form of platform and

breakthrough projects, in addition to, incremental projects. Once these ideas are screened

they are introduced formally to the organization and continue down a path of defined

New Product Development (NPD) stages where they finally become commercialized.

Figure 3 is defined as the traditional stage-gate approach for projects. Within this figure

no detail is provided for the very earliest stage known as the Front End of Innovation,

however, it is generally modeled as a funnel where many ideas enter and only a few exit.

Given that many project management teams adhere to a similar structure and that many

resources result in wasted innovation, it is important to add definition and clarity to fuzzy

front end activities. This does not imply adding rules and stifling creativity and hence

innovation but rather outlining a process that encourages the best opportunities move

forward, while others may be further refined as part of an incubation process.

12

Figure 3: Traditional Stage Gate (Consortium for Corporate Entrepreneurship)

To summarize how the Front End of Innovation (FEI) differs from New Product

Development (NPD), the Consortium for Corporate Entrepreneurship has separated the

two processes and compared there similar attributes (Table 1).

Table 1: FEI vs NPD (Consortium for Corporate Entrepreneurship)

The deliverables of the Fuzzy Front End can best be summarized as follows.

1) A clear product concept

13

2) General knowledge and understanding of the concept (market and technical

uncertainties defined)

3) A new product development plan

Reduce or Create Ambiguity

Understanding how fuzziness is defined in the common literature of management

theory and organization is important because authors define key concepts that will be

investigated in subsequent chapters and how they manifest themselves within technology

companies. Fuzziness can be understood through the terms uncertainty, ambiguity and

equivocality (Eric Brun, 2008). Organizational informational requirements have been

studied by Daft and Lengel and they state that whenever organizational information

requirements are not met, this will lead to uncertainty or equivocality by stakeholders

(Daft and Lengel, 1986). They also define uncertainty as the absence of information and

as the value of information increases, uncertainty decreases. Daft and Lengel go on to

define equivocality:

“Equivocality means ambiguity, the existing of multiple and conflicting

interpretations about an organizational situation. High equivocality means

confusion and lack of understanding. Equivocality means that asking yes-no

questions is not feasible. Participants are not certain about what questions to

ask, and if questions are posed, the situation is ill-defined to the point where a

clear answer will not be forthcoming.” (Daft and Lengel, 1986)

Stakeholders involved in the new product development process deal with both

uncertainty (not knowing the market potential or future technical roadmaps) and

14

equivocality (conflicting interpretations of a product idea or market need) (Eric Brun,

2008). Another equally important definition of the term equivocality is defined as the

presence of two or more possible meanings for the same cue (Weick, 1979). The

definition of a cue in this respect is equivalent to an idea or opportunity, oral or written

information, a physical artifact or a situation (Eric Brun, 2008). Eric Brun goes on to

define a framework model for the interpretation of a cue and how ambiguity is

introduced, Figure 4 (Conceptual Framework).

Figure 4: Conceptual Framework

In the Conceptual Framework model each line originating from the cue represents

the process of interpreting an opportunity or idea. The end result is multiple

interpretations of the opportunity leads to ambiguity. A model for Risk, Uncertainty and

Profit was developed by economist Frank Knight in 1921 commonly referred to as

“Knightian Uncertainty”. Frank Knight (Figure 5) argued that risk applies to situations

15

where one does not know the outcome of a given situation but can accurately measure the

odds of future outcomes. Uncertainty applies to situations where one cannot know all the

information that is needed to set accurate odds in the first place. In other words,

Knightian Uncertainty is simply unmeasurable risk.

Figure 5: Risk, Uncertainty & Profit (Frank Knight 1921)

From Knight’s model a range of possible outcomes exists for a single opportunity,

which leads to uncertainty regarding the payoffs of each outcome. For technology

companies it is important to shape, control and create favorable outcomes and therefore

reduce uncertainty. The same applies to generating opportunities. “When you create

16

opportunities, you essentially print lottery tickets.” (Terwiesch and Ulrich, 2009).

Terwiesch and Ulrich (2009) go on to state “there is little harm associated with a ticket

that does not win; the ‘winning’ tickets in your pocket are the only ones you care about.

These winning tickets are the exceptional opportunities that create the bulk of financial

value from innovation.” So how do you print more winning tickets?

Modeling the Fuzzy Front End

Many articles, books, models and publications have been written about project

management, new product development, innovation, and idea generation; however, a

concentrated effort to understand the fuzzy front end has been undertaken in earnest only

during the last 10-15 years. The “holy grail” would be to have a process or model

available that guarantees the best ideas move forward and more importantly generate

profits for the company. The very notion of a “process for innovation” can be considered

a contradiction given that innovation is fundamentally concerned with creating new

things. This researcher’s belief is that a common thread exists among all successful

innovative products and that at some basic level the methods used to select the best

opportunities can be standardized and possibly modeled.

Assuming corporations and small businesses are generating ideas, what model

exists to filter the best opportunities? A detailed literature review focused on screening

and filtering of ideas represents a deep void in the understanding of what opportunities

move forward versus which are shelved for a finite amount of time or indefinitely. A few

of the well-known fuzzy front end models have been reviewed to understand the overall

process and structure. The objective behind this review is either to utilize existing

models or develop a new model that can be used to select the best opportunities.

17

Khurana & Rosenthal Fuzzy Front End Model

“Towards Holistic ‘Front Ends’ In New Product Development” written by Khurana and

Rosenthal (1998) studies how strategy plays a role when selecting opportunities during

the fuzzy front end. Through 2001 Khurana and Rosenthal’s work has been considered

the most comprehensive study on the fuzzy front end (Koen, 2001). The authors studied

18 companies throughout the United States and Japan and the most successful companies

were able to link business strategy, product strategy and product-specific decisions. The

authors research showed new opportunities in the front end of innovation can be aligned

to the company’s strategy by means of the company’s processes or company’s culture.

These are great insights that can be used as part of a future model; however, Khurana and

Rosenthal do not fully develop their findings into a usable model.

The Khurana and Rosenthal model (Figure 6) pulls two concepts together that are

very important to take note. The first is opportunity identification and second is product

and portfolio strategy. The article and model go on to briefly explain tasks and

deliverables of the “core team” for both Phase Zero and Phase One. Phase Zero is

defined as follows “a) identify customer needs, market segments, and competitive

situations; b) perform a technology evaluation of current capabilities and requirements,

as well as the alignment with existing business and technology plans; c) identify core

product requirements; d) test the concept; e) specify the resources needed to complete the

project; and, f) identify key risks and challenges.” (Khurana and Rosenthal, 1998). For

Phase One, “the product and project both need to be defined and key project participants

and sources of required functional support need to be specified.” (Khurana and

Rosenthal, 1998).

18

Figure 6: A Stylized Model of the Front End of NPD (Khurana and Rosenthal, 1998)

The goal of this model was to demonstrate how strategy can influence processes

in the fuzzy front end. This finding is extremely valuable; however, the authors do not

explain what is considered a success in terms of metrics which are measurable and

specific to the fuzzy front end. In addition, the model provides very little guidance as to

what steps should be taken in cultivating a concept and does not explain potentially the

iterative nature of concept generation.

Koen – New Concept Development Model

In 2001 Koen published “Providing Clarity and a Common Language to the

Fuzzy Front End”, in which he introduced a new process model that captures the complex

nature of the fuzzy front end. Koen’s model expresses five major elements of the fuzzy

front end versus defining actual processes (Figure 7). According to Koen (2001),

19

“processes imply a structure that may not be applicable and could force a set of poorly

designed NPPD [New Product and Process Development] controls to manage the front-

end activities.” The underlying message driven by this circular model is that “ideas are

expected to flow, circulate and iterate between and among all the five elements in any

order or combination, and may use one or more elements more than once.”(Koen, 2001).

Koen (2001) wanted to emphasize that opportunities are expected to proceed in a more

“random and non-sequential fashion.” The outer black ring signifies influencing factors;

such as, internal business strategies and organizational capabilities, as well as, outside

factors including governmental policy, regulations, laws, distribution channels,

competitors and socioeconomic trends just to name a few.

