Tech innovation s3_tools

ATOA Scientific Technologies Engineering Simulation For Innovation

Technology and Innovation Management: S3 Technology Tools for Innovation Raj C Thiagarajan, PhD

To

SIBM SIII MBA Students

Tools are essential for Technology Development

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• Axe

Primates also use tools….

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• Female Gorilla is using a stick to find the water depth.

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

TIM –S3: Technology Tools for innovation

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• Technology tools for value creation

• Innovation: Quality : Speed: Cost

• Engineering Tools

– Concurrent Engineering

– QFD

– DfX

– FMEA

– Simulation based product Development

• Innovation Tools

– TRIZ


Wealth Creation Cycle

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• Tools for Wealth creation

• Value = Benefit – Cost

• Technology and innovation tools for value creation

Basic Research

Applied Research

Industrial Research

Innovation/ Product

development

Commercialization

Wealth Creation


Traditional Product Development

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• Product plan to

Commercialization

• Water fall 1. Product Planning

2. Concept Design

3. Concept Evaluation

4. Preliminary Design

5. Design Evaluation

6. Final Design

7. Prototyping

8. Pilot production

9. Mass production

10. Product commercialization

• Sequential process

Product

Planning

Concept

Design

Concept Evaluation

Preliminary

Design

Design

Evaluation

Final

Design

Prototyping

Pilot

production

Mass

production

Product commercialization

Development Cycle Time


Traditional Product Development

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• Benefits

• Easy Management and control

• Uncertainty is minimized

• Functional expertise optimization

• Drawback

– Potential to miss customer requirements

– Design that can’t be Manufactured

– Longer cycle time

Marketing Engineering Pilot

production Testing

Mass production

Product Information Flow

Design Changes, Errors, Corrections


Cost of Design Changes

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• Cost of Design changes increases exponentially with product development cycle.

• 80% of the product cost is determined or committed at the concept design stage

Product Planning

Concept Design

Final Design

Pilot Production

Mass production

Co

st o

f D

esig

n c

han

ge

Product Planning

Concept Design

Final Design

Pilot Production

Mass production

Pro

du

ct C

ost

Product Development Cycle 0%

100%

Committed

Actual


Cost of fixing

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• Cost to find and repair defects

– @ Part 1X

– @ Sub assembly 10 X

– @ Final Assembly 100 X

– @ the Dealer 1000 X

– @ the customer 10000 X

@

Part

1X

@

Sub assembly

10 X

@

Final Assembly 100 X

@

Dealer

1000 X

@

Customer

10000 X


Product Management Influence

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• Product development and management

• Management activity ratio

• Management influence potential

Product Planning

Concept Design

Final Design

Pilot Production

Mass production

HIGH

Low

Act

ivit

y an

d

Infl

uen

ce In

dex


Typical Response time of Industry

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• 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

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• ENVIRONMENTAL REGULATIONS

Waste & Emissions

Occupational Health & Safety

Laws

Emergency Planning Laws

Air Quality Laws

Contaminated Land Requirements

Water Quality Laws

Chemicals Chemical Management Laws

Hazardous Material Transportation Laws

Waste Management Laws

Health & Safety


Product Development

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We definitely need better process….

Customer Requirements

Product Requirements

Product Design

Product Marketing

Product Delivery

Real Customer Requirements


Concurrent Engineering

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• 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



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• Concurrent Engineering

• Simultaneous Engineering

• Integrated Product Development


Concurrent Engineering: Definition

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• “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

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• Concurrent Product Design and Development

• Lowest overall life cycle costs

• Problem prevention from Problem Solving

DESIGN

Performance

Manufacturability

Quality and Cost

Service, Life

Environmental

Pilot

production

Testing and

Verification

Mass

Production


Benefits

• Significant development time , defects, time to market and failure reduction

• Improvements of service life, Quality, productivity and ROI.

Item Benfits

Development Time 30-50% Reduction

Engineering changes 60-95% Reduction

Scrap and Rework 75% Reduction

Defects 30-85% Reduction

Time to Market 20-90% Reduction

Field Failure Rate 60% Reduction

Service Life 100% improvement

Overall Quality 100 -600% improvement

Productivity 20 -110% improvement

Return on Assets 20 -120% improvement

BEFORE

QFD

AFTER

QFD

CONCEPT DESIGN PLANNING FINAL DESIGN PRODUCTION

PLANNING PRE DESIGN FINAL DESIGN PRODUCTION

BENEFITS


CE Environment

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• People

– Team

– Project

• Process

– Process modeling

– Process reengineering

– Info/ Data integration


• Technology

– Problem solving mechanisms

– DBMS

– PLM

– Simulation based Engineering (SBE)


Concurrent Engineering Tools

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• QFD: Quality Functional Deployment

• DfM: Design for manufacturing

• FMEA: Failure Mode Effect Analysis

• DFSS: Design for six sigma

• SPC: Statistical Process Control

Product Planning

Concept Design

Final Design

Pilot Production

Mass production


QFD


QFD

DfM

VE

QFD

FMEA

DfM

DFSS

QFD

FMEA

SPC, LEAN

DFSS


QFD

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• 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

• Matrix of requirements relations

• Competitive benchmarks

• Engineering targets

team response and solutions

What’s What’s

vs.

