• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.80-88
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• 2017

Product data analytics (PDA) is a data-driven analysis method that uses product data management (PDM) databases as its operational data. It aims to understand and evaluate product development processes indirectly through the analysis of product data from the PDM databases. To educate and train PDA efficiently, this study proposed an approach that employs courses for both product development and PDA in a class. The participant group for product development provides a PDM database as a result of their product development activities, and the other group for PDA analyses the PDM database and provides analysis result to the product development group who can explain causes of the result. The collaboration between the two groups can enhance the efficiency of the education and training course on PDA. This study also includes an application example of the approach to a graduate class on PDA and discussion of its result.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.366-374
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• 2014

This study assessed and analysed a result of a product design experiment through Product Data Analytics (PDA), to find reasons for failure of some projects in the experiment. PDA is a computer-based data analysis that uses Product Data Management (PDM) databases as its operational databases. The study examines 20 product design projects in the experiment, which are prepared to follow same product development process by using an identical PDM system. The design result in the PDM database is assessed and analysed by On-Line Analytical Processing (OLAP) and data mining tools in PDA. The assesment and analysis reveals the lateness in creation of 3D CAD models as the main reason of the failure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.139-146
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• 2016

This study proposes a methodology to analyse social networks among participating design engineers during product development projects. The proposed methodology enables product development managers or the participating design engineers to make a proper decision on product development considering the performance of participating design engineers. It considers a product development environment where an integrated product data management (PDM) system manages the product development data and associated product development processes consistently in its database, and all the design engineers share the product development data in the PDM database for their activities in the product development project. It provides a novel approach to build social networks among design engineers from an operational product development data in the PDM database without surveys or monitoring participating engineers. It automatically generates social networks among the design engineers from the product data and relationships specified by the participants during the design activities. It allows analysts to gather operational data for their analysis without additional efforts for understanding complex and unstructured product development processes. This study also provides a set of measures to evaluate the social networks. It will show the role and efficiency of each design engineers in the social network. To show the feasibility of the approach, it suggests an architecture of social network analysis (SNA) system and implemented it with a research-purpose PDM system and R, a statistical software system. A product configuration management process with synthetical example data is applied to the SNA system and it shows that the approach enables analysts to evaluate current position of design engineers in their social networks.

• The Journal of Society for e-Business Studies
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• v.1 no.2
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• pp.93-116
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• 1996

Nowadays, there are many efforts to integrate CAD/CAM and other systems. The key of integration is engineering database implementation through the product data definition. In this paper, we suggest the product data definition and their properties for electronic consumer product throughout the requirement analysis for engineering database. Electronic consumer products include electric/electronic parts. as well as mechanical part which mainly compose of machinery. The paper is composed of three parts: the first is analysis about engineering data base. the second is understanding of product data structure and properties. and the last is modeling of product data including static and dynamic characteristics.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• Korean Journal of Computational Design and Engineering
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• v.19 no.2
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• pp.103-110
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• 2014

Engineering changes are indispensable engineering and management activities for manufactures to develop competitive products and to maintain consistency of its product data. Analysis of engineering changes provides a core functionality to support decision makings for engineering change management. This study aims to develop a method for analysis of engineering changes based on On-Line Analytical Processing (OLAP), a proven database analysis technology that has been applied to various business areas. This approach automates data processing for engineering change analysis from product databases that follow an international standard for product data management (PDM), and enables analysts to analyze various aspects of engineering changes with its OLAP operations. The study consists of modeling a standard PDM database and a multidimensional data model for engineering change analysis, implementing the standard and multidimensional models with PDM and data cube systems and applying the implemented data cube to core functions of engineering change management, the evaluation and propagation of engineering changes.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.334-341
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• 2008

Computer-aided engineering (CAE) analysis is considered essential for product development because it decreases the simulation time, reduces the prototyping costs, and enhances the reusability of product parts. The reuse of quality-assured CAE data has been continually increasing due to the extension of product lifecycle management; PLM, which is widely used, shortens the product development cycle and improves the product quality. However, less attention has been paid to systematic research on the interoperability of CAE data because of the diversity of CAE data and because the structure of CAE data is more complex than that of CAD data. In this paper, we suggest a CAE data exchange method for the effective sharing of geometric and analysis data. The method relies on heterogeneous CAE systems, a virtual reality system, and our developed CAE middleware for CAE data exchange. We also designed a generic CAE kernel, which is a critical part of the CAE middleware. The kernel offers a way of storing analysis data from various CAE systems, and, with the aid of a script command, enabling the data to be translated for a different system. The reuse of CAE data is enhanced by the fact that the CAE middle-ware can be linked with a virtual reality system or a product data management system.

• Korean Journal of Computational Design and Engineering
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• v.18 no.1
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• pp.36-48
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• 2013

In general, the product is used under several circumstances including environmental and usage conditions. According to the circumstances, the product has various performance degradation processes. In order to optimize the lifecycle of product usage, it is important to observe the degradation process and make suitable decisions on product operations. However, there are not much research works in evaluating the degree of product degradation based on product usage data. Recently, due to emerging ICT (Information and Communication Technology) technologies, it becomes possible to get the product usage data. Based on the gathered data, it is possible to analyze the degree of product degradation. The analysis of product usage data can improve product use and product design with advanced decisions. To this end, this study addresses one approach based on FMEA/FMECA method, called PDMCA (Performance, Degradation Modes and Criticality Analysis) for evaluating product degradation status and making suitable decisions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2704-2719
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• 2022

Lately, neo-Chinese style furniture has been frequently noticed by product design professionals for the big part it played in promoting traditional Chinese culture. This article is an attempt to use big data semantic analysis method to provide effective design research method for neo-Chinese furniture design. By using big data mining program TEXTOM for big data collection and analysis, the data obtained from typical websites in a set time period will be sorted and analyzed. On the basis of "neo-Chinese furniture" samples, key data will be compared, classification analysis of overall data, and horizontal analysis of typical data will be performed by the methods of word frequency analysis, connection centrality analysis, and TF-IDF analysis. And we tried to summarize according to the related views and theories of the design. The research results show that the results of data analysis are close to the relevant definitions of design. The core high-frequency vocabulary obtained under data analysis, such as popular, furniture, modern, etc., can provide a reasonable and effective focus of attention for the designs. The result obtained through the systematic sorting and summary of the data can be a reliable guidance in the direction of our design. This research attempted to introduce related big data mining semantic analysis methods into the product design industry, to supply scientific and objective data and channels for studies on design, and to provide a case on the practical application of big data analysis in the industry.