Choice Complexity Tech Decisions

8/11/2019 Choice Complexity Tech Decisions

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October 4, 2014 1 Crafitti Consultin Private Ltd.

INVITED TALK :

CHOICE COMPLEXITY

TECHNOLOGY DECISIONSfor New Product Development

Navneet [email protected]

Phone: +91 9902766961

22 August 2014,

GTRE, DRDO,

Bangalore


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How do we make Decisions?

Dostoyevsky

man acts in the way he feels like acting

and not necessarily in his best interests


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A GAME OF CHANCE

Please give your preference choice from the following

options. You have 4 Ladoos to choose from.

With each choice you will get two benefitsan initialsum of money and a chance to get $1Million in asweepstakes (confirmed to get with specific probability)

Option You Get Immediately (USD) Probability to get $1 Million

Ladoo A 2000 1%

Ladoo B 1000 5%Ladoo C 500 10%

Ladoo D (Minus) 5000 (You Payinitially)

15%


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A GAME OF CHANCEThe results (9 respondents)

Option Number of People Reasons

Ladoo A 4 Probability of 15% is too low hencetake maximum immediately

Ladoo B 1 I want some money assured and I amok to take some risk as well

Ladoo C 3 I can live with 10% chance of winningwith a smaller immediate benefit. The

outflow of 5000 with a likelihood ofnot winning is not all that palatable.

Ladoo D 1 Willing to surrender current gains forfuture value. It is an investment-

however willing to take the risk of

actually loosing

Which Ladoo will you choose?


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A GAME OF CHANCEThe resultsSurprising .Rationality/Objective/Value theory tells us the following formula

Profit = GainsInvestmentValue = probability x Prize + Initial Gain

Option You Get Immediately (USD) (G) Probability (p) to get$ 1 Million in next

three months

Value = p x 1000000 +G

Ladoo A 2000 1% 12000

Ladoo B 1000 5% 51000

Ladoo C 500 10% 100500

Ladoo D (Minus) 5000 (You Pay initially) 15% 145000

The Rational Value theorytells us Ladoo D will give the maximum gains, however, humanbeings give different weights to different parameters hence the variation

1. Ladoo Aguys are sayingLet me get what I am getting now (2000 USD) I know whetherit is 1% or 15% it is sameI will not get it!

2. Ladoo Cguys want to get maximum of both sidescertainty and uncertaintytherebysaying I will not give anything from my pocket (-5000) but still would like to maximize theprobability of getting maximum in the sweepstakes.

3. Ladoo Dis the rational value follower with objective analysis he follows the rationality

based on mathematical analysis.He is taking calculated Risk!!!4. Ladoo Bis the most perplexing! (may be caught in between A and C)


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A GAME OF CHANCEInferences .

Rational Value Theory is no t the way in whic h peop le decide. In real lifepeople perceive specific coarse weights (for example anything below20% is actually 0% probability) (DECISION MAKING IS EMOTIONAL,EXPERIENCE BASED SUBJECTIVE PROCESS)

Giving away what you poss ess now, even i f you have a probabi l i ty ofget t ing high returns is a very di f f icu l t threshold to c ross. (RISKTHRESHOLD) {A Bird in Hand }

Companies that are getting Ladoo A regularly will find it difficult toinvest for Ladoo Deven if the rational value analysis says D is better.

(COMFORT ZONES)

Different people give different preferences to different parameters of aproblem hence it is not a simple matter of arithmetic. (PERSONDEPENDENT)


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Decision Making in a Complex World

Contents

Globalizing WorldComplexity Explosion

Need for Decision Engineering

Problem Solving, Brainstorming,

Innovation and Decision Making

New MethodologiesAHP, DSM, TRIZ,

SBCE, Decision Dependency Matrices

Case Studies

Possible Ways Ahead


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Globe has been Re-engineered!

FlatteningWorld

And we dont even realize it

While the defining measurement of (oldworld) was weight the definingmeasurement of the globalization system is

speedspeed of commerce, travel,communication and innovat ion

Thomas L. Friedman, The Lexus and Olive Tree


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Complexity in aFlatteningWorld

Number of alternatives

Time pressure

Need for analysisInformation de-coherence

Connections

Networks

Human Processing Limits(The Magic Number 7 2)

Framing LimitsConfidence

Rapid Explosionof Complexity

Connections create Value andDependencies create complexity

Future is approaching us Faster than

History is leaving us! Increasing distance between user

requirements of what they really needversus what they want.

With every choice we make today we Kill

many possible futures


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Decision Making in aFlatteningWorld

Number of alternatives

Time pressure

Need for analysisInformation de-coherence

Connections

Networks

Human Processing Limits(The Magic Number 7 2)

Framing LimitsConfidence

Rapid Explosionof Complexity

Each Decision (a Choice) affects futureChoices (decisions)

Each Decision is impacted by past

Decisions (Choices) made by someonesomewhere

With every choice we make today we Killmany possible futures

With every choice we make today we

Select only a small subset of possiblefutures


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The Magical Number Seven, Plus or Minus Two:Some Limits on our Capacity for Processing Information

George A. Miller (1956)Harvard University

First published in Psychological Review, 63, 81-97.

