Tech innovation s3_tools

Technology and Innovation
Management:
S3
Technology Tools for Innovation
Raj C Thiagarajan, PhD
To
SIBM SIII MBA Students

ATOA Scientific Technologies
Engineering Simulation For Innovation

Tools are essential for Technology Development
• Axe

2

Primates also use tools….
• Female Gorilla is using a stick to find the water depth.

3

TIM –S3: Technology Tools for innovation
• Technology tools for value creation
• Innovation: Quality : Speed: Cost

• Engineering Tools
– Concurrent Engineering
– QFD
– DfX
– FMEA
– Simulation based product Development

• Innovation Tools
– TRIZ
©ATOA Scientific Technologies Pvt Ltd | SIBM , III Semester MBA| Technology and Innovation Management 4

Wealth Creation Cycle
• Tools for Wealth
Wealth Basic
creation Creation Research

• Value = Benefit – Cost
Commerci Applied
• Technology and alization Research

innovation tools for
value creation
Innovation/
Industrial
Product
Research
development

©ATOA Scientific Technologies Pvt Ltd | SIBM , III Semester MBA| Technology and Innovation Management

Traditional Product Development
Product

• Product plan to Planning

Concept

Commercialization Design

Concept

• Water fall Evaluation

Preliminary
1. Product Planning Design
2. Concept Design Design
3. Concept Evaluation Evaluation

4. Preliminary Design Final
Design
5. Design Evaluation
6. Final Design Prototyping
7. Prototyping
Pilot
8. Pilot production production
9. Mass production Mass
10. Product commercialization production

• Sequential process Product
commercialization

Development Cycle Time
6

Traditional Product Development
• Benefits
• Easy Management and control
• Uncertainty is minimized
• Functional expertise optimization
Product Information Flow

Pilot Mass
Marketing Engineering Testing
production production

• Drawback Design Changes, Errors, Corrections
– Potential to miss customer
requirements
– Design that can’t be Manufactured
– Longer cycle time


Cost of Design Changes

Cost of Design change
• Cost of Design
changes increases
exponentially with
product development
cycle. Product Concept Final Pilot Mass
Planning Design Design Production production

100%
• 80% of the product Committed
cost is determined or
Product Cost
committed at the
concept design stage Actual

0%
Product Development Cycle
Product Concept Final Pilot Mass

Cost of fixing
@
• Cost to find and repair defects Part
1X
– @ Part 1X @

– @ Sub assembly 10 X Sub assembly
10 X
– @ Final Assembly 100 X
@
– @ the Dealer 1000 X Final Assembly 100 X

– @ the customer 10000 X @
Dealer
1000 X
@
Customer
10000 X


Product Management Influence
• Product
development and
management
HIGH

• Management

Influence Index
Activity and
activity ratio

• Management
influence
potential Low


Typical Response time of Industry
• Aero engine: ~10 Years to 5 Years

• Pharma: Drug molecule: ~ 8 Years to 4 years

• Medical Technology: ~ 24 months to 12 months

• Renewable NPI (Wind ): ~6 months to 1 months

• Finance: ~1 week to On the spot


Larger Scope of Product design
• ENVIRONMENTAL REGULATIONS
Emergency Planning Air Quality
Laws Laws

Waste &
Emissions
Hazardous Material
Transportation Laws
Chemicals
Chemical Management Laws

Waste
Management Laws

Water Quality Laws

Occupational
Contaminated Land Requirements
Health & Safety
Laws
Health & Safety

Product Development

Customer Product Product Product Product Real Customer
Requirements Requirements Design Marketing Delivery Requirements

We definitely need better process….


Concurrent Engineering
• Concurrent consideration of all the product lifecycle
requirements at early stage of design.
– From functionality, manufacturability, assembly,
testing and verification, maintenance, environmental
impact, disposal, recycling and sustainability.
– Converting hierarchical organizations into teams

• Overall goal of concurrent nature of the process
– significantly increase productivity and Quality
– Reduce development cost and Cycle time
– Prevention of problems



• Concurrent Engineering
• Simultaneous Engineering
• Integrated Product Development


Concurrent Engineering: Definition
• “Concurrent engineering methodologies permit the separate tasks of the
product development process to be carried out simultaneously rather than
sequentially. Product design, testing, manufacturing and process planning
through logistics, for example, are done side-by-side and interactively.
Potential problems in fabrication, assembly, support and quality are
identified and resolved early in the design process.”
Izuchukwu, John. “Architecture and Process :The Role of Integrated Systems in Concurrent Engineering.” Industrial Management
Mar/Apr 1992: p. 19-23.

