Focus your investments in innovations

Building Statistical Support

for

Delivering Focused Innovation:

Focusing Innovation to Achieve Business

Objectives without Sacrificing Innovation

“Freedom”

Agenda and Topics

• Opening

• Evolution of Process and Products Levels and

Dimensions

• The Process Levels and Dimensions

• The Product Levels and Dimensions

Agenda and Topics

• Understanding Innovation

• Definition

• Process

• Tools

• Application of Guidelines to Real-Life Context

• What to Optimize (Process, Product, or Both)

• Considerations for Process Optimization

• Considerations for Product Optimization

• Benefit of Both

Agenda and Topics

• Case Studies

• Process Optimization (Brief Walkthrough)

• Product Optimization (Brief Walkthrough)

• Product Optimization Which Leads to Process

Optimization (Detailed Walkthrough)

• Wrap-up

• Questions

• References

Opening

•Background

•Tutorial flow

•Definition

•The Challenge

•The Rationale

•CMMI ML 4 & 5 PAs Recap

Background

• Innovation is a key to business growth and

improved results

• Innovation means a new way of doing business; it

may refer to incremental, radical, or even

revolutionary changes in the approach to

extracting value for the business (business model)

• Involves a fundamental change to markets,

competencies, partners, technologies, or processes

• Companies that do not innovate eventually lose

customers to a competitor that has found a better

way.

Background

• However innovations – as any other aspect of a

business – require an investment and investment is

about the future.

• These innovation-related investments posit a new

future that plays by new rules. If you make investment

decisions on an extrapolated new future based on the

rules in operation today then you may misjudge the

future and “shut the door” on promising opportunities

• Therefore these decisions require complex analyses.

To make these easier, managers often use tools to help

with the financial analysis. The problem with these

tools is that they often value innovation and non

innovation in the same terms.

Background

• Innovation is more than developing new ideas, it is also

adapting those ideas to the particular context of the

business so that it confers a business advantage

• Thus, we speak of an “innovation lifecycle,” which

includes deployment of the innovation into the appropriate

parts of the organization so that the organization can

exploit the new source for value to the business.

• Deployment is more than introducing the change, it can

include further adaptation of the change and further

learning to be exploited concurrent to its deployment.

• Quality and cycle time are lifecycle attributes important to

the innovation lifecycle just as they are to the product

development lifecycle.

Background

• Our view is that creativity is a process – not an

accident, nor inherent.

• Creativity is initiated with a challenge and

“unleashed” through managing:

-multiple perspectives

-shared understanding

-opportunities for solution reflection,

brainstorming, information gathering, evaluation

-overall state of the expanding dynamic

-environment

Background

• For this reason we have developed a structured

methodology that supports the ongoing discovery and

evaluation of solutions throughout the innovation lifecycle

• We make use of process performance analyses as an input

to three levels of statistical thinking that support the

innovation process from identified needs to pilot results.

Tutorial flow

• The methodology we will be presenting in this tutorial uses

a cross matrix that identifies the appropriate selected

methods and models in conjunction with different

management and engineering disciplines as appropriate to

the innovation lifecycle phase

• Our statistical methodology is based on three main

evaluation phases and for each we have identified different

methods, to be selected as appropriate for the given

situation.

• Idea generation

• Idea screening

• Idea realization

• Case studies that will demonstrate the method in real life

use

Definitions

• Processes are defined as "a set of interdependent

tasks transforming input elements into products”

• Innovation refers to a new way of doing

something. It may refer to incremental and

emergent or radical and revolutionary changes in

thinking, products, processes, or organizations

• Statistically Managed and controlled -

application of the scientific method to understand

behavior

The Challenge Statements

• Innovations as any other aspect of a

business require an investment

• Innovations-related investment is about:

• the future

• the rules

• Making investment decisions on an

extrapolated new future based on today’s

rules may lead to costly mistakes

The Challenge Statements

• Investment and Innovation decisions can require

complex analysis.

• To make them easier, managers often use tools to

help with the financial and proposed solution

analysis.

• The problem with these tools is that they often

value innovation and non innovation in the same

terms.

• They encourage managers to make unfair demands

on returns on investment for innovation projects.