[1] This paper was first read as an Invited Address before the Eastern Psychological Association in Philadelphia on April 15, 1955.

The point seems to be that, as we

add more variables to the display,we increase the total capacity, butwe decrease the accuracy for anyparticular variable. In other

words, we can make relativelycrude judgments of several

things simultaneously.


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DECISION MAKINGInvolves Choice

Def: Choiceof an alternative(s) amongst a set of alternativeson some basisor criteria (usually many, often conflicting) to meet one or multiple objectivesby one or more actors

.

.

.

GOAL (s) CRITERIA

.

.

.

ALTERNATIVES

.

.

.

Actors/Decision Makers(Group Decision

Making)


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Theory and PracticeHow Decisions are made inOrganizations*

NORMATIVE METHODS- What should be done based on rational

theories of choice

DESCRIPTIVE BEHAVIORWhat is actually done by individuals andgroups in practice

* Hoch & Kunreuther, A Complex Web of Decisions, Wharton on Making Decisions, Wiley 2006

PrescriptiveRecommendations

NormativeModels

DescriptiveBehavior

Rational Behavior Maximize Utility

Individuals do notmaximize their Utility

in practice

Managers often are so caught up in

making decisions that they rarely have

the luxury of giving much thought tohow they make them

Spending time thinking

about the process of

decision making can have

significant payoffs,however, because it can

help you improve quality

and effectiveness of

subsequentchoices


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Case for Decision Engineering

An Emerging Disciplinefor developing Tools

and Techniquesfor informed Operational and

Business Decision Making/Problem

Solving/I nnovationwithin Industry bycollating and exploiting distr ibuted

organizational knowledge


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SituationAssessment Explanation

Forecasting

OptionsGeneration

MakingDecision -Choice

Stages & Requirements in Decision Making

Situation

Assessment

Data CollectionData Cleansing

Data Collation

Classification

Observation

Explanation

Causal

Analysis

Cognitive

Mapping

Systems

Analysis

Forecasting

Historical

Analogies

General

Analogies

Prediction

Projection

Forecasting

What if

Analysis

Options

Generation

Decision

Trees

Scenario

Writing

Alternatives

Brainstormin

g

Solution

Choice

Optimization

Decision

Making under

uncertainty and

partial

information


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Situation AssessmentWhere do we stand?Simple Indicators and Checklists/ Complex Indicators/ Scaling (R-factor Analysis)/Typologies (Q-factor Analysis) /Cluster Analysis /Multidimensional Scaling/ Artificial

Neural Networks (ANN)/ Value Stream Mapping / TRIZ9 Windows/ TRIZ- Ideal Final

Result

ExplanationWhy are things as they are?Correlation Analysis/ Regression Analysis/Analysis of Non-Linear Relationships/ Partial

and Multiple Correlation Analysis/ Multiple Regression Analysis/ Path Analysis

ForecastWhat will happen?Systematic Expert Judgment/ Decision Matrix/ Analytic Hierarchy Process/ BayesianInference/ Cross-Impact Analysis/ Early warning Indicators/ Extrapolation with Moving

Averages/ Trend Analysis/ Time Series Analysis/ Spectral Analysis/Combined Trend andTime Series Analysis /Trend Impact Analysis

Preparation of DecisionsWhat are the Options?Game Theory/ Gaming/ Computer Simulation/ Cellular Automata/ Petri Nets/ Econometric

Models/Mathematical Modeling / TRIZ

ChoiceWhat to do?Decisional Trees/ Decisional Matrix/ Linear Partial Information (LPI) Analysis/

Linear/Integer/Non-Linear Programming/ Heuristic Optimization TechniquesGeneticAlgorithms, Simulated Annealing, Tabu Search, Artificial Life / AHP

Techniques & Methodologies


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Skill Set & Capabilities to beDeveloped/Acquired

Data Sampling and Collection

Statistical Analysis Data Visualization

Mathematical Modeling

Petri Nets

System Dynamics

Cellular Automata

Process Algebra

Simulation

Systems Analysis

Optimization Tools andTechniques

Linear and Non Linear

Heuristic Optimization

Operations ResearchQueuingTheory

Industrial Engineering

Quantitative Techniques forExtracting Judgment

MAUT/ AHP

Methods for Group DecisionMaking and Consensus

Risk Analysis Methodologies

Crisis and Disaster ManagementModels

Crisis Gaming/Business Gaming

Effort/Cost Estimation Models

Knowledge Management Models

Business Intelligence Generationand Management

Artificial Life Techniques

Game theory

TRIZ


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Decisions need to be Engineered.