• “The simultaneous performance of product design and process design.
Typically, concurrent engineering involves the formation of cross-functional
teams. This allows engineers and managers of different disciplines to work
together simultaneously in developing product and process design.”
Foster, S. Thomas. Managing Quality: An Integrative Approach. Upper Saddle River New Jersey: Prentice Hall,
2001.


Concurrent Engineering Cycle
Performance

Manufacturability

Quality and Cost Pilot Testing and Mass
DESIGN
production Verification Production
Service, Life

Environmental • Concurrent Product Design and
Development
• Lowest overall life cycle costs
• Problem prevention from Problem Solving


Benefits
Item Benfits

• Significant Development Time 30-50% Reduction

Engineering changes 60-95% Reduction
development time , Scrap and Rework 75% Reduction
defects, time to Defects 30-85% Reduction

market and failure Time to Market 20-90% Reduction

Field Failure Rate 60% Reduction
reduction Service Life 100% improvement

• Improvements of Overall Quality 100 -600% improvement

service life, Quality, Productivity 20 -110% improvement

Return on Assets 20 -120% improvement
productivity and ROI.
BEFORE PLANNING CONCEPT DESIGN FINAL DESIGN PRODUCTION
QFD

AFTER PLANNING PRE DESIGN FINAL DESIGN PRODUCTION
QFD

BENEFITS


CE Environment
• People
– Team
– Project

• Process
– Process modeling
– Process reengineering
– Info/ Data integration

• Technology
– Problem solving mechanisms
– DBMS
– PLM
– Simulation based Engineering (SBE)

Concurrent Engineering Tools
• QFD: Quality
Functional Deployment
• DfM: Design for QFD

manufacturing QFD
• FMEA: Failure Mode DfM
QFD
QFD
Effect Analysis FMEA
FMEA
• DFSS: Design for six VE
DfM
sigma SPC, LEAN
• SPC: Statistical Process DFSS
DFSS
Control


QFD
• QFD: A tool that integrates the “voice of the customer” into
the product and service development process.

• A QFD matrix: The "house of quality".

• Customer requirements
• Engineering requirements
Trade-off opportunities

• Matrix of requirements relations Design How ’s

Competitive Bench marking
Customer Requirements team response and solutions
• Competitive benchmarks
What’s
• Engineering targets What’s

Priorities
vs.
requirements How’s

Technical Ranking requirements flow down
Product Targets


Capture Customer Requirements

QFD Example Partition and Quantify Requirements

Map Requirements to Product/Process Characteristics
(QFD: Quality Function Deployment)

• First FRP Materials Develop Product/Process
Identify and Characterise
Railway sleeper Product/Process Alternatives
Selection Criteria,
Constraints & Goals
to replace Address
Structure Pre-selections
Wood. entire life-cycle
(design
Selections
• IR,RDSO, DRDO, through
disposal)
Process
DST, IIT Compromise

• Planning to Product/Process Specification

First prototype
~ 1 year


Load Class: MLC-70

Sarvatra Single Span Length: 15/20 m

Multi-Span Capability: 75/100 m
• Sarvatra,
Construction time: 15 minutes
developed by
DRDO,
R&DE(E), can
lay a 75-metre-
long bridge in
90 minutes.

• Prototype: ~
3years
• Completion:
~5years


QFD Flow down
PLANNING

PRODUCT

• Planning Matrix PROCESS

• Product Development Matrix
OPS

• Product manufacturing matrix

• Operator instruction matrix


DfX
• Design for ‘X’.
• X is a variable that can be substituted with, for,
Assembly, Cost, Environment, Fabrication,
Manufacture, Obsolescence, Procurement,
Reliability, Serviceability or Test.

• DfM: Design for Manufacturing
• DfA: Design for Assembly
• DfE: Design for Environment
• DfS: Design for Sustainability


Capture Customer

FMEA QFD Satisfaction requirements
To MEET

CTQ
• Failure Modes and Effects Analysis Capture Customer
Dissatisfaction
• Identifying failure modes, failure FMEA Requirements
mechanisms, impact, probability To AVOID

and detection
• A structured engineering analysis
performed on a product or a process Failure Failure
modes? Mechanisms?
• Addresses the type, effects and
severity of failures
Effect on The
• Results in actions that eliminate Customer?
failure modes or reduces their
impact
Probability of the Detection before
• Can reduce liability even for Failure? Failure?
failures that are not eliminated
• Timing: After a design before Severity of the
production Failure?