There are three significant takeaways from this model. The first is that it

emphasizes the iterative nature required to model, remodel and transform concepts based

on analysis. The second observation is that the model identifies sources of external and

internal opportunities that can be used to generate ideas of higher value and more

importantly increase the likelihood of product adoption. Finally the third observation is

that the fuzzy front end should not be considered a linear process similar to well-defined

new product development stage gates. Opportunities in the form of concepts should have

the ability to mature over time reducing uncertainty by identifying the risks prior to being

introduced into the formal organization for commercialization.

20

Figure 7: The New Concept Development Model (NCD) (Koen, 2001)

Koen’s new concept development model (NCD) reinforces how the concepts of

influencing factors, idea genesis, idea selection, opportunity analysis, opportunity

identification, and concept & technology development are all tied together. It is not

enough for a company to do one component of the model well and it is up to the “engine”

(leadership and company culture) to drive all areas equally. The model does have its

short comings. Koen’s model does very little to describe guidance. As a comparison a

new product development/stage gate process has very clear objectives at the end of each

phase to help focus activities. In addition, no indications of cost, time and effort are

provided in an attempt to provide a strengthened concept to new product development

teams.

21

Glassman Control Model

In 2009 Brian Glassman published “Managing Idea Generation and Idea

Management in Order to Better Manage The Fuzzy Front End of Innovation”, in which

he introduces a linear process control model (Figure 8), akin to the new product

development process. One significant difference between the Stage-Gate model and the

Glassman Control Model is that Glassman recognizes the need for feedback control

loops, which help drive a specific measurable output. The output in this case is a

strengthened concept to be introduced into the formal organization for

commercialization.

Glassman proposes four main elements to be used in the control model. The first

is idea generation processes where companies can leverage internal or external sources

for opportunities. The author proposes that “idea generation should have a strong basis

in strategy.” (Glassman, 2009). In some cases this may mean no formal strategy at all

but rather a hap-hazard approach to idea generation where companies throw “stuff”

against a wall hoping that something sticks which in and of itself is a strategy. Glassman

defines three ways idea generation is influenced by strategy. The first is “strategically

driven idea creation” where ideas generated strongly align with the company’s overall

objectives. The second is “strategically influenced idea creation” where company

objectives are loosely defined and are influenced by internal or external forces as defined

by Koen’s new concept development model. The third is “strategy-less idea creation”

process where there is no underlying company strategy guiding or promoting idea

generation.

The second element to Glassman’s Control Model is screening and filtering of

ideas prior to being captured as part of a formal idea management system. Glassman

22

(2009) proposes that the degree of screening and filtering “can be considered to be a

control point which is both separate from, and inside the idea generation activities.” This

implies that screening and filtering is not confined to a formal “stage-gate” within the

model but that can be completed at any stage within the fuzzy front end.

The third element to Glassman’s Control Model is the development of idea

management systems and idea management. Idea management systems serve the purpose

of capturing, storing, organizing and screening ideas with the sole purpose of managing

ideas. Glassman proposes adding the concept of “tagging” ideas as part of idea

management. Tagging “is the act of attaching additional information to the idea, so that

it can be used to a) refine the idea generation and idea management process, as well as

b) aid the later innovation process by formalizing company biases.” (Glassman, 2009).

The benefit of tagging ideas allows for a peek into good or successful idea generation

processes and the conditions that created the ideas. Glassman (2009) states “it is

valuable to know what activities, people, and process produced particular type of ideas,

like incremental, or disruptive, market-drive, or customer-drive.” An observation and

possibly a benefit of tagging, is being able to track a company’s bias and how well this

bias matches company goals and objectives.

The fourth element to Glassman’s Control Model is diffusion and routing of

opportunities. Glassman (2009) best describes diffusion as “the act of taking anything

from a rough idea to a developed concept and spreading it around the organization so

that a) future development projects can be created from it or b) current or future

development projects can be aided by it.” The importance of this is that ideas that are

stored in the idea bank can serve as seeds to cultivate new opportunities. This is where

routing plays an important role along with the appropriate tagging of ideas. Glassman

23

asserts that the use of “idea management to route a disruptive idea to proper development

groups like skunk works, internal incubators, or spin-off-companies the company’s [own]

internal bias toward disruptive ideas can be avoided.” This is an important observation

given that ideas left unattended may never see the light of day or worse yet may be

scrapped for the apparent lack of value.

24

Figure 8: Glassman Control Model (Glassman, 2009)

25

Glassman’s Control Model does a great job of describing the major elements of

the fuzzy front end of innovation and providing some level of guidance for each process

module. The challenge is developing a model that captures the major elements of

innovation that is flexible and can be used by many industries. However, as Glassman

freely admits “neither idea generation nor idea management has any metrics defining

successful outcomes.” (Glassman, 2009). This is in large part due to a high degree of

variability between organizations and the perception of what a good idea really is. To

help add clarity ideas can be given a very specific set of attributes such as financial

potential, technical feasibility, resource requirements, which could be used to measure the

quality of the idea. This could be completed as part of the tagging process described by

Glassman or as a follow-on process step once the idea has been routed to the appropriate

individual or organization. It is at this point the opportunity could be further scrutinized

and evaluated for best fit into a company’s product portfolio. An additional limitation of

Glassman’s idea generation process is that one can have pre-screening and filtering

completed in the very early stages of idea generation. The danger with such an approach

is that companies may lose out on very good ideas or catalyst ideas that spawn new

opportunities. The best opportunities are rare or worst case sitting right underneath our

nose. While Glassman focuses a great deal of effort on sources of information similar to

Koen’s NCD model, Glassman does not provide any valuable insight with respect to the

number of ideas that should be reviewed. If corporations continually use the same

sources of supply and advocate a finite amount of ideas move forward, lackluster results

may follow. As a final observation and limitation of the Glassman Concept Model,

Glassman makes no reference to a company champion and the role that such an

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enthusiastic, passionate person may play in supporting, focusing and aligning early skunk

work activities.

Summary Findings

Modeling the fuzzy front end began with the intent to understand what

available models exist to describe pre-New Product Development activities. Table

2 identifies the main elements that describe the fuzzy front end along with a high-

level summary analysis of the benefits and limitations of each model discussed

above.

Table 2: Summary FFE Model Analysis

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Given that Brian Glassman (2009) has captured the main elements describing the

fuzzy front end of innovation it is not necessary to re-invent a new model but to simply

expand upon his work. Chapter 3 discusses a gap that exists within the screening and

filtering model along with governance of fuzzy front end of innovation activities.

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CHAPTER THREE Screening, Filtering, Management & Governance

Front End of Innovation Process Model

The intent of this study is to design a process model that describes the fuzzy front

end of innovation and ultimately addresses both research questions. “How do decision

makers identify either incremental and/or discontinuous innovations to distinguish

themselves from the competition and drive engines of growth?” and “What

organizational structure and toolset is required to support front end innovation

activities?”

A review of the current practices suggests that a suitable control model may exist

to manage activities within the fuzzy front end of innovation. As recently as 2009 the

Glassman Concept Model provides a reasonable process for defining and providing

guidance with activities that involve the fuzzy front end of innovation. The Glassman

model builds on early pioneers’ (Khurana and Rosenthal and Koen) attempts to study,

understand, document and explain the activities surrounding the fuzzy front end of

innovation with a detailed model. However, it is this researcher’s belief that a void still

remains that describes the process involved to filter and screen opportunities. This void

also exists within Glassman’s Concept Model. “Innovation Tournaments” by Terwiesch

and Ulrich (2009) provides the necessary guidance to properly filter and screen

opportunities, as well as, provide a basic framework for organizational structure. The

limitation of Terwiesch and Ulrich’s work is that it does not provide an overall process

model as depicted and described by Glassman and others.

It is clear that the first question cannot be fully answered using the Glassman

Concept Model but can be partially explained using Terwiesch and Ulrich’s (2009) work

29

on idea screening and filtering. The research proposal is to target a focused filtering and

screening framework that could be embedded within Glassman’s Concept Model. Figure

9 describes the current state of “Screening and Filtering” as defined by Glassman’s

Concept Model.

