ICSA Book Series in Statistics
Series Editors: Jiahua Chen · Ding-Geng (Din) Chen

Dongseok Choi · Daeheung Jang
Tze Leung Lai · Youngjo Lee · Ying Lu
Jun Ni · Peter Qian · Peihua Qiu
George Tiao Editors

Proceedings of the
Pacific Rim Statistical
Conference for
Production Engineering
Big Data, Production Engineering andStatisticsICSA Book Series in Statistics
Series editors
Jiahua Chen, Department of Statistics, University of British Columbia, Vancouver,
CanadaDing-Geng (Din) Chen, School of Social Work and Department of Biostatistics,
University of North Carolina, Chapel Hill, NC, USA

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The ICSA Book Series in Statistics showcases research from the International
Chinese Statistical Association that has an international reach. It publishes books instatistical theory, applications, and statistical education. All books are associatedwith the ICSA or are authored by invited contributors. Books may be monographs,edited volumes, textbooks and proceedings.

More information about this series at http://www.springer.com/series/13402

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Dongseok Choi Daeheung Jang
Tze Leung Lai Youngjo Lee
Ying Lu Jun Ni Peter Qian
Peihua Qiu George Tiao
•

•

•

•

•

Editors

Proceedings of the Paciﬁc
Rim Statistical Conference
for Production Engineering
Big Data, Production Engineeringand Statistics

123
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Editors
Dongseok Choi
Oregon Health and Science University
Portland, OR
USA

Jun NiUniversity of Michigan–Ann ArborAnn Arbor, MI
USA

Daeheung Jang
Pukyong National University
Busan
Korea (Republic of)Peter Qian
University of Wisconsin–Madison
Madison, WI
USA

Tze Leung Lai
Stanford University
Stanford, CA
USAPeihua Qiu
University of Florida
Gainesville, FL
USA

Youngjo Lee
Seoul National University
Gwanak-gu, Seoul
Korea (Republic of)George Tiao
University of Chicago
Chicago, IL
USA

Ying Lu
Stanford University
Stanford, CA
USA

ISSN 2199-0980
ISSN 2199-0999 (electronic)
ICSA Book Series in Statistics
ISBN 978-981-10-8167-5
ISBN 978-981-10-8168-2 (eBook)
https://doi.org/10.1007/978-981-10-8168-2
Library of Congress Control Number: 2017964576© Springer Nature Singapore Pte Ltd. 2018
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The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional afﬁliations.
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Singaporetm551017@yahoo.co.jp

Preface

The Paciﬁc Rim area is one of the key manufacturing sites in the world, andapplications of statistical thinking and methods for production engineering havenever been more important with big data. To address the need, a statisticalconference for production engineering was ﬁrst proposed by Prof. George Tiao,
The University of Chicago, during his opening remarks at 2014 Joint Applied
Statistics Symposium of the International Chinese Statistical Association and
Korean International Statistical Society in Portland. The ﬁrst conference was held at
Shanghai Center for Mathematical Sciences located in Fudan University inDecember 2014. The main goal was to bring researchers and practitioners instatistics and engineering from academe and industry to promote collaborations andexchange the latest advancements in methodology and real-world challenges among
participants. Following the success of the ﬁrst conference, the 2nd Paciﬁc Rim
Statistical Conference for Production Engineering was held at Seoul National
University in December 2016. These proceedings present the selected papers basedon the presentations at the ﬁrst and second Paciﬁc Rim Statistical Conferences for
Production Engineering. We hope that this effort can stimulate further collaborations between academe and industry in production engineering.
The conference series has become a major joint event of the International
Chinese Statistical Association and Korean International Statistical Society. We
welcome those who are interested in this endeavor to join the third conference thatwill be held at National Tsing Hua University in Taiwan in 2018.
Portland, USA
Busan, Korea (Republic of)
Stanford, USA
Seoul, Korea (Republic of)
Stanford, USA
Ann Arbor, USA
Madison, USA
Gainesville, USA
Chicago, USA

Dongseok Choi
Daeheung Jang
Tze Leung Lai
Youngjo Lee
Ying Lu
Jun NiPeter Qian
Peihua Qiu
George Tiaov