Strategic/Policy

Evaluating Options/Alternatives Evaluating Factors affecting a particular decision

Evaluating ROI/ Cost Benefit Analysis

Evaluating Uncertainty

Market Analysis/ Technology Forecasting

Operational Process Evaluation

Process Optimization

Performance Evaluation

Evaluation of Quality AttributesReliability/Availability/Survivability

Project/Program Execution

Technology Evaluation

Choosing a Product

Benchmarking products

Evaluating Architectures


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Multi Criteria Decision Making

A decision may need to be taken on the basis ofmultiple criteria rather than single criterion

Assessment of various criteria and evaluation

of alternatives on the basis of each criteriaAggregation of these evaluations to achieveranking of alternatives

The problem is further compounded when thereare multiple experts whose opinion needs to beincorporated in the decision making

Th P bl


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to organize multiple criteriato assess multiple criteria (on the same scale)

to evaluate alternatives

to rank alternativesto incorporate judgments of multiple experts

The Problem

Lack of adequate quantitative information leadsto dependence on Intuition, Experience andJudgment of Knowledgeable persons calledEXPERTS


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COMPARING APPLESWITH ORANGES

AN EXPOSITION OF AHP


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Analytic Hierarchy Process

Similar to Decision Matrix approach but allows a

wider range of values in comparison of alternatives

Developed by T.L. Saaty (1980)

AHP invites pair-wise qualitative comparisons

The problem is organized as a hierarchy

Bhushan N. and Rai K., Strategic Decisio n Making App ly ing th e Analyt ic Hierarchy Process, Springer,2004


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Analytic Hierarchy Process For DecisionMaking

Goal

Criterion 1 Criterion 2

Sub Criterion 1.1 SC 1.2 SC 2.1 SC 2.2

Alternative 1 Alternative 2 Alternative 3Alternative 1


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AHP Example

Organize the problem as a Hierarchy

Car Rating

Cost Dependability Size Aesthetics

Santro IndicaZen


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AHP Example

Collect Data in the specified formats

Crit. EI SI I MI EQ MI I SI EI Crit

Cost Dep

Cost Size

Cost Aest

Dep Size

Dep Aest

Size Aest

9 7 5 3 1 1/3 1/5 1/7 1/9

EI : Extremely Important SI:Strongly Important I: Important

MI: Marginally Important EQ: Equal


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Evaluation of Criterion

Cost Depend. Size Aesthetics NEV

Cost 1 5 7 9 0.64

Depend. 1/5 1 3 7 0.21

Size 1/7 1/3 1 6 0.11

Aesthe 1/9 1/7 1/6 1 0.04

AHP Example

Reciprocal Matrix

Eigen Value: 4.36 Consistency Index: 0.123 Consistency Ratio:

0.137


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Criterion Weight Rating of Cars using AHP

Zen Santro Indica

Cost 0.64 0.14 0.43 0.43

Depend 0.21 0.48 0.41 0.11

Size 0.11 0.28 0.65 0.07

Aesthetics 0.04 0.27 0.06 0.67

Weighted Sum 0.23 0.44 0.33

Ranking III I II


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AHP : Consistency Evaluation

Principle Eigen Value (Lamda)

|AI| = 0

Order of matrix (n)

For Perfect Consistency : = n

Define Consistency Index (CI) as

CI = | n|/ (n-1)

Random Index (RI)

CI has been generated for random reciprocalmatrices of various order e.g. n= 4 RI= 0.90

Consistency Ratio = CI/RI

CR < 0.1 matrix is consistent

If the Matrix is filled intotally random manner

the average CI = RI

CR measures how far the matrix

is from total inconsistency


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Why AHP ?

Qualitative Inputs

Pairwise comparisons

Gartner

Reduces Complexity of decision by organizing it instructured format

Promotes comparison of homogeneous characteristics

Framework to check logical consistency

Facilitates What if Scenarios

It undermines Political Agendas

Facilitates Senior Managers buy-in

Sound Mathematical Basis

Consistency Evaluation


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CASE STUDY

Mobile ApplicationsForecasting


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Legend

Frozen

Cold

Warm

Hot

Red Hot

KiSS (Killer Sure Score)

< 5.0

5.0 20.0

20.0 50.0

50.0100.0

> 100.0

Killer Sure Scores

Y Y Y


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Travel

Finance

Retail &Distribution

Infotainment

North America Europe Asia Pacific

BP CP

Youth

BP CP BP CP

KiSS Analysis of Infostations

Youth

Youth


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CASE STUDY

Supply Chain Forecasting


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Problem Statement

Forecast data from a leading retailer,

The errors shown above are a consequence ofcombination of factors

Actual Errors

Assumption

2% reduction

* Assumption $1 mn investment

Improvement

% Error $ Cost*

Basic Item

Fashion Item

% Error $ Savings*

13.1% 160,000

139.6% 580,000

11% 32,000

137.5% 106,000


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Three opportunities for Improvement

Areas

Reconciliation of Initial

forecast with Business

Plan

Over-riding of

Reconciled Forecast

Allocation of

Reconciled Forecast

Current Methodology

Initial forecast generated by the

tool is reconciled with the Business

Plan by Inventory & Financial

personnel based on their

experience

Planner based on his experience

can over ride the reconciled

forecast

The reconciled category-levelforecast is allocated to lower

levels based on planners

experience and the tool

Risk

Judgmental errors

Limited ability to

consider and process

large amounts of

information

Judgmental Error

Mis-match with

Business Plan

Judgmental Error

Non- optimal

Allocation

May reduce Category

profitability


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Allocation ofReconciled Forecast