Figure 9: Glassman Concept Model – Idea Management & Idea Banks (2009)

30

Screening and Filtering Process Model

This section addresses the first research question: “How do decision makers

identify either incremental and/or discontinuous innovations to distinguish themselves

from the competition and drive engines of growth?”

To meet this goal, this section begins with a review of Terwiesch and Ulrich’s

work on “Innovation Tournaments”. Next, the major points of screening and filtering are

decided upon and a combined Screening & Filtering Process Model is proposed.

Innovation Tournaments:

At the core of Terwiesch and Ulrich’s work on innovation is the concept of

“Innovation Tournaments”. The authors propose that at the “most basic level, an

innovation tournament is a competition among opportunities, embodying the Darwainian

principle of the survival of the fittest.” (Terwiesch and Ulrich, 2009). Every opportunity

most be subjected to the rigor of multiple layers of review prior to being introduced into

the new product development process (Figure 10). The objective is to allow only the best

most exceptional opportunities to move forward and having the courage in many cases to

not back those that are only merely good.

The authors do provide guidance on how to conduct an innovation tournament. It

is important to understand that rules defined for one industry or company may not

necessarily apply to another. Likewise, the tournament is not absolute with respect to

how ideas are placed into the tournament. Some opportunities may meet the hurdles

imposed by previous rounds and be fast tracked through process. “In the first rounds of

screening, your filtering stresses efficiency. As the opportunities reach the later stages,

you shift the emphasis to accuracy.” (Terwiesch and Ulrich, 2009).

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Figure 10: Innovation Tournaments (Terwiesch and Ulrich, 2009)

Innovation Return Curve:

Terwiesch and Ulrich also introduce a second concept called the “innovation return

curve”, which graphically represents the expected profits from a set of innovation

investments (Figure 11). Creating an innovation return curve helps identify exceptional

opportunities that in turn drive value to the organization. The main components of the

innovation return curve are the following.

A. Required investment

B. Expected profit contribution

C. Profitability Index (ration of profit contribution versus required investment (B/A))

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D. Net profit contribution (expected profit contribution less the required investment

(B-A))

E. Cumulative profit contribution (Sum of all net profits of all the opportunities)

Table 3 and Figure 15 are examples taken from “Innovation Tournaments” that illustrate

the innovation return curve concept.

Table 3: Hypothetical opportunities for investment (Terwiesch and Ulrich, 2009)

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Figure 11: Innovation Return Curve (Terwiesch and Ulrich, 2009)

Each opportunity in Figure 11 above represents an investment option, whose

height is defined by the profitability index and width by the investment required. As part

of the underlying structure each opportunity is placed from left to right in order of

profitability. The areas above 1.0 and below the opportunities profitability index

represent expected profits to the company. This very basic model provides a snapshot

into, which opportunities should be pursued and “why increased spending levels typically

don’t lead to increased financial performance.” (Terwiesch and Ulrich, 2009).

“Successful innovation is not so much about choosing an appropriate spending level as it

is about shifting the innovation return curve to your advantage”. (Terwiesch and Ulrich,

2009). The key is to find more exceptional opportunities and properly categorize and

label the opportunities already in front of the company.

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Innovation Horizons:

While Glassman’s Concept Model provides detailed guidance on the tagging of

opportunities it does not provide an analysis of how tagged opportunities can fall in-line

with company strategy. Consider for example the idea management bank described by

Glassman filled with 2,000 potential opportunities. Which opportunity would have the

most market and technical relevance now versus 6 months, 1 year, 2 years, or even 3

years from now? Through a third and important concept of “innovation horizons,”

Terwiesch and Ulrich (2009) are able to capture the time relevance of opportunities by

adding an additional tag to the opportunity (Figure 12). This concept is particular

important given the finite amount of resources companies have and the current product

portfolios they should be managing. In addition, it helps capture the “aggregate risk that

a firm faces in pursuing an opportunity.” (Terwiesch and Ulrich, 2009).

Figure 12: Innovation Horizons (Terwiesch and Ulrich, 2009)

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Referring to Geoffrey Moore’s (2005) “A Broad Universe of Innovation Types”,

horizon 1 innovations can be classified as incremental by definition (Figure 13). The vast

majority of innovation projects fall into this category and there is nothing necessarily

wrong with pursuing horizon 1 innovations. With horizon 1 opportunities basic financial

modeling tools can be used to estimate the various financial outcomes for each

opportunity (Terwiesch and Ulrich, 2009). Given the relatively simplistic nature of

horizon 1 opportunities, it is reasonable to consider that with incremental opportunities

comes incremental growth. The challenge for management teams is that they must

unlock and realize the remaining potential of horizon 1opportunities. One generally has

an understanding of the risks and uncertainties associated with each project and possible

mitigation plans for each.

Figure 13: A Broad Universe of Innovation Types (Geoffrey Moore, 2005)

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Horizon 2 innovations are defined by the authors as next generation products

serving existing or adjacent markets. These are the opportunities that can be considered

the rising stars of the company that will, over time, become new core business. These

opportunities may require new capabilities and time to build; however, they extend the

company’s current competencies into new but related markets. Terwiesch and Ulrich,

(2009) will also add that when facing horizon 2 opportunities companies are faced with

scenario uncertainty. For example, will the government regulate alternative renewable

energy? Will telecommunications companies regulate cell phone bandwidth and limit

streaming of video conferencing data? Will the semiconductor industry migrate from

300mm wafer size to 450mm wafer size?

With horizon 2 opportunities the average employee or customer may not have the

ability, resources or time to evaluate new opportunities to determine their full potential

for commercialization. It is clear that when evaluating horizon 2 opportunities the

techniques that are applied to horizon 1 evaluations need to be further refined or

developed.

Horizon 3 innovations are often described as breakthrough innovations or

“disruptive innovations” as Geoffrey Moore (2005) describes in his model of innovation

types. As Terwiesch and Ulrich describe in Figure 14, these opportunities are not directly

related to existing markets or technologies, but rather represent new products or services

which are not extensions of existing ones. They should be considered nascent business

ideas and opportunities that drive future growth engines or escape velocity by changing

the nature of the industry. These are types of ideas and opportunities that many business

leaders seek. Historically research and development engineering teams have focused on

just this sort of innovation. Some companies may use the terminology “skunk works” or

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core development teams. These teams often involve individuals with extremely deep

experience of the business and environment in partnership with researchers that may have

limited business experience but equally deep knowledge of the technologies. In theory,

successful research and development should incorporate failure as the norm rather than

the exception to the rule. The best possible tool set used to analyze horizon 3

opportunities will still leave many unknowns about these potential opportunities.

The horizon map is useful given that developing innovations for each horizon

category typically requires a different approach with respect to market and technical

uncertainties. Although, outside of the scope of this thesis it is quite reasonable to

assume that many organizations co-mingle all three types of opportunities within a single

innovation organization. This could explain why many organizations are not satisfied

with the results of their innovation activities, as they may be confusing their goals and

their approaches to innovation. The benefit of tagging opportunities with horizon tags is

that it provides a model between vision and reality. In addition, it serves to drive a strong

distinction between innovations that serve to prolong the status quo versus those that

serve to drive the company vision and ultimately escape velocity. (Terwiesch and Ulrich,

2009).

Required Elements of the Screening and Filter Process

While the Glassman Concept Model builds on efforts from Khurana and

Rosenthal (1998), as well as Koen (2004) to describe the Fuzzy Front End of Innovation,

the Glassman Concept Model lacks the extra definition in the form of guidance required

to select the most exceptional opportunities. How does one determine which opportunity

moves forward and what toolsets are required to screen and filter ideas? Using the

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Glassman Concept Model as the starting point a new front end of innovation model is

proposed. The elements of this model are described below.

Strategic Goals and Objectives must be defined for the company. The

importance of this cannot be overemphasized as it provides an organization and its

employees a basic understanding of the company’s vision and purpose. From an

innovation perspective a strategic plan is formulated in line with the company’s goals and

objectives but as equally as important in line with the company’s organizational and core

capabilities. Having a strategy assists greatly with the filtering and screening of

opportunities according to well-defined strategic criteria or possibly helps identify

specific areas for innovation to target. Consider the following questions as part of the

company’s analysis.

