Browsing by Author "Trappey, Amy J. C."

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A DEA benchmarking methodology for new product development process optimization
(2007)
Trappey, Amy J. C.
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Chiang, Tzu-An
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Chen, Wen-Chih
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Kuo, Jen-Yau
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Yu, Chia-Wei
Developing new products on time within budget constraints is a crucial issue to survive in today\'s competitive marketplace. However, unpredictable incidents occur during new product development (NPD) processes, which often cause expenses, resources and schedule overruns. Traditional project management tools lack of efficient and effective methods to solve these problems and challenges. Hence, this study applies the data envelopment analysis (DEA) concept to develop a novel project planning and management decision support methodology for NPD that can optimally allocate resources and dynamically response to unexpected delays and budget overruns. The research adopts the methodology to a mobile phone NPD project case to demonstrate the method\'s real-world application and illustrate the effectiveness of the proposed methodology in-depth.
A DEA benchmarking methodology for strategic management of new product development based on decentralized profit-center business model
(2007-01-01)
Trappey, Amy J. C.
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Chiang, Tzu-An
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Wu, Hsin-Ying
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Hou, Eric J. -R.
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管理科學系
In today's highly competitive marketplace, technology-driven large organizations widely adopt decentralized profit-center business model. In order to complete a series of NPD activities on time and in budget, the profit-center managers need an objective benchmarking approaches to gain precious perception into the relation of different resource allocations, profits, costs and times for each NPD activity. Thus, this study put forward a novel benchmarking methodology to optimize the NPD activities within a profit center for achieving the maximum profit goal and to guide NPD engineers to learn the best benchmarking practices. By applying the real case of the electric scooter design project, this research demonstrates the methodology at work with superior results, comparing to other existing approaches.
A Fuzzy Ontological Knowledge Document Clustering Methodology
(2009-06-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Hsu, Fu-Chiang
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Hsiao, David W.
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管理科學系
This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed. and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.
A hierarchical cost learning model for developing wind energy infrastructures
(2013-12-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Liu, Penny H. Y.
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Lin, Lee-Cheng
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Ou, Jerry J. R.
Renewable energy has been increasingly promoted and used to substitute non-renewable fossil-fuels, which cause negative effects on the environment. The Taiwan Statute for Renewable Energy Development has regulated and promoted renewable energy since 2009. A feed-in tariff (FIT) for renewable energy is one of the incentives that the government uses to promote the installation of green power generation facilities. The price of the electricity feed-in tariff is based on the current and future costs of renewable energy generation. When analyzing cost trends for renewable energy installation, many researchers use a single factor cost learning curve model. However, past studies indicate that there are multiple factors affecting the overall cost of installing renewable energy. Hence, this research develops a hierarchical installation cost learning model which considers multiple factors to accurately model and forecast wind energy development. This research uses wind power development data from Taiwan as a case study. We identify the cost factors, evaluate the learning effects, and compare the hierarchical learning curve model to the basic (non-hierarchical) learning curve model. The research results show an improved fit between the hierarchical model and the actual data when compared to the basic learning model. The study also provides new insights between the wind power learning progression of Taiwan and three countries in Europe. (C) 2013 Elsevier B.V. All rights reserved.
A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis
(2014-04-01)
Trappey, Charles V.
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Trappey, Amy J. C.
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Peng, Hsin-Yi
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Lin, Li-Deh
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Wang, Tong-Mei
The medical equipment industry has been one of the fastest growing sectors of the decade with predicted global sales reaching US$ 430 billion in 2017 [22]. During the period from 1995 to 2008, the patent applications in medical technology increased rapidly worldwide (World Intellectual Property Organization, 2012). Patent analysis, although useful in forecasting technology development trends, has posed a challenging analysis task since the volume and diversity of new patent applications has surpassed the ability of regular firms and research teams to process and identify relevant information. Further, medical related technologies rely on clinical trials to validate and gain regulatory approval for patient treatment even though patents, protecting the intellectual property rights of inventors, have been granted. This research focuses on developing a knowledge centric methodology and system to analyze and assess viable medical technology innovations and trends considering both patents and clinical reports. Specifically, the design innovations of dental implant connections are used as a case study. A novel and generic methodology combining ontology based patent analysis and clinical meta-analysis is developed to analyze and identify the most effective patented techniques in the dental implant field. The research establishes and verifies a computer supported analytical approach and system for the strategic prediction of medical technology development trends. (C) 2014 Elsevier Ltd. All rights reserved.