Reconciliation ofInitial forecast with

Business Plan

Over-riding ofReconciled Forecast

Reconciliation ofInitial forecast with

Business Plan

Over-riding ofReconciled forecast

Allocation ofReconciled Forecast

Of the three decision areas mentioned where AHP canbe applied, we have chosen Reconciliation of Initial

forecast with Business Plan, for illustrative purpose

Improvement AreasDecision Criterion


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Application of AHP

Calculation of Weights Inventory and Financial personnel individually compare each

criterion against other criteria in the format shown below This matrix checks for consistency of the ratings


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Application of AHP

Calculation of Weights As per the comparisons, weights for each criterion are

calculated individually for Inventory and Financial personnel


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Application of AHP

Calculation of Weights Weights for each criterion calculated using inputs fromInventory and Financial personnel are consolidated


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Application of AHP

Calculation of Reconciled Forecast A category that is to be forecasted is rated across each

criterion on a scale of 01 for two forecasting periods


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Application of AHP

Reconciled Forecast is calculated using the ratio of the ForecastIndicesof the two forecasting periods

Forecast (period 2) = (Relative Forecast Index) X Forecast

(period 1)

Forecasted sales for the period 2, is the reconciled forecast onapplication of AHP


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Continuous Improvement

As a Continuous Improvement process, a part of theerror from the previous forecasting period is added asa feedback to the current forecasting period.

This process synchronizes the Forecasted Sales tothat of Actual Sales, thereby reducing error

Inventory

Inputs

FinancialInputs

Applicationof AHP

ForecastReconciliation

Errorcorrection

Error


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Decision Engineering, TRIZ,Decision Dependency Matricesand Set-Based Thinking for

GLOBAL PRODUCTDEVELOPMENT


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2006 Businessweek most manufacturers understand what Global

Produ ct Developm ent (GPD) is and why it is impo rtant but few real ly

understand how to make it successful.

Product Development going Global

Teams around the globework together toconceive, design anddevelop new products.

The commercial valueproposition

Leverage the Globe Time to market

(24x7 Enterprise)

Cost,

Innovation

Quality/ Robustness


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Co-located teams

Cross-functional interactions,

Informal collaborations

High bandwidthcommunications (face to facediscussions)

Product Development going Global

FROM

Globally dispersed

Culturally un-adjusted

Non-e-mailcommunicationminimized

Physically unaware teams

Spread over thousands ofmiles to collaborate onnew product development

TO

Fuzzy front end of New Product Development amplified in Global Product

Developm ent Scenar ios


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Dimensions of Global Product Development

GEOGRAPHICAL BOUNDARIES

Near-shoring (within samecountry)

Off-shoring (across countries andcontinents)

ORGANIZATIONAL BOUNDARIES

In-House (within same enterprise)

Outsourced (across differententerprisesClient Vendorrelationship)

Collaborated(across differententerprises - peer to peerrelationship)

KEY ENABLERS

Fully digital productdevelopment process

Internet connectivity forbusiness

Global skilled labormarket

International

collaborationexperience.


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Global Product Development Scenarios

GPD Scenarios Geographical Boundaries Organizational Boundaries

Near-

Shoring(same

country)

Off-Shoring

(acrosscountries

and

continents)

In-house

(withinsame

enterpr

ise)

Out-Sourced

(acrossdifferent

enterprisesclient vendor

relation)

Collaborated

(acrossdifferent

enterprise

speer to peer

relation)

Outsourced

Near-shoring

Outsourced

Off-shoring

In-house Near-

shoring

In-house Off-

shoring

Collaborated

Near-shoring

Collaborated

Off-shoring


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LEAN Product Development

How fast one converges to the final design?

Set-Based Concurrent Engineering (SBCE)

A counter-intuitive method of product developmentwhereall alternatives are kept alive as long as possible in theproduct development journey


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Convergence in Design

How to converge from an initial set of conceptual ideas to one idea

that will become the final Design?Early Convergence Strategy -

Point-Based CE

Design Space ChosenDesign

CriticalAnalysis

Modification

DESIGN CHURNING

Toyotas Slow Convergence Set-Based CE

Large Design SpacesIntegration

of Sets

ELIMINATE WEAKEST

ALTERNATIVES

S S B d C E


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Second Toyota Paradox Set-Based Concurrent Engg

SBCE leading to slow convergence seems like an inefficient and

expensive way to develop products, however, Toyota creates newautomobiles faster than industry average with less effort!!

SET-BASEDCONCURRENTENGINEERING

Mapping of the Design SpaceConceptualR

obustness

Integration by Intersection

Feasibilityb

eforeCommitm

ent

ConflictHan

dling


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SBCE ApproachThe Five Key Principles [2]

1. Mapping the Design Space

All functional departments identify thesolution space independent of others

Communication between departmentsis based on Design SpacesNot onSingle Ideas

Discussion is kept vague and abstract

2. Striving for Conceptual Robustness

Design remains functional aftervariations in its environment

Will the Design still fit the solution spaceafter some time?

