Innovation is a key element for companies in providing growth and for increasing results. Innovation means a new way of doing business; it may refer to incremental, radical and/or revolutionary changes in extracting value for a business through a fundamental change in approach to a market, a technology, or a process. A company that overlooks new and better ways of doing business will eventually lose customers to another competitor that has found a better way. However innovations as any other aspect of a business require an investment and investment is about the future. Sometimes you invest in a future that plays by the same rules as today. Other investment is about a new future that plays by new rules. If you make investment decisions on an extrapolated new future based on the today’s rules then you can make costly mistakes. Investment decisions can require complex analyses. To make them 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. They encourage managers to make unfair demands on returns on investment for internal innovation projects. We believe that creativity is a process not an accident (“chance prefers the prepared mind”), although it’s often tempting to believe that individuals are creative or non-creative. Creative people also love to play around with the ideas that they collect. For them everything is connected – part of an overall pattern. Old ideas are moved around, combined, squeezed, and stretched to make new ideas. Innovation within businesses is achieved in many ways. One way involves the use of creativity techniques. These are methods that encourage original thoughts and divergent thinking (e. g. brainstorming, morphological analysis, TRIZ). New ideas that have been generated by the use of creativity techniques have to be structured and evaluated. In order to complete the innovation process the selected promising ideas have to be deployed into practice. For this reason we have developed a structured methodology that supports the ongoing evaluation of innovations throughout the prioritization, piloting, and deployment 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. The first step is 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. It is important to approach innovation and its evaluation through a broad appreciation for causality: al

1. Building Statistical Support
for
Delivering Focused Innovation:
Focusing Innovation to Achieve Business
Objectives without Sacrificing Innovation
“Freedom”

2. Agenda and Topics
• Opening
• Evolution of Process and Products Levels and
Dimensions
• The Process Levels and Dimensions
• The Product Levels and Dimensions

3. 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

4. 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

5. Opening
•Background
•Tutorial flow
•Definition
•The Challenge
•The Rationale
•CMMI ML 4 & 5 PAs Recap

6. 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.

7. 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.

8. 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.

9. 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

10. 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.

11. 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

12. 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

13. 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

14. 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.

15. 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
• Idea realization

16. CMMI ML 4 & 5 PAs Recap
• Organizational Process Performance
• Quantitative Project Management
• Causal Analysis and Resolution
• Organizational Innovation and Deployment

17. 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

18. 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

19. 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

20. 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

21. Evolution
of
Process and Products
Levels and Dimensions
•The Process Levels and Dimensions
•The Product Levels and Dimensions

22. Process Levels and Dimensions
• Planned and Managed Process
• Architected and Engineered Process
• Operationally Optimized Process

23. Process Levels and Dimensions
Planned and Managed Process
• Plan
• Perform
• Control

24. Suggested Measures
Planned and Managed Process
• Availability and
completeness of plan
• Plan for resource
• Overall performing
time
• Omissions in
performance
• Compliance to plan

25. Process Levels and Dimensions
Architected and Improved Process
• Objectives
• Structured
• Monitored / Measured
• Effective / Efficient
• Process Interfaces and
Integration in
Lifecycle
• Prioritize and Balance
Resource Utilization
within Larger Context

26. 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

27. Process Levels and Dimensions
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

28. 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

29. Product Levels and Dimensions
• Planned and Managed System
• Architected and Engineered System
• Operationally Operated and Optimized
System

30. Product Levels and Dimensions
Planned and Managed System
• Requirements
• Constructions and Evaluation
• Deployment

31. 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

32. Product Levels and Dimensions
Architected and Engineered System
• Operational Needs and
Scenarios
• System Architecture
• System Interfaces and
Integration
• Validity / Verifiability
• Compliance with
CONOPS

33. Suggested Measures
Architected and Engineered System
• Maintenance Actions
• Technical Performance
• Performance Rating
• Requirements Coverage
• Defect Containment
• Utilization
• Reuse level
• Interfaces performance
• Validation accuracy

34. Product Levels and Dimensions
Operationally Optimized System
• Scalability
• Availability
• Reliability
• Serviceability
• Maintainability
• Supportability
• Stability
• Reusability
• Soundness of
Technology Future

35. 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

36. Understanding
Innovation
•Definition
•Process
•Tools
•Application of Guidelines to Real-Life Context

37. Innovation Requires Management
Product
Development
Innovation Innovation
The conversion of Management
knowledge and ideas into
new or improved products, A systematic method of
processes, fostering innovation by
Process Service
and services to gain Improvement Development capturing, evaluating,
a competitive and developing ideas to
advantage. conclusion.

38. 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.

39. Process - Background
• 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.

40. Process - Background
• 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.

41. 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

42. 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

43. Process – Steps - Idea realization
• 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

44. 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.

45. 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

46. 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
•

47. 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
•

48. 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

49. 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

50. Application Guidelines

51. 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

52. Managed Process for Innovation
Strategize Capture Formulate Evaluate Define Select Deliver
Define IMO
Business Strategy Reviews Idea
Prioritize Run Portfolio
Business Strategy Analysis
Approval
Capture Idea
Enterprise Build Project Team
Search
Publish Idea to Execute Project
Portal Design-
Market Potential-
Legal Evaluation-
Develop
Business Case Customer Feedback
Strategic Impact -
Market Potential -
Finalize Design
Financials -
SWOT- Document
Publish
Business Case Approval
Community Ratings and Reviews

53. 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.

54. Process Success Factors
• 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.

55. Process Success Factors
• 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.

56. Process Success Factors
• 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.

57. Process Success Factors
• 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.

58. Managed Process for Innovation
Strategize Capture Formulate Evaluate Define Select Deliver
$ %
∑
Analyze the Brainstorm & Business Review and Build project & Review projects Design for X
business capture rationale/ score assign team Select project(s) Prototypes and
Set business Research & justification Portfolio analysis Design, marketing, Assign budget & market testing
drivers initial proof of Cost benefit Proof of concept legal time horizon Manufacturing
Establish a strategy concept assessment funding Customer Approve & MRO
Publish & share Reviews & rating feedback promote Reuse, recycle

59. Summary
Widens the Idea Pipeline Formalizes the Innovation Process
 Fosters a culture of innovation  Balances creativity with process
 Involves more of the right people at discipline
the right time  Ensures key decisions and actions are
 Facilitates collaborative participation taken at the right time
 Secures and manages intellectual
capital
Optimizes ROI and Time to Market
 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

60. What to optimize
(Your Process / Product
or Both)

61. 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

62. 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

63. 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

64. 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

65. Case studies

66. 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)

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

73. 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

75. 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.

76. 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

77. 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

78. 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.

79. 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

80. Model Outputs
2/4/2013

81. 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

82. 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

83. 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

84. Evidence Scenario Results
• 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%)

85. Evidence Scenario Results

86. 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

87. What-if a high level of customer
satisfaction could be achieved
2/4/2013

88. 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?
• 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
93
medium-low return for the Keep on Market stage disappears

89. 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

90. 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

91. 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

92. Stakeholder Audience
• Process Performers
• Involved Processes
• EPGs

93. 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

94. 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

95. Tool Used
• Process Simulation Tool
• Bayesian network

96. 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

97. 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

98. 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

99. 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

100. 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.

101. Contacts
• Kobi Vider – Picker
• K.V.P Consulting
• Kobi.vider@hotmail.com
• Michael Konrad
• SEI
• mdk@sei.cmu.edu