A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
(2019-04-01)
Trappey, Amy J. C.
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Chen, Paul P. J.
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Trappey, Charles, V
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Ma, Lin
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管理科學系
Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.
A multi-agent collaborative maintenance platform applying game theory negotiation strategies
(2013-06-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Ni, Wei-Chun
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管理科學系
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Department of Management Science
Engineering asset management (EAM) is a broad discipline with distributed functions and services. When engineering assets are capital intensive, management requires specialized expertise for diagnosis, prognosis, maintenance and repairs. The current practice of EAM relies on self maintained experiential rules with coordinated collaboration and outsourcing for maintenance and repairs. In order to enhance the life long asset value and efficiency (from the stakeholder's viewpoint) and after sales service quality (from the asset provider's viewpoint), this research proposes a collaborative maintenance platform that integrates real time data collection with diagnostic and prognostic expertise. The collaborative system combines and delivers services among asset operation sites (the maintenance demanders), the service center (the intermediary coordinator), the system providers, the first tier maintenance collaborators, and the second and lower tier parts suppliers. Multi-agent system technology is used to integrate different systems and databases. Agents with autonomy and authority work to assist service providers and coordinate communications, negotiations, and maintenance decision support. Finally, game theory is used to design the decision models for strategic, tactical, and operational decision making during collaborative maintenance practices.
A one-stop logistic services framework supporting global supply chain collaboration
(2016-06)
Trappey, Amy J. C.
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Trappey, Charles V.
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Chang, Sandy W. C.
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Lee, W. T.
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Hsu, Tun Nien
A global enterprise must continuously improve the efficiency of logistic operations between supply chain collaborators. Integrating logistic services, resources, and necessary information flows in the supply chain to ensure efficiency and efficacy is critically important to these companies. Global logistic service companies face challenges from their clients to provide logistic services that are cost effective, accurate, and seamlessly integrate material, information and cash flows. In this research, an improved framework for one-stop logistic services is systematically designed, analyzed, and evaluated. The one-stop logistic service framework, defined in four models, is developed to provide enterprises with integrated and comprehensive services within the global supply chain context. The levels of service importance are assessed using a four categories questionnaire. This research provides a case study of the implementation of one-stop logistic services in the distribution industry and demonstrates the framework operating under different demand conditions. Finally, system dynamics causal evaluation is used to evaluate the advantages of the logistic service framework.
A patent quality analysis for innovative technology and product development
(2012-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Chun-Yi
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Lin, Chi-Wei
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管理科學系
Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products. (C) 2011 Elsevier Ltd. All rights reserved.
A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0
(2017-08-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Govindarajan, Usharani Hareesh
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Chuang, Allen C.
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Sun, John J.
This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Gtiobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study. (C) 2016 Elsevier Ltd. All rights reserved.
A Review of Technology Standards and Patent Portfolios for Enabling Cyber-Physical Systems in Advanced Manufacturing
(2016-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Govindarajan, Usharani Hareesh
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Sun, John J.
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Chuang, Allen C.