Create Designs that work regardless of

what the rest of the team decides to do

3. Integration by Intersection

Overlap of feasible design spaces of thedifferent sub systemsdirectlytranslatable into acceptable solutions

A decision once taken has to berespected by all

Taking late decisions means that moreimportance has to be given to thedecision and hence more effort should

be spent

4. Feasibility before commitment

Multiple concepts considered in parallelprototypes created and infeasible ones

eliminatedEach concept is analyzed from thereasons why a concept is still (in)feasible and the role and impact ofproblem in the overall product


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SBCE ApproachThe Five Key Principles [2]

SBCE reduces the cost of taking back a decisionearlier made, hence thereis more room to improve the concept while developing it. Wrong decisions

in later phases of the development process do not have much impact on

cost and are far less time-consuming than if these would have been made in

beginning

Client Assisted Design Advice SystemSolutions that meet the needs of thecustomer based on

Equality between all related parties

Avoiding asymmetric dissatisfaction to any party in particular

Equality of priorities of different points of viewTwo types of subsets of problems

Competition Vs Cooperation

Domain Level Vs Control Level Conflicts

5. Conflict Handling


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SBCEIdeally Suited for Global Product Development Scenarios

SBCE can help in maximizing the dispersed creative capabilities of the teamsat various geographic locations

With each dispersed team making their components robust while exploringmany alternatives; the global product development teams can leverage thetrue potential of GPD

The coordination effortwhich is an overhead will be minimized

Designs that do not meet the tolerance limits will automatically be eliminatedin the process


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Decision Dependency Matrices

Bhushan N., Decision Dependency Matrices, 8thInternational DSM Conference, Seattle, US, Oct 2006

D i i P i t h S t f Alt ti


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Decision Point has Set of Alternatives

Each Decision Pointhas Set of

Alternatives, Criteriaand Goals

GOAL (s) CRITERIA ALTERNATIVES

ACTORS / DECISION MAKERS

E i ti D i i Th


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Existing Decision Theory

Focuses on a Single Decision Point

Does Not Consider Dependency of other/past decisions on the existing

decision situation Dependency of existing decision on other/future decisions

GOAL (s) CRITERIA ALTERNATIVES

ACTORS / DECISION MAKERS

Rank Orders eachAlternative with respectto each criteria and also

with respect to each other

on the basis of contextualdefinition of VALUE

D i i D d D i i


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Decisions Depend upon Decisions

Decisions Depend upon other decisionsinSpace

and Time Decision Map- Decision Points (DP) in space time

(Nine-Windows Approach*)

1

2 6

5

4

3

7

8

9

11

10

TIME

PAST PRESENT FUTURE

SYSTEM

SUPER

SYSTEM

SUBSYSTEM

* Mann D, Hands on Systematic Innovation,

Creax Press, 2002

Existing Approaches for Network of Decisions


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Existing Approaches for Network of Decisions

Typically the problems addressed are Chain of

Decisions (e.g. Bullwhip Effect defined in the MITsBeer Game) and Methodologies include

Queuing Theory for Network of Queues

Bayesian Belief Networks for probabilisticreordering of inferences from evidences in adynamic situation

Modeling and SimulationSystem Dynamics

(Colored) Petri Nets

Decision Chains (Qualitative Analysis)

Complex Web of Decisions


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Complex Web of Decisions

Network of Decisions has not been addressed

explicitly and comprehensively in literature

The Increasing Global Complexity (mainly due to

multi-dimensional interdependencies) demands acomprehensive methodology to surface decisioncomplexity as it plays out in time and space

Creating a visibility about the impact of eachdecision (read each choice) on other decisions isthe need of the Globalizing world

DECISION DEPENDENCY MATRICES


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DECISION DEPENDENCY MATRICES

We propose a new class of DSM called

Decisio n Dependency Matr ix (DDM).

Def: DDM is a binary square matrix representation ofinterdependencies of all known past, present, future, super-system,

system and subsystem decision points in a decision map.

DDMs can estimate the decision complexity of the system

Each DDM has an associatedAlternatives Dependency Matrixthat

is used for choosing the right alternative at a given decision point ina Space-Time Decision Map

Creating a Decision Dependency Matrix


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STEP 1: Create a Space-time Decision Map

1

2 6

5

4

3

7

8

9

11

10

TIME

PAST PRESENT FUTURE

SYSTEM

SUPER

SYSTEM

SUBSYSTEM



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STEP 2: Identify Decision Dependencies

1

2 6

5

4

3

7

8

9

11

10

TIME

PAST PRESENT FUTURE

SYSTEM

SUPER

SYSTEM

SUBSYSTEM

DDM Time Dependency Representation


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DDMTime Dependency Representation

Decision

Points DP1 DP2 DP3 DP4 DP5 DP6 DP7 DP8 DP9 DP10 DP11DP1 1 1

DP2 1 1 1

DP3 1 1

DP4 1

DP5 1 1

DP6 1 1 1 1 1 1 1

DP7 1 1 1

DP8 1 1

DP9 1 1

DP10 1 1 1

DP11 1 1 1

DDM can give a measure of how much the


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DDM can give a measure of how much the ...