1) Who are your company’s target customers or market segments and why do

they buy from you?

2) What products or services do you offer and how do they differ from the

competition?

A basic toolset can be used to answer the above questions and may be as simple

as developing a technology map of relative positions, defining what the company’s core

competencies and capabilities are, defining industry technology life-cycles, analyzing the

value of products relative the completion or simply purchasing market research.

No matter the method used to develop and define company strategy; the selected

opportunities should be in alignment with strategic goals and objectives.

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Ideation as defined by Glassman tends to be ideas and opportunities planned

through defined brainstorming or ideation activities. While this thesis paper is not about

the activities required to develop the best ideas, idea banks or even idea management, it is

important to consider that the best ideas will not surface from just having planned events.

Opportunities to invent happen at every moment between simple interactions that

business people and consumers have with each other or amongst themselves to solve

problems. Recognizing this possibility requires modifying the Glassman Concept Model

by adding “Unplanned Ideation”. Consider the following from Terwiesch and Ulrich

(2009):

1) Most organizations have employees who work at the edges of the company rather

than at the core, where innovation is generally believed to happen.

2) In order to have a better chance at finding the best opportunity we must consider

a larger mouth to the funnel.

The best game changing opportunities are rare. As Terwiesch and Ulrich (2009)

describe in Innovation Tournaments, “you’ll generate more exceptional [opportunities] if

your process exhibits greater variability. Generating wacky ideas and wild notions

increases the chance that at least one of your opportunities’ will be exceptional.” This

last notion bears repeating. It is particularly important the most exceptional ideas have

the highest variability. This concept is described further as part of the screening and

filtering process for opportunity generation.

Storage and categorization does not take on a new meaning in the newly

proposed ALAS model compared to that of Glassman Concept Model. However, outside

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of the method used to capture and store ideas the premise is to keep things simple in both

execution and in cataloging of ideas. Glassman (2009) provides the definition of tagging

as “the act of attaching additional information to the idea, so that it can be used to a)

refine the idea generation and idea management process, as well as, b) aid the later

innovation process by formalizing the company biases. The analogy to tagging of an

idea can be compared to that of the exchangeable image file format (EXIF) found as

embedded data on digital photographs. For the savvy user information with respect to

the manufacturer and model of the camera, firmware revision, exposure time, focal

length, exposure bias, etc, can be found freely. The end result allows for tracking and

best practices given certain activities or lighting scenarios.

Similarly to the EXIF example, the benefit of adding attributes to ideas is that it

enables the company to improve upon good or successful practices and reproduce

conditions or events that generated the most exceptional opportunities. There is great

value in knowing what “activities, people, and process produced particular type of ideas,

like incremental, or disruptive, market-driven, or customer –driven” innovations.

(Glassman, 2009)

The objective is not to add red-tape or bureaucracy to activities close to the

ideation phase. The intent is to streamline the tagging process proposed by Glassman (24

questions) and only utilize the following five questions. Organizations should keep in

that mind modifying tags as needed may be required to accurately describe their business

environment. The screening and filtering of the opportunity will deep dive as necessary

to flush out additional attributes.

Source Tag Info

o Who generated the idea?

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Event Tag Info

o What event triggered the idea?

Tags Related to the Idea

o What category is this idea (product, service, process, marketing,…)?

o Is this idea considered disruptive or incremental?

o Who else is aware of this idea?

Screening and filtering, as previously mentioned, is not entirely defined by

Glassman. Glassman’s focus is to improve idea management and idea generation as part

of the fuzzy front end of innovation but falls short on tools that can be used to improve

the screening and filtering process. By combining concepts proposed by Terwiesch and

Ulrich (2009) and Frank Knight (1921), uncertainty of opportunities can be reduced and

in addition the most exceptional opportunities can be identified.

To begin, it is important to restate the first thesis question. “How do decision

makers identify either incremental and/or discontinuous innovations to distinguish

themselves from the competition and drive engines of growth?” To answer this question

guidelines are proposed to identify, classify and ultimately develop the most exceptional

opportunities.

Filtering and Screening Guideline:

1) Restate clearly and succinctly the company’s strategic goals and

objectives

a. This should include the company’s core capabilities

2) Define the format of the initial round of the “Innovation Tournament”

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a. Define the rules, the number of rounds and time duration

b. Define the templates that ALL participants must utilize

i. Consider simple problem objective statement and

solution statement along with any relevant pictures.

ii. Limit to one slide. Analogy would be 30 second

elevator pitch to CEO.

3) Define the second round of the “Innovation Tournament”

a. Utilize strategic risk analyzing techniques to describe the

problem (decision trees, probability models)

b. Utilize financial models capturing sales volume, price, cost of

goods of sold, required investments, discount rate, and timing

of cash flows.

c. Utilize main elements of the innovation return curve

i. To be used by evaluation teams to chart at a later point.

d. Identify the technical and market uncertainties with

explanations and mitigation plans for each.

i. Attempt to know the unknowns.

ii. Note: Not being able to identify the uncertainties for

H3 opportunities should not be considered a failure of

the process. This simply means there is more

variability in the opportunity, which may lead to an

exceptional opportunity.

e. Identify the technical and market risks with explanations and

mitigations plans for each.

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f. Identify what core capabilities are required to be successful

4) Define the third and final round of the innovation tournament

a. Craft a mini-business plan for each remaining opportunity.

i. Consider 7 (Figure 14) slides and time duration

Figure 14: Example format Mini-Business Plan

5) Compile Screening and Filtering results

a. Assemble the Innovation Return Curve to target opportunities

with best profit potential for the organization

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b. Using the concept of innovation horizons, bucket the

opportunities into 3 distinct groups based on the having the

most market and technical relevance now versus in the future

as defined by the company’s strategic goals and objectives, as

well as, resource capabilities (human capital, money, core

capabilities)

Diffusion and Routing is defined by Glassman (2009) as “the act of spreading

the ideas and opportunities through the organization, and routing is sending a particular

idea or opportunity to the most relevant individuals.” This concept does not

fundamentally change within the new model, but does take on a slight variation. The

concept of Innovation Horizons described by Terwiesch and Ulrich (2009) allows

management teams to label the outputs (innovation portfolio) of the screening and

filtering process with H1, H2, and H3 tags. By doing so, automatic routing of these

labeled opportunities allows development teams to solely focus on either incremental

(H1) or disruptive/discontinuous (H2/H3) innovations.

By clearly defining which opportunities are considered H1 incremental

development opportunities, skunk works or “core” development teams can be left alone

to quickly flush out concepts or continue to incubate ideas surrounding H2 and H3

opportunities, without the need for business red tape. The proposed model should make

a distinction as to how H1, H2 and H3 opportunities are routed to emphasize proper

organizational handling of the opportunities. Considering that H1 opportunities are

incremental in nature, Table 1 would imply that these opportunities are commercially

highly certain, budgeted, believable, milestone driven and require the implementation

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only available through a multi-function team (engineering, manufacturing, product

marketing, sales, legal, product support, etc.). In other words, H1 opportunities require

the discipline of the organization to implement quickly through a defined product life-

cycle process.

Process Check is clearly defined by Glassman (2009) as “the control model for

idea generation”. The meaning of this may not be clearly evident but with all good

control models it is important to gauge the quality of the output with respect to the input

parameters. If we think of the Fuzzy Front End as an endless pool of opportunities, it is

important to characterize how well the company is doing with respect to selecting the

most exceptional opportunities and improving the overall process.

Glassman (2009) proposes the following two questions to help aid the process

check stage.

1) Are the ideas being created by the idea generation process meeting their

preset goals?

2) Are the ideas being captured from external [and internal] sources meeting the

preset goals set for capture of external [and internal] sources?

The first question primarily focuses on the quality of the idea as it relates to

strategic goals and objectives. With a well-defined set of strategic goals and objectives

the organization and its employees have a basic understanding of the company’s vision

and purpose. From an innovation perspective a strategic plan is formulated in line with

the company’s goals and objectives but as equally as important in line with the

company’s organizational and core capabilities. Having a strategy assists greatly with the

filtering and screening of opportunities according to well-defined strategic criteria or

46

possibly helps identify specific areas for innovation to target. This feedback loop should

not be considered as part of the “unplanned ideation” phase. The source of ideas may

originate from customers or contractors not at all familiar with the company’s internal

strategic objectives.