Cyber-physical systems (CPS) are a collection of transformative technologies for managing interconnected physical and computational capabilities. Recent developments in technology are increasing the availability and affordability of sensors, data acquisition systems, and computer networks. The competitive nature of industry requires manufacturers to implement new methodologies. CPS is a broad area of engineering which supports applications across industries, such as manufacturing, healthcare, electric power grids, agriculture, and transportation. In particular, CPS is the core technology enabling the transition from Industry 3.0 to Industry 4.0 (I 4.0) and is transforming global advanced manufacturing. This paper provides a consolidated review of the latest CPS literature, a complete review of international standards, and a complete analysis of patent portfolios related to the 5C's CPS architecture model by Lee et al. The critical evaluation of international standards and the intellectual property contained in CPS patents is unaddressed by the previous research and will benefit both academic scholars and industry practitioners. The analysis provides a basis for predicting research and development future trends and helps policy makers manage technology changes that will result from CPS in I 4.0. This paper covers the emerging I 4.0 standards from the International Organization for Standardization, the International Electrotechnical Commission, and China's Guobiao standards followed by a patent analysis covering global patents issued in the U.S., Europe, China, and the World Intellectual Property Organization.
A Semantic Based Approach for Automatic Patent Document Summarization
(2008)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Chun-Yi
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管理科學系
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Department of Management Science
With the rapid increase of patent documents, patient analysis becomes an important issue for creating innovative products and reducing time-to-market. A critical task for R&D groups is the analysis of existing patents and the synthesis of existing product knowledge. In order to increase knowledge visibility and sharing among R&D groups, a conconcurrent engineering approach is used to facilitate patent document summarization and sharing. The purpose of this research is to automate patent document summarization as a key analysis and knowledge management. The goal of this research is to develop a semantic-based methodology for capturing patents and to summarize this content for design team collaboration. The system automatically marks, annotates. and highlights the nodes of an ontology tree which correspond to words and provides a Visual figure summary. Patents for power hand tools and chemical mechanical polishing tools were downloaded to evaluate the automatic summarization system. For these cases, the accuracy of classification reached 93% and 92% respectively demonstrating 20% summarization improvements over previous methods.
A strategic product portfolio management methodology considering R&D resource constraints for engineering-to-order industries
(2009)
Trappey, Charles V.
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Trappey, Amy J. C.
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Chiang, Tzu-An
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Kuo, Jen-Yau
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管理科學系
New product development Projects Usually incur high cost and high risk for corporations. For engineering-to-order industries, where each order requires a certain degree of new product design and customisation, R&D resources must be properly utilised for the highest possible profits. Thus, Engineering-To-Order firms often search for optimal product portfolios that effectively allocate limited enterprise resources to projects with the greatest potential for success. This paper develops a Product Portfolio Management (PPM) methodology that systematically analyses product portfolio strategies at the beginning of the product life cycle. Multiattribute decision analysis estimates the performance values used to construct plausible product portfolios during the early Engineering-To-Order design stage. An activity group concept evaluates resource requirements and the potential risks of product portfolios. The revenue of a product portfolio considers the levels Of Substitution among products. Finally, an industrial computer manufacturer, that is, a typical Engineering-To-Order company, adopts the methodology for its PPM to demonstrate the practical application of the research.
Advanced design, analysis, and implementation of pervasive and smart collaborative systems enabled with knowledge modelling and big data analytics
(2017-08-01)
Trappey, Amy J. C.
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Elgh, Fredrik
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Hartmann, Timo
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James, Anne
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Stjepandic, Josip
Advances in aligning knowledge systems, improving business logistics, driving innovation and adapting customer centric services
(2010-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
Advances in aligning knowledge systems, improving business logistics, driving innovation and adapting customer centric services
(2010-11-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
An evaluation model for low carbon island policy: The case of Taiwan's green transportation policy
(2012-06-01)
Trappey, Amy J. C.
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Trappey, Charles
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Hsiao, C. T.
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Ou, Jerry J. R.
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Li, S. J.
Conserving energy and reducing carbon emissions have become the common responsibility of the international community. During the year 2010, the Taiwan government planned a four-year project budgeted at 300 million US dollars, called "The Penghu Low Carbon Island Development Project." The policy objective is to use Penghu Island (population 85,000) as a test platform to evaluate new ways to conserve energy and reduce carbon emissions before attempting to replicate the policies on Taiwan Island. For Taiwan, a zero carbon island green transportation policy will regulate the total number of electric scooters, the total number of gasoline motorcycles, influence government subsidy incentives, and create the need for new motorcycle license issuing and control. These factors interact with each other to form a complex and dynamic system that impacts policy as well as the current way of life. In this study, a system dynamics approach is designed to construct a model for evaluating the green transportation policy on Penghu Island. Simulations are conducted to model green transportation system behavior and related policy effects in a smaller, controlled environment before creating policies for Taiwan Island that will impact the lives of over 23 million people. (C) 2012 Elsevier Ltd. All rights reserved.