The Proposed Solution Rationale

• Structured methodology that supports the ongoing

evaluation of innovation ideas throughout the

different lifecycle phases

• Prioritization, piloting, and deployment of the

innovations based on statistical analysis

• We make use of process performance analysis as

an input to three levels of statistical thinking that

support the innovation process from identified

needs to pilot results.

• Idea generation

• Idea screening


CMMI ML 4 & 5 PAs Recap

• Organizational Process Performance

• Quantitative Project Management

• Causal Analysis and Resolution

• Organizational Innovation and Deployment

Specific Practices of OPP

SG 1 Establish Performance Baselines and Models

SP 1.1 Select Processes

SP 1.2 Establish Process-Performance Measures

SP 1.3 Establish Quality and Process-Performance

Objectives

SP 1.4 Establish Process-Performance Baselines

SP 1.5 Establish Process-Performance Models

Specific Practices of QPM

SG 1 Quantitatively Manage the Project

SP 1.1 Establish the Project’s Objectives

SP 1.2 Compose the Defined Process

SP 1.3 Select the Subprocesses That Will Be Statistically Managed

SP 1.4 Manage Project Performance

SG 2 Statistically Manage Subprocess Performance

SP 2.1 Select Measures and Analytic Techniques

SP 2.2 Apply Statistical Methods to Understand Variation

SP 2.3 Monitor Performance of the Selected Subprocesses

SP 2.4 Record Statistical Management Data

Specific Practices of CAR

SG 1 Determine Causes of Defects

SP 1.1 Select Defect Data for Analysis

SP 1.2 Analyze Causes

SG 2 Address Causes of Defects

SP 2.1 Implement the Action Proposals

SP 2.2 Evaluate the Effect of Changes

SP 3.2 Record Data

Specific Practices of OID

SG 1 Select Improvements

SP 1.1 Collect and Analyze Improvement Proposals

SP 1.2 Identify and Analyze Innovations

SP 1.3 Pilot Improvements

SP 1.4 Select Improvements for Deployment

SG 2 Deploy Improvements

SP 2.1 Plan the Deployment

SP 2.2 Manage the Deployment

SP 2.3 Measure Improvement Effects

Evolution

of

Process and Products

Levels and Dimensions

•The Process Levels and Dimensions

•The Product Levels and Dimensions

Process Levels and Dimensions

• Planned and Managed Process

• Architected and Engineered Process

• Operationally Optimized Process


Planned and Managed Process

• Plan

• Perform

• Control

Suggested Measures

Planned and Managed Process

• Availability and

completeness of plan

• Plan for resource

• Overall performing

time

• Omissions in

performance

• Compliance to plan


Architected and Improved Process

• Objectives

• Structured

• Monitored / Measured

• Effective / Efficient

• Process Interfaces and

Integration in

Lifecycle

• Prioritize and Balance

Resource Utilization

within Larger Context

Suggested Measures

Architected and Improved Process

• Process productivity

• Process resources

utilization effectiveness

• Process resources

utilization efficiency

• Meeting the process

objectives

• Other processes interfaces

efficiency

• Process related defects

density


Operationally Optimized Process

• Known Capability and Stable

• Defined Ingredients

• Known Critical Elements

• Meeting Objectives

• Controlled Interfaces

• Responsive / Modifiable

• Resilience / “Agile”

