The Grammar ofTechnology Development
for Value Creation
Hiroe TSUBAKI
The Institute of Statistical Mathematics

Contents of Presentation• Introduction– Perspectives of Development of Statistical Methods after “The
Grammar of Sciences”– Activities in Japanese TQM• “The Grammar of Technology Development” and Its
Methodologies Developed in Japan• Activities by Statisticians• International Standardization Activities
in ISO TC69 SC8• Establishment of VCP-NET (SNS) by ROIS and
JSA• Concluding Remarks

From the Grammar of Science
as an Interface Between Statistics and Sciences
to The Grammar of Technology Development
as a one of the Interfaces Among Statistics, Relevant Methods andEngineering

INTRODUCTION:

Karl Pearson (1892) The Grammar of Science• A man gives a law to Nature– Statistical Science
as a new way to Scientific thinking in the 20th century.
• Systematic ways to descript “a scientific law”
• Not Scientific Objects but Scientific Process– Plan: Statistical Methods for Planning» Careful and accurate classification of facts» Observation of their correlation and sequence– Do: Constructing Scientific Laws
» Discovery of scientific laws by aid of creative imagination– Check: Checking the Laws
» Self-criticism and the final touchstone of equal validity for all
normally constituted minds• Development of Statistical Methodology as the Supporting tools for
the Scientists along the Grammar– Probabilistic interpretation of cause and effect– Statistical description of a scientific law

Historical Views of applied statistics• Biometrics for Recognition• Techonometrics for Design– Shewhart(1931)• Economic Control of Quality– Process Control» Continual Detectionof Assignable Causethrough Outlying
Facts Detection– Taguchi(1957, 1972, 1976)• Systems of Experimental
Design• Parameter Design, 1984
– Effective Improvement of
Function» Robustness against
Noise Factors– Galton(1884)• Statistical Sciences– Consensus through Discussion– Pearson(1892, 1901, 1911)• The Grammar of Science– Man gives a law to Nature– Fisher (1935)• Design of Experiments– Effective Improvement of
Characteristics•

They used “scientific laws”
estimated by statistical methods inorder to improve their concerning
characteristics•

Man can improve the law for himself– by finding phenomena beyond
the current law or tuning thecontrollable parameters in thelawTraditional TQM Methodology
developed in Japan• Problem Solving– Strategy: QC story,– Tools: Q7, SQC, N7, P7• QC story• Problem Solving Procedure• Procedures for Solving Task-Achieving-Type Problems

Problem Solving Procedure since 1960s
and Elementary Education in NZ– Selecting a Theme– Understanding theCurrent Situation andSetting Targets– Creating a Plan of Action– Analyzing the Factors– Developing and
Implementing
Countermeasures– Confirming Effectiveness– Standardization andEstablishing Control

Traditional Q7 for SQC– Parato Diagram• To get a handle on thereal problem amongmany– Cause and EffectDiagram• To search out andOrganize all Possible
Factors– Classification• Characterization of
Objects– Check Sheets• To take down data simply– Histograms• To understand the form of
a distribution andcompare it to a standard– Scatter Diagrams• To find the correlation– Control Charts• To investigate whether aprocess is stableN7: Qualitative Analysis P7: Process Oriented
Prof. Kanda (1994)– Relation Diagrams– Affinity Diagrams– System Diagrams– Matrix Diagrams– Matrix Data Analysis– PDPC Method– Arrow Diagram• Group Interview– To Clarify Needs• Investigation by
Questionnaire– To Verify Needs• Positioning Analysis– To Grasp positioning ofvarious products in themarket• Two Creative Thinking
Methods– To Clarify Concepts• Conjoint Analysis– To Optimize Concept• QFD– To Transform the Concept
into Design

Trans-disciplinary Engineering• Lecture by Dr. Gennich Taguchi, 1975/04/22– Effective Information Collection by Fisher– Effective Information Communication by Shannon• Engineering for Design and Development Process(2002-, in Trans-disciplinary Federation of Scienceand Technology– Toshihiro Hayashi• TRIZ⇒QFD⇒Taguchi Method• Tsubaki, Nishina and Yamada eds.(2008)– “The Grammar of Technology Development”

The Grammar ofTechnology Development
by Tsubaki, Nishina and
Yamada eds. (2008) ,Springer.