The second question speaks to the source and volume of ideas. Are more ideas

generated internally or externally? If internal idea generation processes are failing, it

may require the organization to rework its core idea generation process. This may be a

function of the number of ideas the organization limits itself to. Consider a larger funnel,

meaning consider more ideas that are allowed to enter the process. This is analogous to

the lottery example described by Terwiesch and Ulrich (2009). “When you create

opportunities, you essentially print lottery tickets.”

As a final consideration, the evaluation of and selection of individuals within the

process model is important. Glassman (2009), as well as, Steven, Burley, & Divine

(1999) mention that certain personality types find great satisfaction and even joy in

analyzing and distributing of ideas while others may not be as engaged with the process.

It is important to consider who is chosen to participate and for what reasons.

ALAS Front End of Innovation Process Model ©

With a thorough understanding of the Glass Concept Model for the fuzzy front

end of innovation in place and guidance from Innovation Tournaments by Terwiesch and

Ulrich, the ALAS Front End of Innovation Process Model is proposed below in Figure 15.

The proposed screening and filtering process model is focused on providing greater detail

to the overall Glassman Concept Model for the fuzzy front end of innovation. This

47

researcher’s belief is that by adding such a model companies can find the most

exceptional opportunities to develop and commercialize.

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Figure 15: ALAS Front End of Innovation Process Model©

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Management & Governance

This section addresses the second research question: “What organizational

structure and toolset is required to support front end innovation activities?”

To meet this goal, this section begins with a review of Terwiesch and

Ulrich’s (2009) work on “Innovation Tournaments”. Next, the major points of

management and governance are decided upon along with high-level guidance.

A theme that is now prevalent within the literature is that the fuzzy front end of

innovation can be defined as a process. Khurana & Rosenthal (1998), Koen (2004), and

Glassman (2009) have attempted to define the innovation process and provide models

that could be used to navigate the fuzzy front end. However, a detailed analysis of

management and company culture is not explored fully within each model. We have to

remember that innovation is not hoping for serendipity or counting on inspirations for the

heavens. Therefore it is important to consider how organizational decisions must be

made for any innovation process to be successful.

Terwiesch and Ulrich (2009) describe four areas that are needed to help define the

organization and governance of the innovation process.

1) How will you configure your organization to generate, sense, and evaluate

opportunities?

2) How much will you centralize innovation?

3) Will you foster competition among opportunities in your tournaments?

4) How will you shape your corporate culture of innovation?

To answer the first question Terwiesch and Ulrich’s (2009) research supports that

companies typically generate organically about 50% of their opportunities internally

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through research labs, serial innovators or through the sales force working with

customers. The remaining 50% of opportunities are generated inorganically either

directly from customers, universities, or lead users. Terwiesch and Ulrich (2009) propose

that if you combine two questions “Where are your opportunities created?” and “Who

makes your selection decisions?” one can end with four possible organizational

configurations for the fuzzy front end of innovation (Figure 16).

Figure 16: Organizational Configurations for the Innovation Process

From Innovation Tournaments (Terwiesch and Ulrich, 2009) these four major categories

are described as follows.

Integrated Innovators: Innovation happens with the firm, often within a single

department. This approach assists in the construction of an efficient portfolio and

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enables the innovator to target its effort where they best support its strategic

direction. The process can be tightly controlled.

Experimentalist: These innovators rely on direct market evidence for selection.

They generate opportunities internally but test them in public forums and markets

to determine which ones deserve further investment.

Publishers: Publishers excel at selecting rather than creating. They leave

opportunity generation to others and count on their insight into consumer

behavior to inform their selection decisions. Publishers can exist outside of the

media industry.

Innovation Hosts: Innovation hosts such as YouTube and Newgrounds neither

need to generate ideas nor make difficult decisions. They simply serve as

marketplace where innovation germinates.

What organization configuration one selects varies with the company and industry

that it is in. As the authors likewise point out not one of the organization setups is either

right or wrong. The area the company selects depends on the capabilities of the firm, cost

structure and environment that it is in. As strategic goals and objectives or new business

units are created it may be possible to branch out into other areas.

“How much will you centralize innovation?” This topic may be a moot point for

small companies and start-ups who typically manage a small product portfolio. The

question becomes much more relevant for larger companies that have to manage broad

and potentially diverse product lines and portfolios. The authors identify three ways a

company can either choose to coordinate or not innovation activities. The first is

“centralization” of activities, which drives efficiency by having a core innovation team

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develop and refine the opportunity that is later rolled out to the business units for

commercialization. All filtering and “experimentation happens centrally, and new

products flow outward.” (Terwiesch and Ulrich, 2009). The second method to coordinate

activities is to “decentralize” control from the core group and allow business units or

individuals the freedom to experiment. The main benefit of this approach is that lead

innovators are potentially closer to the customer and able to sense and react quickly to

customer needs. Decentralization enables quick experimentation and may generate many

opportunities but will also promote inefficiency resulting in wasted capital and company

resources. This approach may allow for business unit to business unit collaboration

especially if communication methods are timely and easy to use. The final approach the

authors describe is a “hybrid” between centralized and decentralized control. It would be

left up to the organization to decide at what times during the business cycle it would need

to be more flexible. A “dynamic response to changing conditions is important”

(Terwiesch and Ulrich, 2009) which may lead to a competitive advantage by being closer

to the customer allowing for quick experimentation. Inefficiencies in the process are

disregarded and it may be at these crucial times costs are somewhat ignored in favor of

market share gains or market neutralization efforts.

“Will you foster competition among opportunities in your tournaments?” Glassman’s

Concept Model provided details regarding idea generation processes by defining sources

of ideas, events and idea generation activities ultimately concluding with screening and

filtering of the best ideas. Terwiesch and Ulrich (2009) add to this concept by advocating

“Innovation Tournaments.” The objective here is that “most tournaments include a mix

of absolute quality hurdles, which opportunities must clear, and relative comparisons in

which the best opportunities in a group are selected for further development.”

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(Terwiesch and Ulrich, 2009). The importance of this concept is that relative

comparisons allow for companies to compare opportunities with products currently in

their product portfolio. In addition, advocates for the ideas being submitted have a

relative comparison (product sales performance and customer adoption) and should be

able to communicate the clear advantages of the proposed opportunity. The point here is

not to add red-tape to the innovation process but to have a clear understanding of your

opportunity portfolios. In theory the best opportunities should quickly pass over absolute

quality checkpoints.

“How will you shape your corporate culture of innovation?” Khurana and

Rosenthal (1998) describe the importance of company culture as part of their work

“Towards Holistic ‘Front Ends’ In New Product Development”. They found this to be

especially true of Japanese company’s unlike US based companies who preferred to have

well defined formal process in place. Khuarana and Rosenthal’s research showed that for

the Japanese culture “subtle control” is important “because new product development is

inherently complex due to the ambiguity and variety of options, and is made further

difficult because of the turbulence in the external environment.” (1998). To overcome the

challenges of opportunity generation “key considerations, business vision, technical

feasibility, customer focus, schedule, resources, and coordination” (Khurana and

Rosenthal, 1998) were always on the mind of these key individuals and Japanese

organization as a whole.

To obtain similar results Terwiesch and Ulrich (2009) propose that if company’s

care about innovation it must start with the top down. Executive managers and

management teams must have the meaning of innovation clearly and communicated to

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their staff. The authors define key areas to consider when establishing a corporate culture

of innovation.

Celebrate Failure: Making a mantra out of the slogan “Fail early, fail often, and

fail inexpensively” will remind your employees that every good innovation

tournament entails failure. Halting an unpromising investment is one of the most

important actions a person can take. It allows resources to be applied elsewhere.

Doing it early can save millions. Yet people tend not to celebrate the termination

of bad investments.

Provide Incentives for contributing opportunities: Do not advocate paying high

bonuses to individuals whose job is to generate the new ideas. Encourage

individuals in the company whose job is not to generate ideas to speak up by

rewarding them with small gifts.