• Relevant ‘What If’s Scenarios

• Accepted Tolerance / Freedom

Boundaries

• Predictable Outcomes

Suggested Measures

Operationally Optimized Process

• Influence of Critical Elements

on process output

• Process resources utilization

‘What If’s Scenarios

• Process elements capability

• Quantitative definition of

process ingredients

Product Levels and Dimensions

• Planned and Managed System

• Architected and Engineered System

• Operationally Operated and Optimized

System


Planned and Managed System

• Requirements

• Constructions and Evaluation

• Deployment

Suggested Measures

Planned and Managed System

• Requirements Status

• Change Request Status

• Component Status

• Increment Content - Components

• Increment Content - Functions

• Technical Performance

• Standards Compliance

• Requests for Support

• Support Time Requirements


Architected and Engineered System

• Operational Needs and

Scenarios

• System Architecture

• System Interfaces and

Integration

• Validity / Verifiability

• Compliance with

CONOPS

Suggested Measures

Architected and Engineered System

• Maintenance Actions

• Technical Performance

• Performance Rating

• Requirements Coverage

• Defect Containment

• Utilization

• Reuse level

• Interfaces performance

• Validation accuracy


Operationally Optimized System

• Scalability

• Availability

• Reliability

• Serviceability

• Maintainability

• Supportability

• Stability

• Reusability

• Soundness of

Technology Future

Suggested Measures

Operationally Optimized System

• Technology flexibility

• Capacity growth models

• System (size) growth

models

• Time to Restore

• Down time

• MTBF

• Support calls causes and

density

• Technology extendibility

Understanding

Innovation •Definition

•Process

•Tools

•Application of Guidelines to Real-Life Context

Innovation The conversion of knowledge and ideas into new or improved products, processes, and services to gain a competitive advantage.

Innovation Management

A systematic method of fostering innovation by capturing, evaluating,

and developing ideas to conclusion.

Product Development

Service Development

Process Improvement

Innovation Requires Management

Process - Background

• Collect together old ideas – as well as existing facts.

• You need to know as much about the world in general and

get a solid, deep working knowledge of the business

situation that underlies the need for a new idea.

• This may seem daunting or unnecessary, but facts are the

raw material for innovation. And because of changes to

markets, competition, regulation, and technologies, “old

ideas” previously dismissed may, perhaps after further

adaptation, take on renewed promise.

• You also need to bring in perspectives and have access to

areas of expertise (either on the team or available to the

team) that can contribute to solution formulation and

evaluation.


• It is important to approach innovation and its evaluation

through a broad appreciation for causality

• All processes and outputs are connected and there are

relationships (synergies and tradeoffs) between all

performance results.

• Instead of taking a narrow focus to evaluating processes,

outputs, and performance results, which hinders progress;

approached more broadly, this “causality web” serves as a

basis for identifying and evaluating innovations.

• Ideas can be rearranged into endless new combinations.

The only practical limit is your knowledge of the facts and

your ability to see relationships between them.


• The final key evaluation step is to determine how to make

the innovation practical and profitable.

• At this point, many ideas stop looking so attractive.

• They start looking like a lot of hard work with no certain

reward.

• In this phase, valid historical data can help you determine

whether you have the assets, including skills, necessary to

successfully deploy an innovation.

• A deep understanding of the business situation may also

help you more fully flesh out the candidate innovation by

resolving potential barriers and identifying potential

partners and other resources that can help make the

candidate innovation effectively and economically

deployable.

Process – Steps - Idea generation

• Idea generation

• In this phase, an analysis of performance results and

more broadly the business situation will help in

identifying those business / operational areas that

require more than just incremental improvements.

• Experience in the systems and system-of-systems arena

demonstrate that idea generation best takes place

through a broader view of the “causal web” in which a

business finds itself, which in turn drives identification

of the criteria, measures, and analysis that will be

needed for evaluating ideas

Process – Steps - Idea screening

• Idea screening

• In this phase, our prediction and simulation

models and techniques support a deeper

evaluation of the appropriate idea for feasibility

and appropriateness to the business and the

broader delivery capability

Process – Steps - Idea realization


• since in this phase the innovation is maturing

and being transitioned to a ‘new’ project,

methods that support its management and

further evaluation (and further adaptation) are

applied toward achieving a higher degree of

confidence relative to the impacts to the

business and achievement of businesses

objectives

Suggested Methods

• Brainstorming

• Brainstorming is a group creativity technique designed to generate a

large number of ideas for the solution of a problem. In 1953 the method

was popularized by Alex Faickney Osborn

• Although traditional brainstorming does not increase the productivity of

groups (as measured by the number of ideas generated), it may still

provide benefits, such as boosting morale, enhancing work enjoyment, and

improving team work. Thus, numerous attempts have been made to

improve brainstorming or use more effective variations of the basic

technique

• Ground Rules

• Focus on quantity

• Withhold criticism

• Welcome unusual ideas

• Combine and improve ideas association.

