tech innovation s3

8/3/2019 Tech Innovation S3

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ATOA Scientific TechnologiesEngineering Simulation For Innovation

Technology and InnovationManagement:

S3Technology Tools for InnovationRaj C Thiagarajan, PhD

To

SIBM SIII MBA Students



Tools used from Pre history

2

• Axe



Primates also use tools…

3

• Female Gorilla



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

TIM –S3: Technology Tools for innovation

4

• 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

• Tools for Wealthcreation

• Value = Benefit – Cost

• Technology and

innovation tools for

value creation

BasicResearch

AppliedResearch

IndustrialResearch

Innovation/Product

development

Commercialization

WealthCreation




Traditional Product Development

6

• Product plan to

Commercialization

• Water fall1. 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

ConceptEvaluation

Preliminary

Design

DesignEvaluation

Final

Design

Prototyping

Pilot

production

Mass

production

Productcommercialization

Development Cycle Time


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Traditional Product Development

7

• Benefits

• Easy Management and control

• Uncertainty is minimized

• Functional expertise optimization

• Drawback

–Potential to miss customerrequirements

– Design that can’t be Manufactured

– Longer cycle time

Marketing EngineeringPilot

productionTesting

Massproduction

Product Information Flow

Design Changes, Errors, Corrections



Cost of Design Changes

8

• Cost of Designchanges increases

exponentially with

product development

cycle.

• 80% of the product

cost is determined or

committed at theconcept design stage

ProductPlanning

ConceptDesign

FinalDesign

PilotProduction

Massproduction

C o s t o f D e s i g n

c h

a n g e

Product

Planning

Concept

Design

Final

Design

Pilot

Production

Mass

production

P r o d u c

t C o s t

Product Development Cycle0%

100%

Committed

Actual



Cost of fixing

9

• 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

10

• Productdevelopment and

management

• Managementactivity ratio

• Management

influencepotential

Product

Planning

Concept

Design

Final

Design

Pilot

Production

Mass

production

HIGH

Low

A c t i v i t y a n d

I n f l u e n c e

I n d e x



Typical Response time of Industry

11

• 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

12

• 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 MaterialTransportation Laws

Waste

Management Laws

Health & Safety



Product Development

13

We definitely need better process….

Customer

RequirementsProduct

Requirements

Product

DesignProduct

Marketing

Product

Delivery

Real Customer

Requirements



Concurrent Engineering

14

• Concurrent consideration of all the product lifecyclerequirements 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




15

• Concurrent Engineering

• Simultaneous Engineering

• Integrated Product Development



Concurrent Engineering: Definition

16

•“Concurrent engineering methodologies permit the separate tasks of theproduct 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 ManagementMar/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 worktogether 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

17

• 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

• Significantdevelopment 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

BEFOREQFD

AFTER

QFD

CONCEPT DESIGNPLANNING FINAL DESIGN PRODUCTION

PLANNING PRE DESIGN FINAL DESIGN PRODUCTION

BENEFITS



CE Environment

19

• People – Team

– Project

• Process

– Process modeling

– Process reengineering

– Info/ Data integration

• Technology

– Problem solving mechanisms

– DBMS

– PLM



Concurrent Engineering Tools

20

• QFD: QualityFunctional Deployment

• DfM: Design for

manufacturing

• FMEA: Failure ModeEffect Analysis

• DFSS: Design for six

sigma

• SPC: Statistical ProcessControl

Product

Planning

Concept

Design

Final

Design

Pilot

Production

Mass

production


QFD


QFD

DfM

VE

QFD

FMEA

DfM

DFSS

QFD

FMEA

SPC, LEAN

DFSS



QFD

21

• 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

P r i o r i t i e s

Trade-off opportunities

requirements

requirements flow down Technical Ranking

Product Targets

Customer Requirements

Design C o m

p e t i t i v eB e n c h m a r k i n g



PRODUCT DESIGN QFD

• Customer Requirements (WHATs)

