Uk Jung

An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria

As countries strive to become more efficient in investing limited resources in various national R&D projects, the evaluation of project has become increasingly important. However, due to the heterogeneity of the objectives of national R&D programs, few studies have been conducted on comparing programs on the basis of performance. This study explores the application of the analytic network process (ANP) approach for the evaluation of R&D projects that are elements of programs with heterogeneous objectives. The ANP produced the final priorities of projects with respect to benefits and costs when there are interdependencies between programs and evaluation criteria. The paper provides value to practitioners by providing a generic model for project evaluation. For researchers, the paper demonstrates a novel application of ANP under specific situation of heterogeneous objectives. The ANP approach is tested against empirical data drawn from R&D projects sponsored by the Korean government.

Wavelet energy-based visualization and classification of high-dimensional signal for bearing fault detection

This study investigates a methodology for interpretable visualizing and classifying high-dimensional data such as vibration signals in machine fault detection application. Although principal component analysis is one of the most widely used dimension reduction methods, it does not clearly explain the source of signal variations (i.e., statistical characteristics such as mean and variance), but just locate signals on low-dimensional space which maximizing data dispersion. This deficiency restricts its interpretability to specific problems of process control and thus limits their broader usefulness. To overcome this deficiency, this study exploits the multiscale energy analysis of discrete wavelet transformation, so-called wavelet scalogram, in unsupervised manner. Wavelet scalogram allows us to first obtain a very low-dimensional feature subset of our data, which is strongly correlated with the characteristics of the data without considering the classification method used, although each of these features is uncorrelated with each other. In supervised learning scheme, it can be eventually combined with silhouette statistics for the purpose of more effective visualization of the main sources of different classes and classifying signals into different classes. Finally, nonparametric multi-class classifiers such as classification and regression tree and k-nearest neighbors quantitatively evaluate the performance of our approach for machine fault classification problem in terms of the 10-fold misclassification error rate.

Literature Review on the Quality Innovation in KSQM for 50 Years

Purpose: This paper reviews the papers on quality innovation issues which are published in the Journal of the Korean Society for Quality Management (KSQM) since 1965. The literature review is purposed to survey a variety of quality innovation issues Methods: We divide all of quality innovation issues into 2 categories: quality control circle, quality innovation and improvement Results: Dividing all of papers on quality innovation published in KSQM for 50 years into 2 categories, we provide a chronological roadmap for individual categories, and summarize the contents and contributions of surveyed papers Conclusion: The review paper is expected to provide future direction to improve quality innovation theory and practices in manufacturing and service industries

Regarding the Preliminary Feasibility Study of National R＆D Program : With Focus on the Applicability of Theory of Attractive Quality

Purpose: This paper discusses the intrinsic assumption of one-dimensional relationship between the upper and lower levels in AHP(Analytic Hierarchy Process) for the Preliminary Feasibility Study of National R&D Program. This assumptions has not been questioned in academia and industry so far. Methods: This discussion is induced by understanding the Theory of Attractive Quality (Kano et al. 1984) and explains the limitation of AHP in the preliminary feasibility study of national R&D program. Results: In this paper, we propose a new questioning method based on two dimensional perspective, which is named as 2D-AHP (two dimensional AHP), to overcome the limitation. The main idea stems from the observation that the relationship between the upper and lower levels in AHP can vary depending on the subject of R&D. Conclusion: The two dimensional perspective pointed out in this paper should be more deeply studied in the field of MCDM(multi-criteria decision making) since it can be applied to the more general problems in human decision making.

The impact of firm's intra-cooperation practice on NPD performance: with focus on the moderating effect of environmental uncertainty

ABSTRACT Purpose: This study aims to explore the relationship between the focal firm’s interdepartmental cooperation and new product development (NPD) performance with focus on the moderating effect of environmental uncertainty. The basic hypothesized model is that there are positively associated relationships. Methods: The proposed research model was tested using structural equation modeling with 601 responses from multi-functional and multiple respondents in Korean manufacturing firms. Multi-group SEM analyses were conducted to explore the degree to which the hypothesized model was equivalent for different levels of environmental uncertainty.Results: Interdepartmental cooperation between R&D and production is positively associated with NPD per-formance under both higher and lower environmental uncertainties, while one between R&D and marketing is positively associated under only higher environmental uncertainty.Conclusion: This paper determined that NPD performance is positively correlated with R&D-production coop-eration in a focal firm, and the relationship between R&D-marketing cooperation and NPD performance is positively moderated by level of environmental uncertainty. Consequently, this study suggests that it is always important for firms to put much effort on R&D-production cooperation for a better NPD performance, while R&D-marketing cooperation should be enhanced especially under higher environmental uncertainty than lower.