Suggested Methods • Brainstorming

• Method

• Set the problem

• Create a background memo

• Select participants

• Create a list of lead questions

• Session conduct

• The process

• Evaluation

• Variations

• Nominal group technique

• Group passing technique

• Team idea mapping method

• Electronic brainstorming

• Directed brainstorming

• Individual brainstorming

Suggested Methods

• TILMAG's Five Steps for Solving Innovative Problems

• The transformation of ideal solution elements through associations

(TILMAG) is a leading method for a dominant class of issues that

arise in innovation thinking

• The steps

• Define the problem

• Identify the ISE ideal solution elements

• Build the TILMAG matrix

• Generate solutions

• Consolidate and prioritize

•

Suggested Methods

• QFD

• Quality Function Deployment (QFD) is a systematic process for

motivating a business to focus on its customers. It is used by cross-

functional teams to identify and resolve issues involved in

providing products, processes, services and strategies which will

more than satisfy their customers.

• A structured approach to defining customer needs or requirements

and translating them into specific plans to produce products to

meet those needs. The "voice of the customer" is the term to

describe these stated and unstated customer needs or requirements

•

Suggested Tools

• Reliability

• Ability of an equipment, machine, or system to

consistently perform its intended or required function or

mission, on demand and without degradation or failure.

• Probability of failure-free performance over an item's

useful life, or a specified timeframe, under specified

environmental and duty-cycle conditions. Often

expressed as mean time between failures (MTBF) or

reliability coefficient. Also called quality over time.

• Consistency and validity of test results determined

through statistical methods after repeated trials

Suggested Tools

• Validity • Degree to which an instrument, selection process, statistical

technique, or test measures what it is supposed to measure.

• Effectiveness • Degree to which objectives are achieved and the extent to which

targeted problems are resolved. In contrast to efficiency,

effectiveness is determined without reference to costs and, whereas

efficiency means "doing the thing right," effectiveness means

"doing the right thing."

• Piloting • Small-scale campaign, survey, or test-plant commissioned or

initiated to check the conditions and operational details before full

scale launch

Application Guidelines

Application Guidelines

• Considers the Real-Life Context

• Considers the Innovation System Frame

• Considers Innovative Capacity

• Examines what are here termed Technological Innovations

Systems, referring to a particular strand of innovation

theory.

• Discusses issues of policy with regard to the integration of

environmental concerns in innovation

• Discusses cultural determinants of innovation

Deliver Select Capture Formulate Evaluate Define Strategize

Define Business Strategy

Prioritize Business Strategy

Capture Idea

Enterprise Search

Publish Idea to Portal

Develop Business Case

-Strategic Impact - Market Potential - Financials - SWOT

Publish Business Case

IMO Reviews Idea

Run Portfolio Analysis

Approval

Build Project Team

Customer Feedback

Finalize Design Document

Execute Project

-Design -Market Potential -Legal Evaluation

Approval

Community Ratings and Reviews

Managed Process for Innovation

Process Success Factors

• Reveals emerging expectations with minimum effort and investment.

• Reveals expectations customers will appreciate.

• Reveals emerging expectations to anyone using the innovation system

without needing special talent.

• Reveals emerging expectation whenever needed.

• Reveals emerging expectations that won’t quickly face competition.

• Every emerging expectation is an opportunity for commercial success.

• Reveals emerging expectations early enough to develop & deliver new

products exactly when customers begin expecting them.

• Generates the ideas with minimum effort and investment.

• Generates ideas customers will like and warns of risky ideas or

potential threats.

• Generates new ideas whenever needed.


• Generates ideas competition can’t easily copy.

• Every new idea is successful.

• Ideas generated early enough to allow efficient implementation.

• Provides the design or reveals sources with minimal effort or expense.

• Designs cover the entire range of uses.

• Only provides needed uses (no need for unrealistic uses).

• Logical system that anyone can use.

• Competition can’t easily copy range of uses.

• Enhances your existing strengths.

• Every new design is successful.

• Ideas are immediately converted into designs.


• Designs new products so each is launched with minimum effort and

investment.

• Only designs products with total cost of ownership customers like.