Past depends upon Past

Past depends upon Present

Past depends upon Future

Present depends upon past

Present depends upon Present

Present depends upon Future

Future depends upon Past

Future depends upon Present

Future depends upon Future

DDM Time Dependency Representation


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DDMTime Dependency RepresentationPAST PRESENT FUTURE

Second Order

Dependencies

First Order

Dependencies

Third Order

Dependencies

PAST

PRESENT

FUTURE

Decision

Points DP1 DP2 DP3 DP4 DP5 DP6DP

7 DP8 DP9 DP10 DP11

DP1 1 1

DP2 1 1 1

DP3 1 1

DP4 1

DP5 1 1

DP6 1 1 1 1 1 1 1

DP7 1 1 1

DP8 1 1

DP9 1 1

DP10 1 1 1

DP11 1 1 1

Creating the Alternatives Dependency Matrix


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Creating the Alternatives Dependency Matrix

Let us consider a small example with 6 Decision Points

Decision Map

1

2 6

43

5

TIME

PAST PRESENT FUTURE

SYSTEM

SUPER

SYSTEM

SUBSYSTEM

Alternatives and Values using MCDM


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Alternativesand Values using MCDM

DecisionPoint (DP)

Alternative Alternative Value

DP1A11 0.45A12 0.35

A13 0.20

DP2

A21 0.70

A22 0.15

A23 0.15

DP3A31 0.50

A32 0.50

DP4

A41 0.30

A42 0.35

A43 0.35

DP5A51 0.60

A52 0.40

DP6

A61 0.05

A62 0.50

A63 0.45

Alternatives Dependencies


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Alternatives Dependencies

Alternative Dependencies(Quantitative Rating)

Explanation

BindingSynergy (BS) = (1.5) If the independent Alternative has been chosen,the dependent Alternative will have to be chosenand together they give move value compared toindividual Alternatives.

Binding Degradation (BD) = (0.5) If the independent Alternative has been chosen,the dependent Alternative will have to be chosen

and together they give less value compared toindividual Alternatives.

Binding (B) = (1.0) If the independent Alternative has been chosen,the dependent Alternative will have to bechosen.

Synergy (S) = (1.25) Together they give move value compared to

individual.

Degradation (D) = (0.75) Together they degrade the value of each other

Conflict (C) = (-1) Only one of Alternatives can be chosen from thepair

No Dependency (ND) = (0) Alternatives do not directly depend upon each

other

The Algorithm for Alternative Dependency Value


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The Algorithm for Alternative Dependency Value

Create a pair-wise comparison matrix of qualitative dependencies ofalternatives

Convert the qualitative dependencies to quantitative dependencyratings to form Alternatives Dependency Matrix (ADM)

ADM is converted to a Direct Connection Matrix (DCM) using thefollowing rule i f ai j> 0 bi j= 1 else bi j= 0, where, aijis an element of

ADM and bij is the corresponding element of DCM

Alternative Dependency Value Matrix (ADVM) is created such that

dij= aijx bijx Vj

where Vjis the MCDM Value of the alternative Aj

The Alternative Dependency Value (ADV) is defined as the column sumof ADVM for each alternative value.

Alternatives Dependency Matrix


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Alternatives Dependency Matrix

Create Direct Connection Matrix & AlternativeDependency Value Matrix


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Dependency Value Matrix

Alternative Dependency Value (ADV)


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Alternative Dependency Value (ADV)

Path analysis on this matrix can lead to a detailed exercise in finding the optimalpath in alternatives matrix which maximize the Value ( this include the currentvalue and the value due to dependencies on other alternatives)

However the Alternative Dependency Value defined as above gives much quickerand easier measure of relative value of the alternative taking into account thecurrent value and dependent value to create much clear visibility of the decisionmap

DDM compared to local MCDM based best decisionmaking


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DP Local Best

Decision w/o DDM

Using Connected

Alternative ValueRanking

DP1 A11 A12 A13 A11 A12 A13

DP2 A21 A22 A23 A21 A22 A23

DP3 A31 A32 A31 A32

DP4 A41 A42 A43 A41 A42 A43

DP5 A51 A52 A51 A52

DP6 A61 A62 A63 A61 A62 A63

making

Decision DependencyMatrices

Vs

Existing Local BestAlternative using

MCDM

We propose use of Decision Dependency Matrices [5](DDM) asf k f SBCE


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a framework for SBCE

Consider the development of a (group) decisionmaking system using wireless interface to a

complex simulation at the server side. Let Mainsoftware modules be

1.Database Design

2.Client Side User Interface

3.Basic Report Generator4.Complex Decision Simulation

5. User Interaction Model

6.Caching Algorithm

DB

SBCE Using DDMMapping the Design Space


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g pp g g p

Client Side UICaching

User InteractionModel

Database

Basic ReportGenerator

Complex Decision

Simulation

SBCE Using DDMGenerating Alternatives


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g g

Text UIGUIMM UI


Client Side CachingPre-processed CachedBoth Side Cached


Multi-PartyEvent BasedFixed Time-Slot

Database

RelationalObject Oriented


Fast AlgorithmLow Memory Footprint

Complex Decision

Simulation

Fast AlgorithmHi Accuracy AlgorithmLow Memory Footprint

SBCE Using DDMAlternatives Dependency Matrices


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SBCE Using DDMIntegration by Intersection