– features or characteristics that the customer indicates as relevant

• Engineering Metrics (HOWs)

– generated by engineering staff

– quantifiable aspects of system that can contribute to satisfying customer requirements

– mixture of performance parameters and design parameters

• Matrix of Requirements Relations (Whats vs Hows)

– “matrix”with rows of customer requirements and columns of engineering metrics

– each relationship marked with an “x” or Scored 1 3 9 or H M L

• Benchmarking (Competition)

– opportunity to explicitly compare your design to that of a competitor’s

– mark the customer requirements that are met with an “o”.

• Engineering Targets ()

– target may be the value that

– the requirement must achieve

–in order to compete

– with the benchmarked products What’s What’s vs. How’s

How’s vs.

How’s

How’s

P r i o r i t i e

s

Target values

Competitive

Assessment

Importance computed

trade-off opportunities

requirements other products

team response and solutions

requirements flowdown

development priorities

C C R i



QFD Example

23

• First FRPRailway 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

Load Class: MLC 70




Sarvatra

24

• 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

25

• Planning Matrix

• Product Development Matrix

•Product manufacturing matrix

• Operator instruction matrix

PLANNING

PRODUCT

PROCESS

OPS




DfX

26

• 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 forfailures that are not eliminated

• Timing: After a design before

production

QFD

FMEA

CTQ

Capture Customer

Satisfaction requirements

To MEET

Capture CustomerDissatisfaction

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

28

•Quality

meets the

specification

when new

• Reliability

continues to meet

the specification

through a period

of use

Quality/Reliability

intimately tied tovariability

WEAR OUT

failures

Overall

failures

Early failures

“Infant Morality”

Constant

Failure

Rate

Increasing

Failure

Rate

Decreasing

Failure

Rate

F a i l u r e

R a t e

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 Hazardouswithout

warning Very high severity ranking when a potentialfailure mode affects safe system operation

and/or involves non compliance with

federal safety regulation without warning

10

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

9

Very High System/item inoperable with loss of primary function 8

High System/item operable, bit at reduced

performance level. User dissatisfied 7 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

30

• 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, andmanagement 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)

31

• SBE product development• Virtual Product Development

• Rapid Prototyping

• Customer Experience

• SBE tools for Complete Product life cycle simulation




The Traditional Engineered Process

The Simulation Based Engineered Process

The Engineering Process

32

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

33

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 onwhich 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)

34

•Engineering is the profession in which aknowledge of the mathematical andnatural sciences gained by studyexperience, 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 forunique customer experience with highestperformance and reliability at lowest cost .

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

Si l ti b d E i i D i




Simulation based Engineering Design

(SBED)

35

• 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 firsttime right

• Eventually can be used to predict the future outcome

• Simulations has none of the following limitations of experimentaldesigns /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 NationalCommittee 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

36

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 andthe effects of the Von Karman vortices. In addition, the behavior of thecables 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 thedamage was judged, upon computations, as nonserious. Reason: the

model used did not take properly into consideration the size of the foamdebris.

B. Numerical Treatment Problem

• The Sleipner Accident. The gravity base structure of Sleipner, anoffshore platform made of reinforced concrete, sank during ballast testoperation in Gandsfjorden, Norway, August 23, 1991. Reason: finiteelement analysis gave a 47% underestimation of the shear forces in thecritical 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 theMars Atmosphere. Reason: unintended mixture of Imperial and metricunits.

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/commons/4/42/Mars_Climate_Orbiter_during_tests.jpg

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




Reliability of Simulations

37

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 toactual and accurate performance prediction

Objective is to increase the reliability of simulations.




Simulation and Testing + V&V

38

•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 thecomputational 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

39

•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 zoominto 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




Four Stages of Complimentary Simulation and

Testing for the Engineering Design of First Time

Right Product Development

40



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

tech innovation s3

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