• Designs new products within needed range of total cost of ownership.

• Utilizes available resources in the “standardized” way.

• Uses resources competition can’t easily use.

• Every new design successfully uses available resources.

• Making new products takes no time.

• Launches new product with minimum effort and investment.

• Only launches products customers like.

• Launches new products only when needed.


• Products launch.

• New products can’t be easily repeated by competition.

• Every new product is successful.

• New product is delivered to the customers exactly when they begin

expecting it.

• Value of new product is communicated with minimum effort and

investment.

• Only communicates values customers like.

• Only communicates values when it’s needed and only in the way

needed.

• Communicates values in the “standardized” way.

• Competition can’t easily repeat communication of values.

• Every communication successfully reaches the proper Target

Customers.


• Values are communicated to the customers exactly when they start

seeking.

• Collects maximum relevant information with minimum effort.

• Only collects true information.

• Collects information only when needed.

• Collects information in the “standardized” way.

• Collects information that competition doesn’t collect and doesn’t

understand its value.

• Gets needed information every time it’s needed.

• Provides relevant information so corrections are made exactly when

the customers start expecting them.

• Fits your organization’s existing systems and culture.

• Provides motivation to use the innovation system.

Deliver

Design for X

Prototypes and market testing

Manufacturing

MRO

Reuse, recycle

Select

Review projects

Select project(s)

Assign budget & time horizon

Approve & promote

Capture

Brainstorm & capture

Research & initial proof of concept

Publish & share

Formulate

Business rationale/ justification

Cost benefit assessment

Reviews & rating

Evaluate

Review and score

Portfolio analysis

Proof of concept funding

Define

Build project & assign team

Design, marketing, legal

Customer feedback

Analyze the business

Set business drivers

Establish a strategy

Strategize

%

∑

$

Managed Process for Innovation

Summary

Fosters a culture of innovation

Involves more of the right people at the right time

Facilitates collaborative participation

Widens the Idea Pipeline

Balances creativity with process discipline

Ensures key decisions and actions are taken at the right time

Secures and manages intellectual capital

Formalizes the Innovation Process

Provides objective and strategic selection criteria

Capitalize on business opportunities by improving the speed and robustness of idea selection

Maximize the financial return of selected ideas

Optimize budget allocation according to strategic value

Optimizes ROI and Time to Market

What to optimize

(Your Process / Product

or Both)

Considerations for Process

Optimization

• Where are we now and where do we need to be to

achieve our future performance goals

• What are the performance ranges can we expect from

our existing key processes

• What resources do we need to “improve” our

performance range to achieve future performance goals

• How much can we afford/must to invest to achieve our

improvements

• What is our multi-stage campaign to implement our

improvements

Considerations for Product

Optimization

• Optimization is successful when the cost of

manufacturing will drop and your profit will

increase

• Produce high-quality products within shorter time

lines

• To Correct balance between time and cost versus

yield and quality is essential to maximize return

on investment

Considerations for Product

Optimization • Demonstration of the scalability

• Partial selection of what to optimize

• Material

• Cost of product

• Design for

• Scalability

• Availability

• Reliability

• Serviceability

• Maintainability

• Supportability

• Stability

• Reusability

• Sustainability of the Technology as a solution

Benefit of Both

• Product development involves selecting both the

product (what to build) and the approach and

resources (how to build).

• By expanding your innovation process to

encompass both product and process, you may

find new combinations of product assemblies and

processes, resulting in promising products and

business models

• Leading to more growth for the business

Case studies

Process & Product

Optimization (Brief Walkthrough)

Our Objectives are

To identify process best value chain; improvements

and strengths

To develop what to focus on for improvement

(suggestions and an improvement action plan)