Recognize Champions: Personal passion and advocacy that challenges the

judgment of the majority is valuable.

Terwiesch and Ulrich (2009) provide a few other notable methods for creating a

company culture of innovation.

1) Transparency rules for innovation tournaments. To help ensure transparency

in the process and eliminate “pet projects” participants are asked to submit

ideas using the same format and template. The rigor placed on each idea must

be the same.

2) Physical arrangement of the workplace can help foster innovation and spark

idea generation discussion among groups. Having product managers, sit with

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engineering, materials, sales and manufacturing groups can foster

collaboration.

3) Recruiting practices and how companies recruit employees can help drive a

change in company culture. As the authors so clearly put it “Your people are

your culture.” (Terwiesch and Ulrich, 2009).

4) Slack Time. Allowing a certain percentage of your employee’s work week

dedicated to pursue ideas that pique their interest.

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CHAPTER FOUR Research Methodology

The purpose of this study is to answer the research questions as follows. First,

can the proposed screening and filtering model aid in the understanding and selection of

the most exceptional opportunities? Second, does the proposed model capture the

required factors needed to select the most exceptional opportunities?

Limitations of this Study

Opportunity management coupled with screening and filtering processes are

poorly understood. There are no complete models describing the functions of idea

generation and opportunity screening or for that matter metrics to compare a successful

process relative to and uncontrolled process. This is in part due to one company’s

definition of a successful opportunity generation process and how it can vary widely

between companies let alone industries. Attributes used between companies such as

return on investment, return on assets, resources, market uncertainty, technical

uncertainty, etc. are not always consistent. In addition, company products may in fact be

services and not physical products that consumers can utilize.

The output of idea generation processes can be generally measured in terms of the

quality of the idea, the quantity of the ideas or the attributes of the idea. A second

measure of success can be measured over time. As the ALAS Front End of Innovation

Process Model is defined, will a set of given inputs drive the expected output? This is the

motivation behind this study.

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Sample size required to support this sort study was limited to 6 individuals.

Ideally a large sample size would be required to properly capture all the nuisances and

best practices for screening and filtering of ideas over a number of industries. A

substantial amount of time on the part of this researcher would be required to conduct the

interviews and analyze the responses.

Study Type Selected

Interviews will be used to support the proposed model. Selected interviewee

candidates will be asked a series of questions regarding the fuzzy front end of innovation

and be directed to the Screening and Filtering stage of the proposed model. It is

proposed that interviews will provide the best source of information for how technology

companies explore, quantify and manage ideas prior to the new product development

process. Face-to-face interviews will help:

a) uncover any additional missed points of control as experienced by seasoned serial

innovators or entrepreneurs

b) demonstrate if the model accurately represents the fuzzy front of innovation with

a specific focus on screening and filtering of opportunities

c) demonstrate if the model accurately represents governance

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Interview Pool

Sources of potential participants will be limited to lead users, serial innovators or

managers in high-tech companies. In addition, participants will be selected based on

their contribution to the fuzzy front end and organizational decision making.

i. Must be actively involved with the management, selection, development or

design of technology (software, hardware, small or large)

ii. Must have one of the following job functions. Titles may vary however

overall job function as it related to innovation is important.

1. Entrepreneur

2. R&D Manager, Project Manager

3. President, Vice President or Division Managers

4. Fellow/Technologists/Engineer, Advisor

iii. Must have an understanding of the product development process and

commercialization of products

iv. Demonstrate or articulate the phase-gate process for their respective company.

Interview Structure & Questions

In-depth interviews were used to evaluate how serial innovators and innovation

managers select the most exceptional opportunities when navigating the fuzzy front end

of innovation. They were asked to provide in detail all aspects of a single highly

successful project brought to market from its initiation through formal introduction into a

new product development process. Participants were given latitude in describing their

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projects in order to develop a richness and depth in understanding their experiences.

Questions were primarily focused on understanding the genesis of the idea, identifying

market and technical uncertainties and intensity of planning & communication during the

early project stages (Appendix). They were then asked to provide information on a

second failed project for which they held primary responsibility. Again the same line of

questioning was geared towards understanding the genesis of the idea, identifying market

and technical uncertainties and intensity of planning & communication during the early

project stages. In essence, why did the project fail given the same toolset described as

part of the successful project?

Finally a series of pointed questions were asked of the participants regarding specific

elements of the fuzzy front end not found as part of the described models. The objective

was to document “gut feelings” and any additional tools utilized by these practitioners to

uncover the most exceptional opportunities.

i. Finding the “right” problem

ii. Completely understanding market and technical uncertainty

a. Reducing ambiguity

b. Reducing risk

iii. Circling between the perceived right solution and ensuring they understand

the problem

iv. Alignment of idea generation with company strategy

v. Alignment of idea generation with portfolio management

vi. Methods used to screen and filter concepts

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Interview participants were not provided with any questions ahead of the

interviews but were merely introduced to the topic in a general context.

Study Type Not Used

A survey model was considered as part of the design due to its numerous

advantages but ultimately not selected. Advantages and considerations of a survey model

are shown below.

a) can capture a wide target audience

b) can generate quantitative and qualitative data on best practices, perceptions and

needs

c) economical and efficient

d) possible broad acceptance of model

However, for items defined as part of the interview process, refinement of the

proposed model can be quickly validated with serial innovators through detailed

discussions. Capturing tacit knowledge is not easily accomplished through a survey and

can easily lead to missed points of control.

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CHAPTER FIVE Findings, Limitations & Further Research, and Conclusion

The purpose of this thesis was to develop a general methodology in the form of a

process model to guide organizations through the Fuzzy Front End of Innovation. To

date, most opportunity selection approaches use elements of various models or strategic

decision making toolsets to evaluate opportunities but lack controls to manage these

activities analogous to product lifecycle management or Stage-Gate processes. A second

purpose of this study was to define the organizational structure required to incubate

opportunities. To help achieve the purposes of this study, a questionnaire was developed

to uncover “gut feeling” and nascent tendencies of successful serial innovators,

entrepreneurs and managers in charge of innovation hubs. This chapter will be used to

examine and summarize key findings that were uncovered as part of the interview

process, as well as, proposed modifications to the ALAS Front End of Innovation Process

Model. In addition, recommendations for future research work will be provided.

Findings

The main qualitative data set for this study was taken from direct one-on-one

interviews with serial innovators, entrepreneurs and managers responsible for or

participating in innovative opportunities within their respective companies. From the

transcribed data set the following themes emerged: (a) Understanding of customer

friction (“pain points”); (b) Fail fast, fail early, fail cheaply; (c) Framework used to

manage or distinguish between opportunities; and (d) Opportunity generation and

opportunity management. No common theme could be extrapolated for Organizational

Structure; however, some details were provided by one respondent concerning his

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organization. In addition, a summary table is provided following the results that

summarizes the common and divergent themes between respondents.

Customer friction points (“pain points”) were identified as a main source of

opportunity generation. As one participant concluded opportunity generation could be

recognized at the intersection of “personal pain and market opportunity.” For at least 2

of the 6 participants this was described as a personal motivation that could bring about

better understanding. For the two respondents it was important to validate their

understanding of the problem through the eyes of the customer. While no formal

customer needs analysis was completed they both spoke to a gap in the literature as

defined in Table 2.

“My motivation was personal…there are some people that say, where

can I make the money? I look at it as where am I feeling personal pain?

What is a problem that I am personally invested in and that I then

understand? And then apply a solution to that. Like most people, I feel

personal pain in about a thousand interactions a week.”

“I pick problems I think I can solve and I will enjoy overcoming the

problem and finding the solution. I go after things I’m excited about or

else you won’t have the drive to see it through. It’s has to be something

I’m passionate about. It’s got to make a breakthrough

product/technology…not something incremental. Breakthroughs with

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high margins and things that no one has ever seen before, things that

solve the biggest problems.”

“You have to walk the line of your customer to fully understand.” ”I am

on the lookout for their pain points and help me deliver for them better.

I’ll be able to empathize with them. When you don’t immerse yourself in

the space, you’re going to fail.”

“I constantly ate my own dog food. I shopped on my own site with my own

money and I was always pissed off with myself. We’re the worst

experience ever…you find yourself to be the constant target. Be your own

customer!”