Step 1: Value Selection•
Value
Selection

Translation

•

•

Value
Injection

Optimization

Objectives– Selection of values with targets by defining
expected VOC
Methods– Predicting and analyzing the difference ofuser’s performance between the real existingsociety and the virtual society affected by thedesigned technology
Example: Useful Statistical Tools– Sampling Survey– Conjoint Analysis– Knowledge Discovery• Data Mining

Realized
SocietyEngineering
Model

TargetSociety• Residual Analysis• Exploratory Classification

Step 2: Translation
Value
Selection

Translation

Value
Injection

Optimization

Realized
Society

TargetSociety• Objectives– Translation of
the Selected VOC
into Functional Quality
Elements (Voice ofEngineers)• Methods– Clarifying systems to attainthe requirements from thesociety.

• Supporting Tools for Planning
Engineering Models– QFD– Cause and Effect Diagrams

Engineering
ModelStep 3: Optimization


Value
Selection

Translation

Value
Injection

Optimization

Objectives
Attainment of usability by
optimizing design parametersof the engineering models.
 Methods:

Designing the best performing
systems against variation of
uncontrollable factors.
Realized
SocietyEngineering
Model

TargetSociety

Tools

DOE

Robust Parameter Design

Step 4: Value Injection??
Value
Selection

Translation

Value
Injection

Optimization

Realized
SocietyEngineering
Model

TargetSocietyObjectives: Value Realization
To attain the consistency
between the realized
functional qualities and thecorresponding perceived
quality in the real society.
Methods???:Communication andinformation management tomake users notice thevalue of the designed
technology

Japanese Typical Contributions to Design Oriented Quality Improvement

Not Recent But Since 1950s• Value Selection and Value Injection– Methods for Concept Generation• New QC 7 Tools by Prof. Nayatani (1983)– Non-linear Quality by Prof. Kano (1984)• Translation– Concept Transformation to Design Parameters• QFD by Profs. Akao and Mizuno (1978)• Optimization– Optimization of Design Parameters to Obtain Robustness
against Noises• Taguchi Method since 1952?!
– Design of Experiment for Technology Assessment– Japanese Ways?!

ACTIVITIES BY STATISTICIANS

Graphical Modeling and SEM (Structural
Equation Modeling)• Exploratory Causal Analysis since 1996

– graphical modeling popularized by the JSQC technometrics researchgroup ( M. Miyakawa, T. Haga, K. Nishina, S. Yamada, M. Hirono et al.)• Haga and Hirono developed a software for conversational graphicalmodeling “CGGM and CLGM”• JSQC published “Practice of Graphical Modeling” in 1999 with several
cases from Japanese industry.

• Confirmatory Causal Analysis since 1995

– Japanese Industries have the largest number of users of SEM by
AMOS• SEM is commonly used not at optimization or translation stages for
quality improvement but at value selection stages as recognizing thecustomers behaviors in marketing divisions.

– In 2006 JUSE developed a new software in which CGGM by and EQS
are combined• Graphical Modeling among latent factors• Regression Modeling with measurement uncertainties

Case.1 Fusion of Physical laws and SEM
Lecture by Nonaka and Tsubaki (2004) in JUSE
• Theoretically suggested negative correlation between Br(magnetic flux density) and Hc (coercive force) and its
observed correlation

Regression analysis to improve
the Br and Hc• 13 explanatory variables– 2 material conditions– 3 burning conditions– 2 composition conditions– 6 forming conditions• The regression coefficients of Br becomes positive in
multiple regression of Hc to 13 variables and Br.