Business Goals

• Simplified the Product Development Initiatives to clear scope and users

• Identify, map and assign appropriate priorities the different stakeholders and

commitments

• Identify and predict the Large Complex or Global Teams coordination and

alignment efforts Inventions impact on the program and other team members \

teams

• Identify and predict processes efficiency And/or Effectiveness impact on the

program and teams

• Identify and predict Conflicts in Product Development Time vs. the

stakeholders expectations

• Identify and predict redesign Effectiveness impact on the program and teams

• Identify and predict changing in teams impact on the program and teams

• How to choose the right way Problem Solving, or Fire Fighting based on

quantitative and prediction of impact analysis

Goal Alignment with Models - 1

• Simplified the Product Development Initiatives to clear

scope and users • QFD and Dynamic Bayesian Games

• Identify, map and assign appropriate priorities the different

stakeholders and commitments • Quality Function Deployment

• Identify and predict the New Product Initiatives /

Inventions impact on the program and other stakeholders • Game Theory; Bayesian Networks and Dynamic Bayesian Games

• Identify and predict the Large Complex or Global Teams

coordination and alignment efforts Inventions impact on

the program and other team members \ teams • Bayesian Networks and Dynamic Bayesian Games

Goal Alignment with Models - 2

• Identify and predict processes efficiency And/or Effectiveness impact

on the program and teams

• Bayesian Networks and Dynamic Bayesian Games

• Identify and predict Conflicts in Product Development Time vs. the

stakeholders expectations

• Game Theory; Quality Function Deployment; Bayesian Networks and

Dynamic Bayesian Games

• Identify and predict redesign Effectiveness impact on the program and

teams

• Quality Function Deployment; Dynamic Bayesian Games

• Identify and predict changing in teams impact on the program and

teams

• Dynamic Bayesian Games

• How to choose the right way Problem Solving, or Fire Fighting based

on quantitative and prediction of impact analysis

• Bayesian Networks and Dynamic Bayesian Games

Professional Challenges (Partial list only)

• Information analysis

• Requirements Structure Analysis

• Requirements Position in Business Environment

• Requirements Value Chain

• Operational System Value Chain

• Development Elicitation to Requiremnts Type and

Classification

Operational Challenges (Partial list only)

• Product / Program Objectives Definition in Quantitative Way and Structure

• Definition of 'Good Enough' Level

• Differentiating Different Program Objectives and Success Factors For the Different Life Cycle Phases

• Resource Usage and Adjustment Elicitation to Plan and Objectives

Completing the Graphical Model

• To simplified the presentation we used a four

stage New Product Development process.

• The nodes indicating the potential return at

selected four stage gates

• To simplified this presentation, the gates are:

• New Product Concept Return

• New Product Design Return

• Production Startup Return

• Keep On Market Return

Completing the Graphical Model

• We identified and selected thirteen relevant criteria that are

influencing our factors, grouped into main five factors

• Each of it forms a node in the network. And its Arcs

from specific criteria to the relevant factors indicate

the criteria, e.g.:

• Sales Growth and Market Share influence Market Opportunity

• And the factors Arcs stage gate (Return node) indicate that

each factor influence each stage gate.

• One of our assumption during the development of the

causal relationships was criteria's that influence a factor do

not change between NPD stages

Completing the quantitative aspects of

the model

• The third step in structuring decisions is the refinement and

precise definition of all the elements of the

decision model.

• This relates to the second step of building a BN.

• The second step of building the BN is to

associate probabilities with the causal relationships

defined in the previous slides.

Defining States

• First action in the quantitative modelling phase is

to define appropriate states for each of the nodes

• Due to the large number of possible states in the

model (explained later) it was decided to use

numerical intervals

• for all criteria to have three states 1, 2 and 3.

These states can be interpreted appropriately from

worst to best for each of the criteria

Defining States

• Factors states are determined by the criteria that influences

each state.

• It was chosen to normalise any contributing criteria so that

the factor values will always be between 0 and 1.

• This eased the understanding of the outputs and the

development of the expressions determining the

probabilities of the NPD Return nodes.

• Again it was chosen to have three states for each factor.

The factor states indicate intervals for the result of the

expression that determine the factor value.

• The implemented factor states are therefore 0-0.33, 0.33-

0.66, and 0.66-1.

• Again these states can be interpreted appropriately from

worst to best for each of the factors

Defining States

• States for the NPD Return nodes are determined by the

possible states for the factors and the weightings of the

relationships.

• It was found that a granularity of only three states for the

NPD Return did not provide sufficient resolution to aid

understanding of the results.

• Therefore we have decided to implement four states for

the NPD Return nodes.