Probability | Consequence | Avoidance

FMEA Decreasing
Failure
Constant
Failure
Rate
Increasing
Failure
Rate
Rate

Failure Rate
• Quality
meets the Overall
specification failures
WEAR OUT
when new Early failures failures
“Infant Morality”

• Reliability
continues to meet
Time
the specification “Infant Morality”
through a period WEAR OUT
of use Load Strength Load Strength
Quality/Reliability
intimately tied to
variability

Deterministic Vs
Probabilistic


Risk Priority Scores
Effect Severity of Effect Ranking
Risk Priority Score = Hazardous
without
Very high severity ranking when a potential
failure mode affects safe system operation
10

Impact X Probability X warning and/or involves non compliance with
federal safety regulation without warning
Detection Hazardous Very high severity ranking when a potential 9
with failure mode affects safe system operation
warning and/or involves non compliance with
federal safety regulation warning
Impact: Severity of effect Very High System/item inoperable with loss of
primary function
8

High System/item operable, bit at reduced 7
performance level. User dissatisfied
Probability: Likelihood of Moderate System/item operable, but
comfort/convenience item inoperable
6

occurrence Low System/item operable, but 5
comfort/convenience item operable at
reduced level
Very Low Defect noticed by most customers 4
Detection: Difficulty of Minor Defect noticed by average customer 3

identifying failure Very Minor Defect noticed by discriminating customer 2
None No effect 1


Other Tools/ Methods
• Value Engineering
• VA/VE is an approach to productivity improvement that attempts to increase the value obtained by a
customer of a product by offering the same level of functionality at a lower cost.
• prioritise parts of the total design that are most worthy of attention.

• Configuration management
• Configuration simply refers to the arrangement of the parts or elements of something, and
management refers to the act or practice of managing.

• TQM
• Total quality management (TQM) is a philosophy of pursuing continuous improvement in each process
through the integrated efforts of all individuals in the organization.

• DFSS, SPS, LEAN


Simulation Based Engineering (SBE)
• SBE product development
• Virtual Product Development
• Rapid Prototyping

• Customer Experience


The Engineering Process

MATERIAL ENGINEERING PROCESS PRODUCT

The Traditional Engineered Process
Conceptual Design Fabrication Assembly Testing

The Simulation Based Engineered Process
Mathematical Virtual Testing for
Predictive
Computational product/ Validation &
Processing
Design system verification

Simulation Based Engineering Process
Minimizes the Uncertainty in the Concurrent Engineering Process
For Enabling Faster and low cost Innovative product development


The Simulation for the First time right
MATHEMATICAL MODEL
• Captures the THE PHYSICS EMBEDDED IN THE ENGINEERING SCIENCES Real product/
system
• Simple closed-form solutions to establish essential relationships, Numerical solutions for complex problems
• Properties of different types of differential and integral equations
• Closed-form solutions only available for very simple problems
• The mathematical model only transforms the available information about the real problem into a Mathematical
predictable quantity of interest model

• COMPUTATIONAL MODEL
• Computers have revolutionized techniques for solving differential and integral equations Computational
• Finite element methods,
model
• Availability of Fast and cheap computing power
• Accurate numerical solutions to complex problems
• Nonlinearities easily handled Prediction
• The purpose of computation to model the real system to output the quantities of interest on (Output)
which a decision can be made
• NEW PARADIGM: Simulation based engineering Design (SBED) with Multiphysics and Multiscale depth

It is a must to incorporate all the known Scientific and or Engineering knowledge for a
given problem solving or new product design.
Failure by not integrating the known knowledge is not professionally acceptable.

Simulation Based Engineering (SBE)

• Engineering is the profession in which a
knowledge of the mathematical and
natural sciences gained by study
experience, and practice is applied with
judgment to develop ways to utilize,
economically, the materials and forces of
nature for the benefit of the society -
Accreditation Board for Engineering and Technology

• SBE to develop Virtual Innovative Products for
unique customer experience with highest
performance and reliability at lowest cost .