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Contents

Part I
1

2

Bottom-Up Estimation and Top-Down Prediction: Solar Energy
Prediction Combining Information from Multiple Sources . . . . . . .
Youngdeok Hwang, Siyuan Lu and Jae-Kwang Kim
The 62% Problems of SN Ratio and New Conference Matrix for
Optimization: To Reduce Experiment Numbers and to Increase
Reliability for Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teruo MoriPart II
3

4

6

3

15

Analytic Methods of Big Data

Possible Clinical Use of Big Data: Personal Brain
Connectomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dong Soo Lee
The Real-Time Tracking and Alarming the Early Neurological
Deterioration Using Continuous Blood Pressure Monitoring in
Patient with Acute Ischemic Stroke . . . . . . . . . . . . . . . . . . . . . . . . .
Youngjo Lee, Maengseok Noh and Il Do HaPart III
5

Design and Collection of Big Data

23

33

Operation/Production Decision Making

Condition Monitoring and Operational Decision-Making inModern Semiconductor Manufacturing Systems . . . . . . . . . . . . . . .
Dragan Djurdjanovic

41

Multistage Manufacturing Processes: Innovations in Statistical
Modeling and Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hsiang-Ling Hsu, Ching-Kang Ing, Tze Leung Lai and Shu-Hui Yu

67

vii

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viiiContents

Part IV
7

8

Recent Research in Dynamic Screening System for Sequential
Process Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Peihua Qiu and Lu You
Degradation Analysis with Measurement Errors . . . . . . . . . . . . . . .
Chien-Yu Peng and Hsueh-Fang Ai

Part V
9

Reliability and Health Management
85
95

Recent Advances in Statistical Methods

A Least Squares Method for Detecting Multiple Change Points ina Univariate Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Kyu S. Hahn, Won Son, Hyungwon Choi and Johan Lim

10 Detecting the Change of Variance by Using Conditional
Distribution with Diverse Copula Functions . . . . . . . . . . . . . . . . . . 145
Jong-Min Kim, Jaiwook Baik and Mitch Reller
11 Clustering Methods for Spherical Data: An Overview
and a New Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Sungsu Kim and Ashis SenGupta
12 A Semiparametric Inverse Gaussian Model and Inference
for Survival Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Sangbum Choi

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Chapter 2

The 62% Problems of SN Ratio and New
Conference Matrix for Optimization: To
Reduce Experiment Numbers and to Increase
Reliability for Optimization
Teruo Mori

Abstract Robust design has been widely adopted during product design to reduce
variation and improve quality. However, based on our survey of 171 published casestudies using the L18 orthogonal array in Japan, 62% of the signal-to-noise ratios (SN)of the optimal design cases concluded from the main effects plots were worse thanthe best combinations of the existing 18 runs of the L18 orthogonal array. This meansthat current robust design based on SN ratios and the L18 cannot predict the optimal
conditions accurately and needs further work to improve the analytical prediction
accuracy and optimization efficiency. We will show the six causes of 62% problems.
Now, we have understood to face the serious problems like global warming, foodamounts for increasing population. We need faster and more precise methodology for
researching them, and it will be able to reduce experiment numbers and to increase
reliability using conference matrix.

2.1 Introduction
The job range of engineers’ assignments is wide and includes the basic research on
invention and new product development through the improvement of current products
and improvement of production processes, etc. They need to meet development goals
and to find optimal conditions to reduce product and process performance variation
at the same time (Mori 2011, 2009, 1992).
It is too late to conduct the troubleshooting activities to change product designor production conditions to resolve product defect issues after those products are
T. Mori (B)The Mori Consulting Office, 871-3 Daitocho, Fujieda 426-0044, Japane-mail: tm551017@yahoo.co.jp© Springer Nature Singapore Pte Ltd. 2018
D. Choi et al. (eds.), Proceedings of the Pacific Rim Statistical Conference
for Production Engineering, ICSA Book Series in Statistics,
https://doi.org/10.1007/978-981-10-8168-2_2

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15

16

T. Morimanufactured and shipped to the market and to customers. It is common to use
the recall and warranty activities to resolve quality problem issues. Also, it will behappened at a loss to customers. Of course, company guarantees the product andservice quality as top priority and is willing to take action to reduce customers’ loss
due to defective products (Mori 2014).
In this paper, we will review first the problems of the current robust design. Then,we will show to expect the new conference matrix for optimization methods.