Model Outputs • For ease of discussion the NPD Return states of 0-23.25,

23.25 - 46.5, 46.5 - 69.75, 69.75 - 93 will be referred to as

low, medium-low, medium-high and high returns

respectively. Where appropriate for the

factors and criteria low, medium, and high will also be

used to refer to the relevant associated states

• The realized model with no evidence entered, as shown in

the next slide, shows a high probability for medium returns

in all three stages. This is based on equal probabilities for

the sixteen input criteria.

• The benefit of the Bayesian network is that evidence entry

is not limited to the input nodes, in this instance the criteria

nodes. Evidence can be entered at any of the nodes and

will propagate through the

network

Model Outputs

2/4/2013

Model Results • At New Product Concept; the model results show:

• 83.63% required probability for high Strategic Fit

• 48.64% for Keep on Market

• The Technical Feasibility is more important over the

Concept and Design phases

• For Concept phase 84%

• For Design phase 88.84%

• vs 80.82% and 47.10% respectively

• Also we observe that technical feasibility also has an

important part to play during production start up

Model Results • Customer Acceptance is important throughout the process

but especially after product launch

• Our model shows a high probability requirement for Customer

Acceptance for all stages

• Concept = 85.80%

• Design = 91.52%

• Production = 90.62%

• Keep on Market = 99%

• with the highest required level for Customer Acceptance

(99%) at the Keep On Market stage, that is after the product has

launched

• The model indicates that Financial Performance importance is fairly

constant over the NPD stages. A slight increase towards the later

stages is in line with the paper results

Evidence Scenario Results

• We found that it is very useful to use the model to run what if’s

• The next scenario could be described as:

• A new product of medium cost is to be developed.

• The product is within the company’s niche area and would therefore

leverage the company resources very well.

• It is unknown whether the resource would be available and no evidence of

this is entered.

• The product is very well aligned with the company strategy and the

window of opportunity is good but not extremely so.

• It is not sure how good the market acceptance or customer satisfaction

would be.

• It is clear that a product of high quality can be developed. Calculation

shows that the margin rate and Internal Rate of Return would be medium

good.

• The sales volume can not be predicted at this stage.

• Both sales growth and market share is predicted to

be medium


• The results can be interpreted as

• The technical feasibility of the project is high (63% likely) to

medium-high (33% likely)

• The project strategic fit is perfect. Whether the customer

acceptance would be high (55% likely) or medium-high (44%

likely) is unsure.

• The project’s financial performance and market opportunity are

predicted to be medium.

• All this translates into a high probability (almost 80% in all stages) of

achieving a medium-high return in all stages, zero probability of

achieving only a low return at any of the stage gates, and small

probabilities to reach a medium-low (1.68% to 12.26%) or high return

(6% to 17%)


What-if a high level of customer

satisfaction could be achieved

• The power of the Bayesian network lies in the ability to

perform what-if analysis. In the scenario as described

above one viable question that could be asked is:

What-if a high level of customer satisfaction could be

achieved?

91


satisfaction could be achieved

2/4/2013


satisfaction could be achieved • The power of the Bayesian network lies in the ability to

perform what-if analysis. In the scenario as described

above one viable question that could be asked is:

What-if a high level of customer satisfaction could be

achieved?

• The results can be interpreted as follows: • For all stages the probability of achieving a medium-low return becomes

zero. This is not a big influence as the original probabilities were already

very low.

• Increasing customer acceptance to high will almost double the probability

of indicating a high return at the design stage (from 17% to 31%).

• Same applies to the Production Startup stage (probability for high return

changes from 13% to 24%).

• Also of significance is that the 12% probability of indicating only a

medium-low return for the Keep on Market stage disappears 93

Conclusions and Recommendations

• Applying decision support techniques (specifically Bayesian

Networks) to the area of New Product Development will address some

of the primary challenges that mangers have

• Bayesian Networks can be implemented in order to develop a decision

support system in the management of new product development

domain

• This model addresses various aspects of new product development

over multiple stages

• The model can deal with quantitative and qualitative input and missing

data

• decision support technique such as Bayesian Networks can be

implemented to address our managerial problems and to support our

managers with strong ‘what if’s’

• The implementation of a graphical user interface hiding the

complexities of the Bayesian network

Discussion Points

• Performance data

• Cost of poor planning building elements

• Quantifying the operational impact of

support planning

• Effecting and effected stakeholders

mapping

• Quantifying the impact of support planning

on the development teams

• Appling this model on other domains

Outcome(s) Predicted

• Visual model that indicates the causal

relationships between various aspects in the

process

• Will enable us to deal with uncertainty and

missing data and allow the user to experiment with

possible outcomes (What-if analyses).