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g g y

The Elimination of AlternativesUsing ADVs


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g

Text UIGUIMM UI


Client Side CachingPre-processed CachedBoth Side Cached


Multi-PartyEvent BasedFixed Time-Slot

Database



Fast AlgorithmLow Memory Footprint

Complex Decision

Simulation

Fast AlgorithmHi Accuracy AlgorithmLow Memory Footprint

The Elimination of AlternativesEvolution of Optimal Design


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Text UIGUI


Client Side CachingPre-processed Cached


Event BasedFixed Time-Slot

Database



Fast Algorithm

Complex Decision

Simulation

Fast Algorithm

The Process Starts with Large Design Spaces


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And Ends with an Optimal design with very less largeIterations .At each stage the DDMs get shorter and

shorter.

SBCENot much success outside Toyota


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y

Although SBCE is known for many years and many research publicationshave described the process, it has not been picked up by many companies

as pr inc ip les are counter intu i t iveand in time and budget constrainedcommercial organizations, it becomes very di ff icu l t to not to show onedesign quick lyso as to show the development project is on the right track tothe top management.

The in format ion, decis ion, design and o rganizat ion com plexi tyalsoincreases as SBCE as a process requires strict discipline in following theprocess by everyone as there is no central control, i t creates a self-organizing sy stem.

Further, the SBCE principles dont describe specific methods, techniques,

tools or frameworks for execution. It is this important gap that TRIZ

(Theory of Inventive Problem Solving) can help in bridging for

the global product development scenarios.

A Brief History of TRIZ


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TRIZInventive Problem Solving by Altshuller

Teoriya Resheniya Izobreatatelskikh Zadatch1946 PatentOfficer inRussian NavyDiscoveredpatterns inpatents,

published paper.Sent to Gulag1954 released,analysed2,500,000patentsIdentified what

makes asuccessfulpatent1956-1985 TRIZformulated

Worlds best ideasin this situation

(Access)

A situation like mine(Abstraction)

Access

WorldKnowledge

Base

Abstraction

My specific solutionMyspecific situation

Specific

Me / my companyEvaluate

Same Problems and Solutions appear again and again but indifferent industries

There are a series of recognizable Technological Evolution pathsfor all industries

Innovative solutions used theories outside their own area/industry

The most powerful solutions uncover and eliminate contradictions

Questions that TRIZ asks you


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1. What is my Ideal Final ResultHow

can I achieve the functionalitywithout spending any resources orcost

2. How the problem/situation/systemlooks in time and space coordinates

3.Am I using all the existing

resources or potential resources tothe fullest

4.What is themain useful function Ineed to deliver. What are variousways in which I can deliver thefunction

5.How others have solvedthe sameproblem in the past

A

B

C

TreeSeed

ForestPlain

DNA Fruit

Coal

Timber

Pie

Principles in the Contradiction Matrix


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Parameter that gets worse

Recommended principles

Parameter toimprove

TRIZ - Contradiction Matrix Elements1. Weight of moving object2. Weight of stationary object3. Length of moving object4. Length of stationary object5. Area of moving object6. Area of stationary object

7. Volume of moving object8. Volume of stationary object9. Speed

10. Force11. Tension, pressure12. Shape13. Stability of object

27. Reliability28. Accuracy of measurement29. Accuracy of manufacturing30. Object affected harmful effects

31. Object generated side effects32. Manufacturability33. Convenience of use34. Repairability35. Adaptability36. Complexity of device37. Complexity of control38. Level of automation39. Productivity

14. Strength15. Duration of action - moving object16. Duration of action - stationary object17. Temperature18. Brightness19. Use of energy by moving object

20. Use of energy by stationary object21. Power22. Waste of energy23. Waste of substance24. Loss of information25. Waste of time26. Amount of substance

1. Segmentation

2. Extraction3. Local Quality4. Asymmetry5. Combination6. Universality

7. Nested Doll8. Counterweight9. Prior Counter-Action

10. Prior Action11. Prior Cushioning12. Equi-potentiality13. The Other Way Round14. Spheroidality15. Dynamics16. Partial or Excessive Action

17. Another Dimension18. Mechanical Vibration19. Periodic Action20. Continuity of Useful Action

21. Skipping22. Blessing in Disguise23. Feedback24. Intermediary25. Self-Service

26. Copying27. Cheap/Short Living28. Mechanics Substitution29. Pneumatics and Hydraulics30. Flexible Shells/Thin Films31. Porous Materials32. Colour Changes33. Homogeneity34. Discarding and Recovering35. Parameter Changes36. Phase Transitions