Robust Newsvendor Model With Random Yield and Customer Balking

Abstract Purpose: In this paper, we have considered a problem of newsvendor mod el in an environment of random yields in quality and customer balking behavior, in which only the mean and the variance of the demand are known. In practice, the distributional information of the d emand is very limited and only the mean and variance are guessed by experience. In addition, due to the cus tomers balking behavior occurring when the available inventory level decreases, the product’s demand becom es a function of inventory level so that the classical newsvendor’s optimal order quantity is no longer opti mal. Methods: We have developed an optimal order quantity model that enables us to incorporate the random yield of a product and the customer balking information such as a threshold inventory level of balking and the corresponding probability of a sale during the balking.Results: We illustrated the concepts developed here through simple num erical examples and showed the ro-bustness of our model in a various setting of parameters.Conclusion: This paper provides a useful analysis showing that our distri bution-specific and distribution-free approach to the optimal order quantity in the newsboy model can act as an effective tools to match supply with demand for these product lines.Key Words：Distribution-Free Approach, Newsvendor Problem, Balking, Rando m Yield

Sparse abnormality detection based on variable selection for spatially correlated multivariate process

Abstract Monitoring the manufacturing process becomes a challenging task with a huge number of variables in traditional multivariate statistical process control (MSPC) methods. However, the rich information is often loaded with some rare suspicious variables, which should be screened out and monitored. Even though some control charts based on variable selection algorithms were proven effective for dealing with such issues, charting algorithms for the sparse mean shift with some spatially correlated features are scarce. This article proposes an advanced MSPC chart based on fused penalty-based variable selection algorithm. First, a fused penalised likelihood is developed for selecting the suspicious variables. Then, a charting statistic is employed to detect potential shifts among the variables monitored. Simulation experiments demonstrate that the proposed scheme can detect abnormal observation efficiently and provide root causes reasonably. It is shown that the fused penalty can capture the spatial information and improve the robustness of a variables selection algorithm for spatially correlated process.

Nonparametric Wavelet-Based Multivariate Control Chart for Rotating Machinery Condition Monitoring

Signal processing of vibration signals from rotating machinery has been an active research area for recent years. Especially, discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. However, the number of retained DWT features can be still too large to be used for standard multivariate statistical process control (SPC) techniques although DWT significantly reduces the dimensionality of the data. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most of methods require a parametric distributional assumption that restricts their feasibility to specific problem of process control and thus limits their applications. This study introduced new feature extraction technique to alleviate the high dimensionality problem of implementing multivariate SPC when the quality characteristic is a vibration signal from bearing system. A set of multiscale wavelet scalogram features was generated to reduce the dimensionality of data, and is combined with the bootstrapping technique as nonparametric density estimation to set up an upper control limit of control chart. Our example and numerical simulation of a bearing system demonstrated that the proposed method has satisfactory fault-discriminating ability without any distributional assumption.

Lessons from the history of Samsung’s SCM innovations: focus on the TQM perspective

The purpose of this paper is to provide some lessons learned from the implementation of supply chain management (SCM) innovation in the Samsung Group, arguably the most successful globaliser in South Korea. Our case study allows direct observation of the field, which would be particularly suitable for approaching several managerial functions combined together towards successful SCM performance. There have been some distinguished factors of success such as innovation-oriented leadership and culture, customer-focused product innovation and globalisation, speed-focusing strategy, and closed-loop sales and operations planning and Six Sigma. The experiences in a single company may not fully cover those that could have been made in other successful firms. However, it is expected that Samsung’s success in SCM innovation caused several meaningful ramifications for those interested and the results of this work provide useful implications in this respect.

Identification of structural damage using wavelet-based data classification

Predicted time-history responses from a finite-element (FE) model provide a baseline map where damage locations are clustered and classified by extracted damage-sensitive wavelet coefficients such as vertical energy threshold (VET) positions having large silhouette statistics. Likewise, the measured data from damaged structure are also decomposed and rearranged according to the most dominant positions of wavelet coefficients. Having projected the coefficients to the baseline map, the true localization of damage can be identified by investigating the level of closeness between the measurement and predictions. The statistical confidence of baseline map improves as the number of prediction cases increases. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating structural damage even in the presence of a considerable amount of process and measurement noise.