Exact linear relation between true Br and true HC
Br and Hc are mainly affected composition and forming conditions,
respectively.
⇒Association between true Br and true HC becomes
highly significantly negative
材料条件2
.15

.01

.00
.08

推定値
TrueBr <---

成型条件 6.16512

TrueBr <---

成型条件 5.00000

TrueBr <---

成型条件 4.00110

TrueBr <---

成型条件 3.00000

TrueBr <---

成型条件 2

TrueBr <--TrueBr <---

標準誤差

検定統計量

確率

.01547

10.67495***.000512.15008.03155.00-2723.41.00.54, .00

.00

材料条件1

焼成条件1

.00 .00
.04,

焼成条件2
.00

17.97 .00-.03
-1.83.00 .06 152.58, 4251.31

.00

.00
.00

焼成条件3

.02605.010682.43947.01471

成型条件 1.06221組成 2-.02769.00897

6.93502***.00339-8.17145***

TrueBr <---

組成 1-.00668.00092-7.24790***

TrueBr <---

焼成条件 3-.00002.00000-8.58302***

TrueBr <---

焼成条件 2.00000

TrueBr <---

焼成条件 1.00000

TrueBr <---

材料条件 2.00000

TrueBr <---

材料条件 1.00000

TrueHc <---

TrueBr-5809.81427

567.29868-10.24119***

TrueHc <---

材料条件 22911.97084

531.49189

5.47886***

TrueHc <---

焼成条件 2.00000

TrueHc <---

成型条件 49.74550

4.250632.29272.02186

TrueHc <---

成型条件 3-1.58260.36648-4.31835***

TrueHc <---

材料条件 1

19.12662

11.09727

1.72354.08479

TrueHc <---

成型条件 1.00000

TrueHc <---

成型条件 2.00000

TrueHc <---

成型条件 5-11.54235

3.72527-3.09839.00195

TrueHc <---

成型条件 6

1575.35114

158.83846

9.91795

***

Br

<---

TrueBr1.00000

Hc<---

TrueHc1.00000.00.00.00 1.58

30.63, .03
.00
組成1

0
.00
.00

1.05, .00

.00
-.01
.00
-.03

-5.50974***.00

4.30, .00

19.13

1

0

-.04
1.00

Br

.06
.03

成型条件1

0, .00eb

2911.97

TrueBr

組成2.000003.63,4206.30, 214906.15.00-5809.81.00.00

4.31, .00

成型条件2
-.01

.00
.00
.00

-1.58
.00
27.43, 13.43
9.75
.17

成型条件3

.48

-11.5426.28, .19
1575.35成型条件4812.72, .15

成型条件5
00

7.52, .00

成型条件6-4442.98
1.00

TrueHc0Hc

10, 1314.7

ehCase.2 Modified Conjoint Analysis
Application of DOE for Value Selection• Toya, Nishio and Tsubaki (2005) • Usual conjoint analysis using L8
or L9 cannot examine the effects• Consumer’s Sense of Value andof many important factors
Preference of Retail Financial• We assigned 7 factors to L27
Services– Experimental design with ranklogistic model fitting provide theevidence that customer preference
has linear and non-linear
relationship with their value.
– The findings are useful forpractical marketing strategy.
•

Customers those who want toenjoy their life prefer to haveflexibility of deposit cancellation.
• Customers those who have self•
respect prefer to virtual channel.

– one for assignments of different
conjoint survey sheets with 9
financial product profiles.
– Six controllable factors to design
financial services– Individual structures on utility are
not estimable so we measured
several covariates possibly
associating with “Financial
Values” of customers

Estimate the interaction
between the covariates and theassigned factors

An Illustrative result• The interaction between thelevel of the hedonic attitude
and the degrees of freedom fordeposit methods is significant .
– For hedonic customers, a multiway for their deposit is a linear
quality element.
– For less hedonic customers it is anatural quality element.

Preference
Level
High Hedonic
Attitude

0.5
0.4

生活を楽しむ性向＝１
生活を楽しむ性向＝２
生活を楽しむ性向＝３
生活を楽しむ性向＝４
生活を楽しむ性向＝５
生活を楽しむ性向＝６
生活を楽しむ性向＝７
生活を楽しむ性向＝８
生活を楽しむ性向＝９
生活を楽しむ性向＝10

0.3
預
入
方
法
へ
の
選
好

0.2
0.1
0
預入方法自由度小預入方法自由度中

預入方法自由度大

-0.1
-0.2
-0.3

Low Hedonic
AttitudeDegrees of Freedoms for Deposit Channels

INTERNATIONAL STANDARDIZATIONIN ISO TC 69/ SC 8

TC 69 Standardization for Traditional Statistical
Approaches to Quality Management a-priori given by
Functional Quality Characteristics• Output Control by Sampling Inspection (SC 5)• Process Control by Control Charts etc (SC4)• Process Improvement– Six-Sigma (SC 7), Kaizen, etc.
• Adjustment of the average of Quality Characteristics– Improve the dispersion of the Quality Characteristics etc.