2.2 Verification Assessment to Confirm the Optimal
Condition to Exceed the Best of L18 Trials
After finding the optimal candidate condition, engineers will conduct experiments toconfirm and verify that the results of the optimal candidate condition are reproducible.
Table 2.1 shows the SN ratio results (Mori 2013) (a) of the optimal conditions for 171
case studies to compare the best SN ratio values (b) of the L18 trials. One hundredand six (62%) cases were a<b. Theoretically, the SN ratio results of (a) are as goodas or better than (b), because the optimal condition candidates (a) are chosen frommany more possible combinations of factor levels than (b).
Unfortunately, 62% of the optimal conditions of (a) are worse than the best valuesof (b) as illustrated in Table 2.1 (Japan Quality Engineering association 2003–2012).
Engineers who have been trained in statistical modeling of Taguchi may be surprised at the “prediction uncertainty of the optimal design candidates” shown in
Table 2.1. Then, they will be requested the more advanced mathematical analysis for
improving the prediction accuracy and reduce the uncertainty of the optimal design
solutions.

2.3 Investigating the Root Causes for 62% Problems
Assume that the mean of the output response is µ; and that the main effects of four
selected experimental factors (A, B, C, and D) are a, b, c, and d.
The interaction terms are expressed using a multiplication term such as ab,
ac,…abc…, and abcd for the four factors. Let the summation of experimental error
and measurement error be (e). The experimental output response (y) is expressedwith the mean value µ, main effects, a, b, c, and d, and the quadratic, interaction andhigher terms as shown here:
Experimental output response (Jeff Wu and Hamada 2009)y = µ + a + b + c + d + aa + ab + ac + · · · + cd + abc + · · · + bcd +abcd + (e)= Mean value + main effects + quadratic + interaction effects + higher terms+ errortm551017@yahoo.co.jp

2 The 62% Problems of SN Ratio and New Conference Matrix for Optimization …
Table 2.1 Optimal condition comparison analysis
QES
Total # of case studies
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
Total
(%)
QES→

17

# of case studies where (a<b)

6
3
9
6
15
8
7
6
20
9
14
9
33
24
23
11
22
18
22
12
171
106
62.0
Japan quality engineering symposium

The optimal conditions (a) of Table 2.1 are selected based on the level averages
which were calculated to divide the sum of response y with data numbers. The
response graphs are made with the level averages.
On the other hand, orthogonal array tables like L8 , L9 , L18 as the design matrix
have the linear effect structure, so that it will be expected that the response should
consist of linearity components. If the response has nonlinear effect, the responsegraph will be shifted from the original by contaminating nonlinear effect. Nonlinear
effect will consist of quadratic, and interaction between factors and higher otherterms. We can estimate the nonlinear effects in the response with the empty columnof the orthogonal array table. So, we tried to look for the nonlinear effect from start
to finish of the robust process.

2.4 Causes Analysis for 62% Problems
We have done to analyze the causes of 62% problems related to nonlinear effects. We
finally detected six nonlinear effects at robust process on Fig. 2.1. It has the marked
1 
6 on nonlinear in Fig. 2.1.
–
1 
6 at Fig. 2.1 to explain the complex mathematics backWe will introduce –ground to get contaminated with nonlinear effects.

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18

T. Mori䐟Combine the best
䕿 levels

䐡
䐢

䐠

10.0

5.0

䐣

0.0
A1A2A3 B1B2B3 C1C2C3 D1D2D3

䐤Fig. 2.1 Six nonlinear effects on robust design process

2.5 Multiple Contamination of Six Type of Nonlinear Effect
Six types of nonlinear effects were separately investigated as the cause of 62%
problem. Actual optimum cases will be contaminated single or multiple of them. We
cannot detect the real causes individually if columns were filled with factors.
However if there were empty columns in orthogonal array tables, we can make adiagnose the degree of contamination the with the empty column factor effects.
We selected the published typical three case studies with empty columns for SN
ratio. We showed Figs. 2.2(BGA), 2.3(circuit), 2.4(straw) (Tanabe 2016).