“The customer of my customer is my customer!”

Failing fast, failing early and fail cheaply was a common theme among for all 6

participants. In some cases this theme could have been considered a mantra for the

organization. Building prototypes was agreed by all the respondents as a key to success.

This observation was in line with the prototype testing element described in Table 2.

Consider the following statements made by various respondents.

“The key to success in my mind is driving an effective cycle of

learning. From the first experiment you run to see if the idea holds

any water, to the point that you can turn the product, you have

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many cycles of learning. When my team comes up with an idea, I

say, you need to measure in weeks from the idea to the first

experiment…not months and not years. Cobble something

together, do some experiments with some modeling but do

something with it, otherwise it will sit on a shelf and collect dust.”

“For us it’s test things as fast as you can and as quickly as you can

through prototypes and being willing to go spend $500-1000 to

send [customers] to a site even if we don’t know if we’re going to

make money off of it to see how people behave on the site so then

we have some idea of how to actually make money off of it.”

“Come as close to the final form, not function factor and take that

to the target customer.”

“Get something we can show to our customers to determine if it’s

viable or not via a beta version and invite customers to participate

in that or even usability testing with not fully functional software

or web apps but we spend a lot of time going through good flows

and mock-ups with a camera on their computers watching their

activity.”

A framework used to manage or distinguish between opportunities took on

many different meanings for the respondents from highly important, to wanting to have

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diversity in their product portfolio, to not playing a significant role as part of the idea

generation process.

How is concept (idea) generation aligned with portfolio management? This

question was asked of all participants to understand how innovators manage and

distinguish opportunities. The underlying assumption is that portfolio measurement

systems are required to evaluate and balance the various innovation efforts across market

and technical horizons. Participant answers to this question are summarized below.

Prove out and have a self-sufficient product line that will allow for

future product expansion. “Create a minimal viable product”

Well defined “core capabilities and platform first”, which then

allows for creating diversity in the product portfolio. “We want

something that looks a lot like what we have but creates diversity.”

A coupling of company objectives, with product roadmaps and

product development. Leeway to work outside of what the

company is not currently focused on, “especially when going after

a completely new user base which may not even be a user base that

we want to grow strategically but is an opportunity for additional

revenue we think down the road. We’re in a little bubble where we

are free to explore these things.”

No need for “portfolio analysis early on in idea generation

because you don’t know if you are defining a new segment”.

However, early evaluation of the opportunity is required and is

“what some companies call sustainable differentiation. So you

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don’t know the entire market potentially but does this idea have

real differentiation and is this sustainable over time so you can

actually make money on it?”

It is important to note that only two of the six respondents were able to verbalize

and provide a basic model for innovation and product portfolio management for their

particular organization. The first respondent described a process extremely similar to that

of the Terwiesch and Ulrich (2009) involving Innovation Horizons as described

previously in Figure 14.

Given the size of the organization and market focus it was important for the

second respondent to have an understanding of product seasonality. One peak and one

trough would not be enough to sustain the company throughout the year. As shown in

Figure 17, the second respondent described a seasonal sales product mapping model that

analyzed the sales contribution of each product offering throughout the year. The

objective was to analyze the gaps in the overall product portfolio and focus innovation

efforts to fill those gaps.

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Figure 17: Seasonal Sales Product Mapping Model (Respondent #6, 2011)

Opportunity generation and opportunity management had for the most part

one common theme, closeness to the customer. While the answers varied from company

to company and industry to industry, all 6 respondents articulated the need to work close

with the customer in an effort to understand potential market opportunities.

A second theme was the diversity in answers when participants were asked to

respond to questions related to finding the right problem and capturing and screening of

opportunities (Appendix - Questions 3.X & Questions 8.X). It is conceivable that the

diversity in respondent answers is in large part due to the size of the organizations and

industries they serve. However, it was clear that for at least two respondents working

within smaller companies with limited product offerings; the focus was not necessarily on

continued opportunity generation and opportunity management but rather marketing and

being successful with their current products.

Analyzing the remaining respondent data allowed for the following takeaways.

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1. Recognize the value of idea fragments.

2. Understanding of market trends (sales conferences, expos,

technology seminars, opportunities to leverage cost pressures,

technology market trends and steering group technology roadmaps,

etc.)

3. A formalized innovation group with the charter to identify

discontinuous innovations.

4. Developing a culture of innovation inclusive of the entire

organization.

5. Capturing of ideas using formalized web-based processes or Post-

it® notes on a hallway “idea tree”

Recognize the value of idea fragments, takeaway #1 above, was particularly

important for several of the respondents. Consider the following respondent information

with respect to Opportunity Generation.

“My thought process a lot of the time is ‘here is a new technology

that I’m learning, is there anything I can solve with that new

technology? Is there anything I can solve with that’? My mind

tends to run to these things I enjoy doing.” “Which [idea] should I

pursue? Is this patentable? Is this breakthrough? If things start

lining up this way, then I start going down that path and start

driving.”

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“The breakthrough with this [idea] came out of exploring some

new technology that helped us develop the new software. There

have been several times where myself and other developers are

into a new technology and because of their passion are learning

for work around the clock and being paid the same salary.”

“Of course you need to be aware of what technologies are out

there and what ideas are out there to implement these sets of

platform capabilities to enable you to compete and win and

differentiate your product.” “These are the Lego pieces to build a

better structure.”

A Culture of Innovation, takeaway #4 and Opportunity Management, takeaway

#5, were described by only one respondent. The process involved the capturing of

internal company ideas inclusive of all employees within the organization. It is clear that

for this particular company that they not only embrace ideas generated by core employees

whose job function it is to generate new ideas but encourage individuals in the company

whose job is not to generate ideas to speak up. The opportunity management described

by this respondent allows for product managers within the organization to not only have

access to a singular database of potentially hundreds of ideas but also respond to ideas

that “bubble up to the top.” The company allows for complete employee transparency

and peer review of ideas with a built-in mechanism to vote ideas up or down. In

addition, keeping in-line with company culture, each product manager must develop at

70

least 3 ideas from the idea bank. To further refine the idea, the organization has a

simple set of best known methods to evaluate the proposed opportunities.

Is this idea high value or low value? Will this be of value to the

existing customer base? Will it help drive new customers to the

company?

Is it easy to implement or hard to implement

Review of opportunity costs

Comparison against product roadmaps

Compare against strategic goals and objectives (a big splash in a

certain space)

Once the above criteria are met and opportunities prioritized funding is provided

to develop the idea into functional or non-functional prototypes.

Organizational Structure was an area that did not yield a set of common themes.

This was in large part due to two factors. The first was a limitation of the questions

posed to the respondents. No specific question asked of the respondents focused on the

organizational structure required for innovation. The second limitation was the size of

several of the respondent’s organizations. In some cases the organizations were very

small and focused on the success of a singular product that was the result of an innovative

idea.

Of the respondents interviewed only one organization had an “Innovation Lab.”

The following are some attributes associated with this innovation group.

Focused on big and small innovations

Not fully staffed or officially funded

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Time is volunteered and projects are considered short in duration

Flexibility to pursue ideas that can potentially make a “big splash”

Parent company has a similar innovation lab focused on only disruptive

innovations. Objective is projects with 8 to 9 figure return

Company culture is important in fostering innovation

When it comes to innovation, stress that everyone is on equal footing

and “leaving ego at the door”

Summary Findings

Respondent answers to Appendix questions are summarized in Table 5 and

is useful in identifying similarities and differences between the 6 respondents

interviewed. Given that the 6 respondents each worked for varying size

companies and technology groups the evaluation of the responses is made relative

to the importance given by said individual or company they work for. In other

words, was there a specific element present within the boundaries defined by the

fuzzy front end of innovation that was of relative importance to the respondent?

The following legend (Table 4) is provided to provide clarity to the table values.

Table 4: Legend – FFE Elements Respondent Answers

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Table 5: Fuzzy Front End Key Elements versus Respondent Answers

73

Key findings discussed in the previous sections are also highlighted (GREEN) in

summary Table 5. These elements can also be found as part of the final control model

(Figure 18). In addition, there were numerous key differences (RED) between the

respondents that were also addressed by the final control model. For example, Product &

Portfolio Strategy had varying importance to respondents; however, is captured as part of

the final control model as an element considered in the screening and filtering process.