Statistical and related methods
along the value chain cycle
Value

CustomersandSocieties

Selection

VOC

Quality

WG1 Collection of
VOC or VOS by
SurveyThe next process is
an importantcustomer

Value

SystemInjection

Selection
Good
Products

SC4 and SC5
activities

Parameters of
SystemsGood
Design

WG2: Selection ofengineering models or
systems to attain the
VOC or VOS

SystemSystem

Implementation

Optimization

WG3:
Systemdesign andoptimization

What SC 8 will not do.
• Strategic guidelines for non-statisticians– What SC 8 will expect SC 7 relating to “Six Sigma” etc.
• To publish process guidelines for engineers and managers
including both statistical product and production process control
or improvement (The Grammar of Statistical Quality Management)
and further the statistical technology development (The Grammarof Technology Development)• Standardization of methods to control or improve acurrent product or its production process

ISO TC 69 SC 8 ESTABLISHED IN 2009:“APPLICATION OF STATISTICAL AND
RELATED METHODOLOGY FOR NEWTECHNOLOGY AND PRODUCTDEVELOPMENT ”

Scope• The new SC will publish a series of technicalstandards on the application of statistical andrelated procedures for new technology andproduct including private or public services
developments to optimize their values fromthe viewpoints of customers and/or societies.

Three WGs
for SC 8 (chair: Tsubaki) Activities• Value Selection Process （ SC 8/WG 1 ） :– Selection of values from voices of customers orsocieties• By Mr. Marius Cronje from South Africa• Transformation Process (SC 8/WG 2)– Transformation of the values into appropriate
parameters defined in engineering systems• By Dr. Glenn Mazur from USA• Optimization Process (SC 8/WG 3):– Optimization of performance of the engineeringsystems• By Masayoshi Koike from Japan

Outlines of a Series of Standards• ISO 16355 “Application of statistical andrelated methods to New technology andProduct Development Process”,• General Scope:– This Standard describes statistical and related
procedures along QFD process for new technology
and product development including private or
public services developments to optimize theirvalues from the viewpoints of customers and/or
societies.

One standard with 8 parts• 1. General Principle and Perspective of QFD Process• 2. Acquisition of Non-quantitative VOC or VOS• 3. Acquisition of Quantitative VOC or VOS• 4. Analysis of Non-Quantitative and Quantitative
VOC/VOS• 5. Solution Strategy• 6. Optimization – Robust parameter design• 7. Optimization - Tolerance design and output tomanufacturing• 8. Guidelines for Commercialization

Part 1 General Principle andPerspective of QFD Process• Part 1 describes the overall QFD process, its
purpose, users, benefits, and lists of relevant
statistical and related tools and methods– General Flow of QFD Process– Statistical and Related Tools and Methods for
General Flow
• Informative Annex: Illustrative Example of the best
Practices

Part 2: Acquisition of Non-quantitative
VOC or VOS• Part 2 describes methods to acquire VOC and
VOS using non-quantitative methods in orderto develop questions for quantitative surveys– Non-quantitative VOC/VOS Process Flow
– Statistical and Related Tools

Part 3: Acquisition of Quantitative VOCor VOS• Part 3 describes methods to acquire VOC and VOS using quantitative methods– Objective of survey• Clarification of the objective• Clarification of targets• Assessment and accounting of costs and benefits– Quantitative VOC/VOS Process Flow
•
•
•
•
•

Definition of population
Survey design
Implementation of survey including data collection
Editing of results
Report of results– Sampling Methods– Stratification and Clustering– Evaluation of Accuracy of Results• Sampling error• Non- sampling error– Cost Effectiveness of Survey

