• Architectural research
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• v.17 no.2
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• pp.75-81
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• 2015

Insulation and condensation performance evaluation (I&CPE) is an important energy analysis. While considerable amount of data are already represented in a Building Information Model (BIM), the lack of interoperability between BIM modeling and I&CPE programs prevents the simulation process from being efficient and accurate. This study proposes a prototype of a BIM-based I&CPE simulation program, in order to enhance the interoperability between BIM modeling and I&CPE programs. This study discusses the information flow process, defines the required information and its level of detail, develops standardized libraries, and finally proposes a prototype that consists of a data extraction module, conversion module, and performance module. The assurance of interoperability between systems might greatly benefit architects and energy professionals.

• International Journal of Quality Innovation
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• v.8 no.3
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• pp.156-172
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• 2007

The high-tech industry is the economic lifeline for Taiwan. Its characteristics are short product life cycle, rapid changes in the market, and a high obsolescence rate for new products. Under globalization, the high-tech industry has adopted Information Technology (IT) to shorten the manufacturing process, reduce costs and conduct product research and development (R&D) to increase the core competence of enterprises and achieve the goal of sustainable operations. Enterprises should actively strengthen their integration with internal and external resources and lead in R&D management to increase industrial operating performance. Effectively managing operations and R&D management evaluation in Taiwan's High-tech Industry has become a critical subject. This study adopted 4 major Balanced Scorecard (BSC) perspectives to establish the Total Performance Evaluation Indicators for the R&D management department in Taiwan's High-tech Industry. The Analytic Network Process (ANP) was applied to evaluate the overall performance of the R&D management department. The research framework is divided into 2 phases. The first phase is combined with the 4 major perspectives, Financial, Customer, Internal Business Process and Learning and Growth, as the related indicators for each measurement perspective. The Key Performance Indicators (KPI) were selected using Factor Analysis to identify the key factor from the complicated indicators. The relationship between the characteristics of each BSC's evaluation perspective is dependence and feedback. This study applied ANP to conduct the calculation and adjustment of correlation between each KPI, and determine on their relative weights for the objective KPI. The "Financial Perspective" for R&D management department in Taiwan's High-tech Industry focused on the budget achievement rate of R&D management. The weight indicator value is (0.05863). The "Customer Perspective" focused on problem-solving satisfaction. The weight value of this indicator is (0.17549). The "Internal Business Process Perspective" focused on the quantity and quality of R&D. The weight value of this indicator is (0.13506). The "Learning and Growth Perspective" focused on improving competence in the research personnel's professional techniques. The weight value of this indicator is (0.02789). From the total weighting indicators, the order of the Performance Indicators for the R&D management department in Taiwan's High-tech Industry is: (1) Customer Perspective; (2) Internal Business Process Perspective; (3) Financial Perspective; and (4) Learning and Growth Perspective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.163-170
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• 1996

Several formerly independent trends including the increasing rate of technological change, the demand for greater efficiency and productivity in R&D and innovation. The purpose of this study is describe performance evaluation of research and development. Performance evaluation level of R&D have three levels: individual level, research group level, laboratory level. There are development evaluation factors and evaluation routin in each levels.

• Journal of the Korean Institute of Gas
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• v.21 no.6
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• pp.81-87
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• 2017

SOFC(Solid Oxide Fuel Cell) is one of the high-temperature fuel cells, SOFC system and Stack has a high temperature operating Characteristics. These Characteristics cause a lot of trouble in the design of the system and selection materials, securing durability, and securing safety performance. Therefore, For Commercialization and Supply of SOFC, the development of safety performance assessment techniques and evaluation systems should go together. In this study, we analyzed the evaluation items of SOFC Safety performance standards and risk factors of the stack for design of stack safety performance evaluation system. Based on the analyzed data, the evaluation item was deducted for design of stack safety performance evaluation system. Through the results of this study, we expect to facilitate the supply of SOFC and contribute to a safe use environment.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.247-265
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• 1997

The objectives of not-for-profit organizations including government supported research institutes are, by definition, to achieve socially desired nonfinancial goals. Current reporting focuses on providing information to meet the needs of users. This information may be provided in the annual research report, financial report, and other reports. An objective of current reporting is to provide users with information that will assist them in evaluating the performance(efficiency and effectiveness) of reporting entity. The evaluation of R&D project's performance requires information not only about the acquition and use of resources, but also about the outputs and outcomes of R&D activities, This study aims to recommend that R&D Service Efforts and Accomplish- mints(SEA) reporting is useful for performance evaluation in national R&D programmes. To achieve this aims, I attempt to develop the R&D SEA measures with reference to the Concepts Statement No. 2 of the Governmental Accounting Standards Board. R&D SEA measures consist of five categories : (1) input measures, (2) outcome measures, (3) output measures, (4) re1a1e efforts to accomplishments, (5) explanatory information.

• JSTS:Journal of Semiconductor Technology and Science
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• v.5 no.4
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• pp.229-236
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• 2005

This paper presents a system level architecture evaluation technique that leverages transaction level modeling but also significantly extends it to the realm of system level performance evaluation. A major issue lies with the modeling effort. To reduce the modeling effort the proposed technique develops the concept of worst case scenarios. Since the memory controller is often found to be an important component that critically affects the system performance and thus needs optimization, the paper further addresses how to evaluate and optimize the memory controllers, focusing on the test environment and the methodology. The paper also presents an industrial case study using a real state-of-the-art design. In the case study, it is reported that the proposed technique has helped successfully find the performance bottleneck and provide appropriate feedback on time.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.1
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• pp.57-65
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• 1975

A perfect evaluation for scientists and engineers is impossible unless the R&D process is fully explored. But the study in this area is being intencified, and consequently progress is being made. In this paper an evaluation method for scientists and engineers in project-oriented R&D organizations is developed. That is, a method that the evaluation for the job performance directly influences personnel management such as promotion, and the evaluation for their potential, characteristics influences indirectly. Further systematization of evaluation process, which is no less importatant than evaluation itself, is attempted.

• Korean Management Science Review
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• v.34 no.2
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• pp.67-83
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• 2017

This paper proposes a R&D project performance measurement model for private firms combining balanced scorecard (BSC) and data envelopment analysis (DEA). The efficiency of R&D projects is measured in terms of each of the three perspectives of BSC by using DEA : the internal process perspective (DEA-P), the customer perspective (DEA-C), and the financial perspective (DEA-F). The performance indicators of the three perspectives of BSC are considered as outputs of the corresponding DEA models. To provide strategic implications for R&D planning, we also propose the R&D project performance matrices composed of two different types of efficiency dimensions. The proposed model is expected to be fruitfully utilized for R&D performance measurement of private firms.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.101-121
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• 2009

The research literature presents that Total Quality Management (TQM) has been successfully embraced by firms for strategic and operational management in the last decades. However few studios analyze the role of TQM practices in the R&D environment. The objective of this study is to evaluate TQM practices implemented in manufacturing-based R&D organizations with respect to R&D performance. Our research model is primarily based on Malcolm Baldrige Criteria for Performance Excellence framework, including three moderating variables (industry type, organization size, quality management system certification), to propose hypotheses on the relationships between leadership practice, management practice and R&D performance. Results of canonical correlation analysis using data from 130 R&D organizations show that in aggregate leadership practice is positively related to management practice which is, in turn, positively related to R&D performance. These relationships are, however, affected by the moderating variables. Our results imply that TQM practices can be strategically adopted in the R&D organization.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.