An Intelligent System for Automated Binary Knowledge Document Classification and Content Analysis
(2011)
Chiang, Tzu-An
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Wu, Chun-Yi
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Trappey, Charles V.
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Trappey, Amy J. C.
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交大名義發表
Many companies rely on patent engineers to search patent documents and offer recommendations and advice to R&D engineers. Given the increasing number of patent documents filed each year, new means to effectively and efficiently identify and manage technology specific patent documents are required. This research applies a back-propagation artificial neural network (BPANN), a hierarchical ontology technique, and a normalized term frequency (NTF) method to develop an intelligent system for binary knowledge document classification and content analysis. The intelligent system minimizes inappropriate patent document classification and reduces the effort required to search and screen patents for analysis. Finally, this paper uses the design of light emitting diode (LED) lamps as a case study to illustrate and verify the efficiency of automated binary knowledge document classification and content analysis.
An Intelligent Transformer Maintenance System with Real-time Life Assessment Condition Monitoring
(2013-12-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Sun, Yong
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Ma, Lin
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Chen, Leo Kuan Ju
Transformers are critical assets that require continuous monitoring in the generation of electrical power. The management of engineering assets requires real-time diagnosis and preventive maintenance in order to avoid unexpected and catastrophic equipment shut-downs. In addition, the health status and remaining life of the transformer are critical knowledge for proper maintenance and management. This research focuses on oil-immersed transformers as a case study to help managers compile real-time monitoring and sampling data for information systems. The deterioration of the transformation's insulating paper was measured to derive the transformer's remaining life and to project the optimal replacement time. The proposed prediction method uses the Group Method of Data Handling (GMDH) to estimate the remaining life of any given transformer in use. This algorithm uses the dissolved gas-in-oil and furfural formation as the input variables to form the transformer life assessment model. Finally, the derived model is compared to models proposed by the International Electrotechnical Commission (IEC). The results show improved performance over the currently used IEC models.
An R&D knowledge management method for patent document summarization
(2008)
Trappey, Amy J. C.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
Purpose - In an era of rapidly expanding digital content, the number of e-documents and the amount of knowledge frequently overwhelm the R&D teams and often impede intellectual property management. The purpose of this paper is to develop an automatic patent summarization method for accurate knowledge abstraction and effective R&D knowledge management. Design/methodology/approach - This paper develops an integrated approach for automatic patent summary generation combining the concepts of key phrase recognition and significant information density. Significant information density is defined based on the domain-specific key concepts/phrases, relevant phrases, title phrases, indicator phrases and topic sentences of a given patent document. Findings - The document compression ratio and the knowledge retention ratio are used to measure both quantitative and qualitative outcomes of the new summarization methodology. Both measurements indicate the significant benefits and superior results of the method. Research limitations/implications - In order to implement the methodology with practical success, the accurate and efficient pre-processing of identifying key concepts and relevant phrases of patent documents is required. The approach relies on a powerful text-mining engine as the pre-process module for key phrase extraction. Practical implications - The methodology helps R&D companies consistently and automatically process, extract and summarize the core knowledge of related patent documents. This enabling technology is critical to R&D companies when they are competing to create new technologies and products for short life cycle marketplaces. Originality/value - This research addresses a new perspective in R&D knowledge management, particularly in solving the knowledge-overloading issue. The methodology helps R&D collaborative teams consistently to summarize the core knowledge of patent documents with efficiency. Efficient R&D knowledge management helps the firm to take advantage of IP positioning while avoiding patent conflict and infringement.