37. Thermal Expansion38. Strong Oxidants39. Inert Atmosphere40. Composite Materials

40Princip

les

TRIZA Brief Overview


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TRIZ Tools for problem formulation

Focus on Function Main Useful function that product

needs to deliver to meet a customer/user need

Value is Function delivered to meet a user need

Ideal Final Result Value delivered at no cost or

resource expenditure and not harming the system in

anyway, alternatively the function is achieved on its

own self functioning system

How does the problem/situation looks in space and

time using what in literature is called the Nine

Windows Approach

How does the problem looks in depth and scope by

using Why-What Hierarchy

What are the resources available and what theconstraints in and around the problem

Function and attribute analysis

S-Curve analysis where the field is on the S curve

and where the product that needs to be designed for

customer needs should focus on

TRIZ Tools for problem solving

Technical Contradictions Inventive Principles

Physical ContradictionsTrends of Evolution

Resources Are all the resources utilized fully

even the harmful resources as well

Knowledge and effects the codified knowledge

of how others have achieved a particular function,

Ideal Final Result How to take the system closer

to IFR rather than focusing on current issues can

a method be devised to achieve IFR

S Fields and Standard solutions

Psychological Inertia tools that TRIZ has to take

the inventors mind away from the tunnels of corecompetence that restricts exploration of other

fields.

Anticipatory Failure Determination (AFD) or

Subversion Analysis Inventing failures to create

robust designs

SBCEMapping the Design Space


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SBCE Steps Specific Actions TRIZ and Other tools

Mapping the

Design Space(Functional Team

level)

Describe user needs

In case of multiple needs carry outneeds interdependency analysis

Find out key functions to be

performed

Function dependency analysis to

find out interdependencies

Can some high level functions

specific to strengths of different

teams be identified

Let each team explore the

specifications, needs, functions

independent of each other

Each team explore design tradeoffs

through simulations and their pastobservations

Each team should come up with

their sets of different solutions with

in the functional and performance

needs of the product

Problem Formulation and Analysis

Ideal Final Result (IFR) Why-what hierarchy

Nine windows

Dependency Structure Matrix

(DSM) [28]

Function/Attribute Analysis

System Complexity Estimator (SCE)

[4]

S curve analysis

Searching for Solutions

Contradictions Technical/Physical

Trends of evolution

SBCEConceptual Robustness & Integration by Intersection


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Striving for Conceptual

Robustness

(Functional Team level)

Design should remain

functional after variations in

its environment Vulnerability of system to

changes in the environment

should be minimized

Modularized Design with

standard components

IFR

AFD/Subversion Analysis

Robust Inventive SystemDesign (RISD) [7]

DSM

Integration by Intersection

(System level) How are the parts integrated

to meet at the point that will

be regarded best solution

Find out overlap of feasible

design spaces for each sub

component

Decisions about eliminating

the weak designs

Decision Dependency

Matrices (DDM) [5, 6]

Analytic Hierarchy Process

(AHP) [8, 14, 22]

Technical Contradictions

(TC)/ Inventive Principles

(IP)

SBCEFeasibility before commitment & Conflict Handling


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EstablishFeasibility

before

Commitment

Multiple concepts developed usingprototyping simulation

The infeasible ones will be rejected

rest all will continue to be

developed

Decisiontheoretic

principles [20,

21]

AHP

Closer to IFR

Conflict

Handling Cooperative Conflict handling Which solution

is closer to IFR?

DDM

AHP

GPD ScenariosValue of TRIZ in SBCE


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TRIZ with SBCE for

Inhouse Scenarios

00.2

0.4

0.6

0.8

Mapping the Design

Space


Robustness

Feasibility before

commitment

Integration by

Intersection

Conflict Handling

In-house Near-shoring In-house Off-shoring

TRIZ with SBCE for

Collaborated Scenarios

0

0.2

0.4

0.6

0.8

Mapping the Design

Space


Robustness

Feasibility before

commitment

Integration by

Intersection

Conflict Handling

Collaborated Near-shoring Collaborated Off-shoring

Complexity

Time

TRIZ relevance compared to other methods in GPD Scenarios using SBCE


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GPD

ScenariosMapping the

Design SpaceStriving for

Conceptual

Robustness

Feasibility

before

commitment

Integration

by

Intersection

Conflict

Handlin

g

OutsourcedNear-

shoring

Medium High Low Medium Low

OutsourcedOff-shoring

Medium High Low Low Low

In-houseNear-

shoring

High High Low Medium Low

In-houseOff-shoring


CollaboratedNear-shoring

High High Low Medium Low

Collaborated

Off-shoring


Set Based Concurrent Engineering


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95

Conclusions

Decision Engineering and Problem Solving in a ComplexWorld


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Three winners in the new worldDecision Engineering, Lean and TRIZ

Decision Engineering and Innovative Problem Solving is the need

Enterprises need integrated frameworks leveraging the advances made inInnovation, problem solving and decision engineering space

In the Globalization scenariosOld methods are failing

Global Complexity is increasingNeed for New ways of doing thing

Frameworks described here can be usedat least some of the techniques

definitely can be explored

Choice Complexity Tech Decisions

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