• Studies shows that the Simulation based Product
development, reduced the prototyping by 50%
and increased the lead time ~60 days ahead of
the competition.


Simulation based Engineering Design
(SBED)
• SBED provides unparalleled access to real-world conditions
• SBED is credited with numerous success story
• SBED can be used to Predict unknown product performance for first
time right
• Eventually can be used to predict the future outcome

• Simulations has none of the following limitations of experimental
designs /tests,
– Cost constraints
– harsh/unrealistic parameter ranges, and
– Environment, Health and Safety concerns.

• It has become indispensable for
– Weather prediction
– Medical diagnosis (Virtual human)
– Material modeling
– Drug synthesis From: Research Directions In Computational Mechanics, A Report of the United States National
Committee on Theoretical and Applied Mechanics, September 2000

– Auto design for crashworthiness Ref: Jaroslav Mackerle Finite-element analysis and simulation of machining: a bibliography (1976–
1996), Journal of Materials Processing Technology 86 (1999) 17–44


Type of Failure and Examples
A. Modeling Problem/ Unknown Phenomenon
The Tacoma Narrows Bridge. The suspension bridge across Puget-Sound
(Washington State) collapsed November 7, 1940.
Reason: the model did not properly describe the aerodynamic forces and
the effects of the Von Karman vortices. In addition, the behavior of the
cables was not correctly modeled.
• The Columbia Shuttle Accident June 2003. It was caused by a piece of
foam broken off the fuel tank. After it was observed, the potential of the
damage was judged, upon computations, as nonserious. Reason: the
model used did not take properly into consideration the size of the foam
debris.
B. Numerical Treatment Problem
• The Sleipner Accident. The gravity base structure of Sleipner, an
offshore platform made of reinforced concrete, sank during ballast test
operation in Gandsfjorden, Norway, August 23, 1991. Reason: finite
element analysis gave a 47% underestimation of the shear forces in the
critical part of the base structure.
C. Computer Science Problem
• Failure of the ARIANE 5 Rocket, June 1996. Reason: problem of
computer science, implementation of the round offs.
D. Human Problem
• Mars Climate Orbiter. The Orbiter was lost September 23, 1999, in the
Mars Atmosphere. Reason: unintended mixture of Imperial and metric
units. Simulations helps to avoid failure & From: Babuška, F. Nobile, R. Tempone, Reliability of

make it first time right. Computational Science, Numerical Methods for Partial
Differential Equations, DOI 10.1002/num 20263,
www.interscience.wiley.com


Reliability of Simulations
Engineering accidents can happen due to,
– Modeling Error,
– the numerical treatment,
– computer science problems, and
– human errors.

Reliability of simulation depends on
• The Mathematical model.
• Resources vs performance
• Deterministic/ Probabilistic
• Prediction/quantification
– Failure probability
– Confidence level/ Factor of safety
• Simulations are moving from Trend prediction to
actual and accurate performance prediction
Objective is to increase the reliability of simulations.


Simulation and Testing + V&V
Real product/ Mathematical Computational Prediction
Simulation
system model model (Output)

Testing Validation Verification

• The interplay between Simulation and Testing.
• Testing is a process to help validation and verification
for first time right.
• Validation is a process determining if the mathematical
model describes sufficiently well the reality
• Verification is a process of determining whether the
computational model and the implementation lead to
the prediction with sufficient accuracy.
• V&V concepts are applicable to all stages of testing….

Reference: Leszek A. Dobrza´nski, Significance of materials science for the future development of
societies, Journal of Materials Processing Technology 175 (2006) 133–148


Virtual Testing
• Simulation to predict the experimental properties of
systems.
• For example, It is difficult to characterize all the
anisotropic properties of composites. Numerical
models is used to predict the complimentary
anisotropic properties.
• Simulation to mimic the testing is performed to zoom
into the inner working mechanism of materials and
products.
• The progressive growth, failure, damage mechanics
can help to reverse engineer the materials for
improved and optimal performance.
• Virtual Testing are used to simulate and predict high
risk and costly experimental tests for cost effective
product development.


Four Stages of Complimentary Simulation and
Testing for the Engineering Design of First Time
Right Product Development


TIM –S3: Technology Tools for innovation
• Technology tools for value creation
• Innovation: Quality : Speed: Cost

• Engineering Tools
– QFD
– DfX
– FMEA
– Simulation based product Development

• Innovation Tools
– TRIZ

Tech innovation s3_tools

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