Part 4: Analysis of Non-Quantitativeand Quantitative VOC/VOS• Part 4 describes methods to analyze nonquantitative VOC and VOS in order to understandhow customers measure satisfaction from
customer’s perspective– Non-quantitative VOC/VOS Analysis Process Flow
– Statistical and Related Tools for Non-quantitative
VOC/VOS Analysis– Quantitative VOC/VOS Analysis Process Flow
– Statistical and Related Methods for quantitative
VOC/VOS Analysis

Part 5: Solution Strategy• Part 5 describes statistical and related
methods to translate VOC/VOS priorities andtargets into product requirements such asfunctional requirements and target
specifications– Solution Strategy Process Flow
– Statistical and Related Tools

Part 6: Optimization – Robust
parameter design• ISO DIS 16336• Part 6 describes a guidance of applying
optimization method of parameter design, an
effective methodology for optimization basedon Taguchi Methods– Parameter design for robust products –overview– Evaluation of robustness by SN ratio– Procedure of parameter design• Informative Annex Example of application experimentPart 7: Optimization - Tolerance designand output to manufacturing• Part 7 gives a guidance of applying
optimization method of tolerance design– Design process and output to manufacturing –overview– Tolerance design – basic concepts– Procedure of tolerance design• Informative ANNEX; Example of application
experimentPart 8: Guidelines forCommercialization• Part 8 describes statistical and related
methods to assure quality and customer
satisfaction throughout the rest of thecommercialization and product life cycle– Commercialization Process Flow
– Statistical and Related Tools

Invitation to the Joint Research Activities of ROIS and JSA

VALUE CREATION PROCESSNETWORK41

Objective of VCP-NET• Our Environment– Varieties of Slogan and TIPS for Improvement, Innovation and Reconstruction• Explosion of Knowledge on Methods and Tools• Difficulties of Disclosure of Wisdom in Problem Solving Process in Japanese Industry• Inefficiency of Problem Finding for Value Creation• Proposal– Collection, Sharing and Autonomously Evolution of Wisdom on Effective
Application of Knowledge– Constructing Network of Driven Engineers, Researchers and Managers todevelop the Wisdom• Activities– Developing “Wisdom Base” by Voluntary activities as Wikipedia– Communication among academia and industries in the network– Standardized Collection of wisdom on the problem finding and solving
processes and their supporting methods and tools for value creation intechnology and product development

42Activity of WG1• Body of Knowledge– Standardized Description of Tools and their
Organic Linkages for Value Creation• Explicit Description of Tools from the viewpoints of
their application activities.
– Input Information and its requirements– Output Information and its requirements– simplest description of Function and Procedures– Possible Tools to use the outputs– Possible Tools to supply the inputs– Similar tools
43Activity of WG2Standardized Process for Value Creation• Standardized Description of Typical Problem
Finding or Solving Processes in Value Chains• Clarification of roles of methods and tools in theprocesses• DPAM as Its pioneering activity in JEITA– Published in 2007/05– http://home.jeita.or.jp/is/committee/tech-std/sekkeiprocess/dpam/– Classification of design processes into 25 element
processes with their concrete objectives to give their
explicit evaluation viewpoints
44

New WG
Collection of Cases in Value Creation• Collection and Recording of Cases on Good
Practices of Value Creation withStandardized Forms by WG 1 and WG 2
– Problem Finding– Problem Solving– Implementing the Solution• Survey Site to Attend VCP-Net– https://codia2.heteml.jp/survey/index.php?sid=74
185&lang=ja
45

Concluding Remarks• Japan has developed several original methodologies
for quality management activities by communications
between academia and industries since 1950.
• Also in present and future? .
– Design of Appropriate Communications in Academia• Between Sciences for Recognition and Design• Among Management Sciences as QM, Marketing, BSC, HRM etc.
• Among Trans-Disciplinary Sciences as Statistics, IT and other
Applied mathematics.

– Redesign of Communication between Industries
and Academia• The perspective view points as the grammar would
support these communications?!