Pohang University of Science and Technology · Business, Management and Accounting
Professor Kwang-Jae Kim's research lab specializes in advanced manufacturing systems, with a focus on multiresponse optimization, product-service system (PSS) design, and quality engineering. The lab develops innovative methodologies for optimizing complex manufacturing processes and supporting the creation of integrated product-service solutions that enhance system performance and customer satisfaction. Key research directions include model-free optimization techniques, quality function deployment, and the application of data-driven decision-making tools in industrial settings.
Figures are computed from collected data and may differ slightly.
SUMMARY A modelling approach to optimize a multiresponse system is presented. The approach aims to identify the setting of the input variables to maximize the degree of overall satisfaction with respect to all the responses. An exponential desirability functional form is suggested to simplify the desirability function assessment process. The approach proposed does not require any assumptions regarding the form or degree of the estimated response models and is robust to the potential dependences
A product-service system (PSS) is a novel type of business model that integrates products and services in a single system. It provides a strategic alternative to product-oriented economic growth and price-based competition in the global market. This research proposes a methodology to support the generation of innovative PSS concepts, called the PSS concept generation support system. The models and strategies of 118 existing PSS cases were analyzed, and the insights extracted were used to develop
Click to increase image sizeClick to decrease image sizeKey Words: Quality function deploymentTarget Design characteristicsOptimizationSpreadsheet
ABSTRACT Most of the works in multiresponse surface methodology have been focusing mainly on the optimization issue, assuming that the data have been collected and suitable models have been built. Though crucial for optimization, a good empirical model is not easy to obtain from the manufacturing process data. This article proposes a new approach to solving the multiresponse problem directly without building a model—an approach called patient rule induction method for multiresponse optimization
Industries such as automotive, LCD, PDP, semiconductor and steel produce products through multistage manufacturing processes. In a multistage manufacturing process, performances of stages are not independent. Therefore, the relationship between stages should be considered when optimising the multistage manufacturing process. This study proposes a new procedure of optimising a multistage manufacturing process, called multistage PRIM (patient rule induction method). Multistage PRIM extends the sco
Product-service system (PSS) is a novel type of business model integrating products and services in a single system. It provides a strategic alternative to product-oriented economic growth and price-based competition in the global market. This paper first reviews the current status of PSS, including its concept, characteristics,benefits, and cases. This paper then reviews the existing literature and identifies major research issues for three main phases of a PSS development lifecycle, namely, PS
A dual-response surface optimization approach assumes that response surface models of the mean and standard deviation of a response are fitted well to experimental data. However, it is often difficult to satisfy this assumption when dealing with a large volume of operational data from a manufacturing line. The proposed method attempts to optimize the mean and standard deviation of the response without building response surface models. Instead, it searches for an optimal setting of input variable
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