How’s

How’s

Pri

ori

tie

s

Trade-off opportunities

requirements

requirements flow down Technical Ranking Product Targets

Customer Requirements

Design Co

mp

etitive Ben

ch m

arking


QFD Example

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• First FRP Railway sleeper to replace Wood.

• IR,RDSO, DRDO, DST, IIT

• Planning to First prototype ~ 1 year

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

Identify and Characterise Product/Process Alternatives

Develop Product/Process Selection Criteria, Constraints & Goals

Address entire life-cycle

(design through

disposal)

Pre-selections

Selections

Compromise

Product/Process Specification

Materials

Structure

Process

Partition and Quantify Requirements

Capture Customer Requirements


Sarvatra

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• Sarvatra, developed by DRDO, R&DE(E), can lay a 75-metre-long bridge in 90 minutes.

• Prototype: ~ 3years

• Completion: ~5years

Load Class: MLC-70 Single Span Length: 15/20 m Multi-Span Capability: 75/100 m Construction time: 15 minutes


QFD Flow down

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• Planning Matrix

• Product Development Matrix

• Product manufacturing matrix

• Operator instruction matrix

PLANNING

PRODUCT

PROCESS

OPS


DfX

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• 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


FMEA

• Failure Modes and Effects Analysis

• Identifying failure modes, failure mechanisms, impact, probability and detection

• A structured engineering analysis performed on a product or a process

• Addresses the type, effects and severity of failures

• Results in actions that eliminate failure modes or reduces their impact

• Can reduce liability even for failures that are not eliminated

• Timing: After a design before production

QFD

FMEA

CTQ

Capture Customer Satisfaction requirements To MEET

Capture Customer Dissatisfaction Requirements To AVOID

Failure modes?

Failure Mechanisms?

Effect on The Customer?

Probability of the Failure?

Severity of the Failure?

Detection before Failure?

Probability | Consequence | Avoidance


FMEA

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• Quality

meets the specification when new

• Reliability

continues to meet the specification through a period of use

Quality/Reliability intimately tied to variability

Deterministic Vs Probabilistic

WEAR OUT failures

Overall failures

Early failures “Infant Morality”

Constant Failure

Rate

Increasing Failure

Rate

Decreasing Failure

Rate

Failu

re R

ate

Time

Load Strength Load Strength

WEAR OUT “Infant Morality”


Risk Priority Scores

Risk Priority Score =

Impact X Probability X

Detection

Impact: Severity of effect

Probability: Likelihood of occurrence

Detection: Difficulty of identifying failure

Effect Severity of Effect Ranking

Hazardous without warning

Very high severity ranking when a potential failure mode affects safe system operation and/or involves non compliance with federal safety regulation without warning

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Hazardous with warning

Very high severity ranking when a potential failure mode affects safe system operation and/or involves non compliance with federal safety regulation warning

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Very High System/item inoperable with loss of primary function

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High System/item operable, bit at reduced performance level. User dissatisfied

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Moderate System/item operable, but comfort/convenience item inoperable

6

Low System/item operable, but comfort/convenience item operable at reduced level

5

Very Low Defect noticed by most customers 4

Minor Defect noticed by average customer 3

Very Minor Defect noticed by discriminating customer 2

None No effect 1


Other Tools/ Methods

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• 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)

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• SBE product development

• Virtual Product Development

• Rapid Prototyping

• Customer Experience


The Traditional Engineered Process

The Simulation Based Engineered Process

The Engineering Process

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Mathematical Computational

Design

Predictive Processing

Testing for Validation & verification

Virtual product/ system

Conceptual Design Fabrication Assembly Testing

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

ENGINEERING PROCESS PRODUCT MATERIAL


The Simulation for the First time right

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MATHEMATICAL MODEL

• Captures the THE PHYSICS EMBEDDED IN THE ENGINEERING SCIENCES

• 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 predictable quantity of interest

• COMPUTATIONAL MODEL

• Computers have revolutionized techniques for solving differential and integral equations

• Finite element methods,

• Availability of Fast and cheap computing power

• Accurate numerical solutions to complex problems

• Nonlinearities easily handled

• The purpose of computation to model the real system to output the quantities of interest on which a decision can be made

• NEW PARADIGM: Simulation based engineering Design (SBED) with Multiphysics and Multiscale depth

Real product/ system

Mathematical model

Computational model

Prediction (Output)

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)

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• 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)

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• 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 – Auto design for crashworthiness

From: Research Directions In Computational Mechanics, A Report of the United States National Committee on Theoretical and Applied Mechanics, September 2000

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

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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.

From: Babuška, F. Nobile, R. Tempone, Reliability of Computational Science, Numerical Methods for Partial Differential Equations, DOI 10.1002/num 20263, www.interscience.wiley.com

Simulations helps to avoid failure & make it first time right.

http://upload.wikimedia.org/wikipedia/en/2/2e/Image-Tacoma_Narrows_Bridge1.gif

http://upload.wikimedia.org/wikipedia/commons/4/42/Mars_Climate_Orbiter_during_tests.jpg


Reliability of Simulations

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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

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• 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….

Real product/ system

Mathematical model

Computational model

Prediction (Output)

Validation Verification

Simulation

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

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• 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

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TIM –S3: Technology Tools for innovation

40

• 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

Business

semester mba technology

innovation technology

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product costcommitted

product lifecyclerequirements

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innovation management13