Fig. 2.2 BGA semiconductor structure

Fig. 2.3 Electro circuit casetm551017@yahoo.co.jp

2 The 62% Problems of SN Ratio and New Conference Matrix for Optimization …

19

Fig. 2.4 Education training case

At Fig. 2.2 case, the empty column 8 is the largest effect. At Fig. 2.3, this casehas six factors, the empty column 8 is fourth largest factor, and it is at middle classposition. At Fig. 2.4, this case 3 factor and empty columns are 5 factors. The largest
empty column 4 is in second largest position. The empty columns effects might
be lower than the layouted columns. Those empty columns are gotten the multiple
contaminations of six nonlinear effects. We cannot ignore such six nonlinear effects
for contamination to other columns. The current robust design process will not bereasonable for engineering subjects. At least, SN ratio and L18 should be replaced
the different way to get more reliability of optimization.

2.6 The New Design Matrix for the Next Generation
We started to create the new process to avoid six types of nonlinear effects for robust
design. L18 has been long time recommended as standard tool at current robust
design. Taguchi (1984) explored statistically the optimum condition like black box.
The current robust design was not supported that reason is to demand too much
experiment number like 108 at dynamic style. If statistician likes to recommend
directly catching the optimal solution itself, the trial number might be naturally
increased to avoid missing it. If a fisherman tries to catch a fish, he will select the
net as much as largest size with the finest mesh to fear failures.
The scientist now is shifting the investing philosophy to confirm the tendency offactor effect to target in mix conditions with design matrix to make the less number
trials and higher reliability. The best deign matrix might be “conference matrix.”
Conference matrix C4 (21 33 ) is the minimum number for three-level matrix with
Table 2.2 C4 (21 33 ) andlinear term of L9 (34 )tm551017@yahoo.co.jp

20

T. Morilinear term. We compare linear term with L9 (34 ) in Table 2.2. (*) is the sum of
product of columns to confirm the orthogonality (Tanaka 2016).
The new process may not use SN ratio with log conversion to avoid six nonlinear
effects, and we are testing to adapt the raw data themselves. It will be complete in2017.

2.7 Conclusion
In this paper, author introduced 62% problems to the current robust design with L18
and SN ratio.
Based on author survey, 171 published case studies using the L18 orthogonal array(OA) in Japan, and 62% of the signal-to-noise ratios (SN) of the optimal design cases
concluded from the main effects plots were worse than the best combinations of the
18 runs of the L18 orthogonal array.
Also, author detected six types of nonlinear effects.
1: Interaction among factors: 2 : Log conversion for response
3: Reciprocal structure of SN ratio: 4 : Diversion (S2 ) size of SN ratio5: Confounding type L18 (21 37 ): 6 : Geometric level average after log conversion.
Also, author is touching the conference matrix and new concept for new typerobust design.

References
Japan Quality Engineering association, Proceeding (2003–2012).
Jeff Wu, C. F., & Hamada, M. S. (2009). Experiments: Planning, analysis, and optimization., Wiley
series in probability and statistics New York: Wiley.
Mori, T. (1992). Methods for new product and new technology development, trend book.
Mori, T. (2009). Taguchi methods- pocket guide book, trend book.
Mori, T. (2011). Taguchi methods: ASME.
Mori, T. (2012). QES2012, the 20th Annual Proceeding, Paper No. 63 (Quality Engineering Symposium).
Mori, T. (2013). QES2013, the 21th Annual Proceeding, Paper No. 23 (Quality Engineering Symposium).
Mori, T. (2014). Technical report, Toyota Bousyoku, vol. 08, pp. 8–19.
Mori, T. (2015). Mathematic and application of near orthogonal array, Mori Office, Chap. 23.
Taguchi, G. (1984). Parameter design for new product development. Japan: Japan Standards Association.
Tanabe, S. (2016). QES2016 The 24 Proceeding (Quality Engineering Symposium) No. 87.
Tanaka, K. (2016). Quality (JSQC) 46(1), 51–54.

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