• Decision analysis that will ptovide structure and

guidance for systematic thinking in difficult

situations

Stakeholder Audience

• Process Performers

• Involved Processes

• EPGs

Factors used in the Process

Performance Model

• Objectives

• Structured

• Monitored / Measured

• Effective / Efficient

• Process Interfaces and Integration in

Lifecycle

• Prioritize and Balance Resource Utilization

within Larger Context

Data Collection

• Due to the unique nature of data elements and related factors we have

collected and analyzed the data elements and factors manually based

on players \ stakeholders per project \ program

• We have initiated historical data base (Excel based) and we are in the

progress to build generic model

• We did not use any sampling because for each project \ program we

need to run the full method from start, therefore we have developed

supporting matrix when to apply it

• The current threats to data quality and integrity that we have faced

• Players subjectivity

• Unclear player role

• Change of players (individuals) in the same position during one or more of

the ‘game’ (project \ program) instance

• We are currently running postmortem on past project to clean and

understand our percentage of measurement error

DBG HDB.xls

Tool Used

• Process Simulation Tool

• Bayesian network

What Worked Well

• What worked well

• Senior staff commitments

• Stakeholders acceptance of the balancing results

• Stakeholders acceptance of their ‘position’ and weight

• Between our side benefits

• ‘snow ball’ effect from other departments

• Request for generic model development

• Request to adjust it to strategic and multi year programs

• Stakeholder inputs

• Give clear world view of all aspects

• Reduce the decision making and factors analysis complexity

• The historical data base from past projects reduce resistance

• Model development team member inputs

• Create more clear understanding on the

• The historical data base from past projects reduce development

time

Discussion Points

• Process performance data

• Cost of poor planning elements

• Quantifying the operational impact of the

process

• Effecting and effected stakeholders

mapping

• Quantifying the impact of the optimized

process

• Appling this model on other domains

Wrap-up

• Innovation is fundamental to continued

business growth and success

• Requires investment, understanding business

environment

• Needs to be evaluated differently than a known

business investment

• Is a business process with its own rules

• Follows a lifecycle

• Needs to be focused and measured

Wrap-up

• Innovation should be thought of as:

• Consisting of a set of process steps (Strategize, Elicit, Screen, …)

• Having both product and process dimensions

• A learned process (define and adopt a methodology, and improve it

over time)

• Derived from performance data and information

• Must be a structured process supported by tools and methods

• Must be managed through monitoring the performance of the

innovation process itself and measured

• Needs to have management focus and commitments

References

• Carbonara, N., Scozzi, B., 2006, Cognitive maps to analyse new

product development processes: A case study, Technovation 26: 1233-

1243.

• Carbonell-Foulquםe, P., Munuera-Alemבn, J.L., Rodrםguez-

Escudero, 2004, Criteria employed for go/no-go

decisions when developing successful highly innovative products,

Industrial Marketing Management, 33:307-

316.

• Clemen, R.T., Reilly, T., 2001, Making hard decisions with Decision

Tools, Duxbury: Pacific Grove.

• Cooper, R.G., Edgett, S.J., Kleinschmidt, E.J., New Product Portfolio

Management: Practices and Performance, 1999, J Prod Innov Manag,

Vol. 16:333-351.

• Cooper, R.G., Edgett, S.J., Kleinschmidt, E.J., 2001a, Portfolio

Management - Fundamental to New Product Success, Working Paper

No. 12.

Contacts

• Kobi Vider – Picker

• K.V.P Consulting

• [email protected]

• Michael Konrad

• SEI

• [email protected]

mailto:[email protected]

mailto:[email protected]