Varying responses in large part had to do with the size of the company. Larger

companies were interested in having a diverse product portfolio, while on the other hand

some of the smaller companies were focused only on a few key products and their

successful market adoption. The outlier in the data set was a company built by a single

individual who recognized the seasonality of products and their adverse effects to the

company (i.e.: limited revenue).

Measures of Success (Metrics) was another area that had varying importance to the

respondents. To some of the respondents the answer was simply money, while to others

it was the joy technology brings along with the challenge of solving difficult problems for

both themselves and consumers. While joy is difficult to measure it can be a metric of

success. Money can be quantified and traced back to customer adoption of the product,

market share, operational efficiencies, etc. In all scenarios no clear metrics could be

defined for the screening and filtering element of the fuzzy front end process model. For

the respondents metrics of success were related to basic initial scoping of the opportunity

or final outcomes related to commercialization of the product and customer adoption.

The proposed ALAS Front End of Innovation Process Model recognizes the need for

improved fuzzy front end metrics and provides guidance with respect to tools that can be

used to measure the opportunity screening and filtering process.

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Limitations & Further Research

As with all research there are limitations inherent with the research methods

selected. In this particular case only 6 individuals were interviewed to gain an

understanding of the fuzzy front end of innovation. The analysis presented as part of this

thesis would have also benefited from a detailed survey provided to many innovators in

varying industries. The collection of such data would have allowed quantitative and

qualitative data on best practices, perceptions and needs to be collected.

The research questions did not yield significant comparative data on

organizational decision making and governance in industry versus academic literature

review. Additional research would be required to validate, modify, or challenge concepts

and relationships found in academic literature review.

This thesis was purposely confined to the front end of new product innovation,

which is defined as Disruptive, Product, Application or Platform innovations. Findings

from this study may add value to the remaining innovation types as defined by Geoffrey

Moore (2005) but they were not investigated as part of this thesis. This is a limitation of

this thesis and left for additional research.

Validation of the ALAS Front End of Innovation Process Model was not

completed as part of this research paper and remains as future research. While the model

is deeply rooted in academic research and literature review, it is necessary to validate the

model with innovation practitioners.

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Conclusion

Modification to the ALAS Front End of Innovation Process Model© was required

as part of the screening and filtering stage based on respondent feedback. While there are

many tools available to screen and filter ideas it is important to consider the performance

of the company’s current product portfolio when evaluating opportunities. This topic

was previously discussed under Management and Governance and is added as part of the

process model under Filtering and Screening.

While not a direct modification to the ALAS Front End of Innovation Process

Model but clearly advocated by respondents is the need to understand the Voice of the

Customer or have Closeness with Customer. Innovation happens by not only

understanding customer friction points but also observing and probing for latent customer

needs. Consider that innovation happens by piecing together fragments of information

that ultimately lead to breakthrough innovations.

Figure 18 describes the modified ALAS Front End of Innovation Process Model©.

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Figure 18: Modified ALAS Front End of Innovation Process Model©

77

APPENDIX Research Questions

1. Describe a recent successful product that you have been responsible for bringing to

market.

1.1. What was the genesis of the idea?

1.2. Why is this project considered a success? What are some of the metrics used to

determine project success?

1.3. What was your specific role?

1.4. How was the project initiated?

1.5. What was the duration of the project from product development to

commercialization?

1.6. How was market uncertainty during the early project phases reduced?

1.6.1. To what degree was market uncertainty reduced?

1.7. How was technical uncertainty during the early project phases reduced?

1.7.1. To what degree was technical uncertainty reduced?

1.8. Describe the level of project planning during the early product definition phases?

1.8.1. What functional groups were involved in the planning steps? (Job

functions)

1.8.2. What were some of the key planning steps taken that positively impacted

the success of the program? Why were they considered?

1.8.3. In hindsight what were key planning steps that must have been taken and

why were these steps not considered?

1.8.4. How much time was spent in planning of the project?

1.8.5. What groups were involved in the planning steps?

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1.9. Describe the level of communication during the early product definition phases?

1.9.1. What groups were involved during the Phase Gate 0 (initial planning

steps)?

1.9.2. Describe the intensity with which these team members contributed to

initial planning steps.

1.9.3. How was formal communication between groups established?

1.9.4. What were the preferred methods of communication?

1.9.5. What were the perceived contributions of these supporting groups in the

initial planning steps?

1.9.6. What did these groups do well on with respect to initial planning?

1.9.7. What did these groups miss with respect to initial planning?

2. Describe a product failure that you have been responsible for, advised or provided

significant technical input.

2.1. What was the genesis of the idea?

2.2. What was your specific role?

2.3. How was the project initiated?

2.4. What was the duration of the project from product development to

commercialization?

2.5. Why did this project fail?

2.6. How was market uncertainty during the early project phases reduced?

2.6.1. To what degree was market uncertainty reduced?

2.6.2. Describe the limitations in reducing market uncertainty?

2.6.3. What could have been done differently to reduce market uncertainty?

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2.7. How was technical uncertainty during the early project phases reduced?

2.7.1. To what degree was technical uncertainty reduced?

2.7.2. Describe the limitations in reducing technical uncertainty?

2.7.3. What could have been done differently to reduce technical uncertainty?

2.8. Describe the level of project planning during the early product definition phases?

2.8.1. What functional groups were involved in the planning steps? (Job

functions)

2.8.2. What were some of the key planning steps taken that negatively impacted

the success of the program? Why were they pursued?

2.8.3. In hindsight what were key planning steps that must have been taken and

why were these steps not considered?

2.8.4. How much time was spent in planning of the project?

2.8.5. What groups were involved in the planning steps?

2.9. Describe the level of communication during the early product definition phases?

2.9.1. What groups were involved during the Phase Gate 0 (initial planning

steps)?

2.9.2. Describe the intensity with which these team members contributed to

initial planning steps.

2.9.3. How was formal communication between groups established?

2.9.4. What were the preferred methods of communication?

2.9.5. What were the perceived contributions of these supporting groups in the

initial planning steps?

2.9.6. What did these groups do well on with respect to initial planning?

2.9.7. What did these groups miss with respect to initial planning?

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3. As a insert title here how do you go about finding the “right” problem? This is

defined as one that has the largest potential for the firm, is feasible, and is acceptable

to both the customer and management.

3.1. How do you decide you have sufficient information?

3.2. When do you decide you have sufficient information?

4. How do you go about ensuring you understand the problem completely from both the

customer and technical perspectives, with forays into understanding the aggregate

market and competitive situation?

4.1. What tools or techniques do you use to reduce technical ambiguity?

4.2. What tools or techniques do you use to reduce customer ambiguity?

4.3. What tools or techniques do you use to understand the competitive market

realities?

4.4. What tools or techniques do you use to understand you organizational

capabilities?

5. How do you circle between completing finding the right solution and ensuring you

understand the problem?

5.1. How do you decide you have sufficient information?

5.2. When do you decide you have sufficient information?

5.3. How do you sufficiently reduce all unknowns so that the proposed solution can

be introduced in the organizations formal product development process?

5.4. What are the key measures of progress? (NPD equivalent would be milestone

achievement).

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6. How is concept (idea) generation aligned with company strategy versus an

experimental approach?

6.1. What key attributes are required to allow an idea to move through the early

planning stages? (Fit with company, technical feasibility, etc.)

7. How is concept (idea) generation aligned with portfolio management?

7.1. What tools or models are used to advance new product projects based on

potential outcome and fit with rest of projects in development? (Examples: ROI,

ROA, probability models, strategic approaches, mapping models, etc.)

8. What methods are used to screen and filter concepts (ideas)?

8.1. What are the methods used to capture concepts?

8.2. What are the sources of capturing concepts? (internal, external, technology

seminars, skunk works, customer, suppliers, consortiums, etc.)

8.3. What are the methods used to screen concepts?

8.4. What are the attributes of the screening processing (category, driver, revenue

potential, etc.)?

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