• Journal of Nutrition and Health
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• v.16 no.4
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• pp.281-286
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• 1983

The interactions of phytic acid with protein and minerals has been blamed to decrease the bioavailability of protein and minerals in soybean products. Tempeh, the traditional Infonesian fermented soybean product, was prepared to investigate the changes of phytic acid contents nesian fermented soybean product, was prepared to investigate the changes of phytic acid contents and its interactions with protein and minerals in the fermentation. The acceptability of tempeh were also studied by conducting sersory evaluation. 1) Phytic acid contents of cooked soybeans and of tempch were significantly lower than that of raw soybeans, indicating that cooking and fermentation resulted in the decrease in phytic acid content of soybeans. In tempeh the fraction of phytic acid retained after ultrafiltration was significantly lower than that in raw soybeans. 2) The total protein contents were not significantly different between raw soybeans and tempeh. Phytic acid contents per gram of protein retained ultrafiltration were significantly higher in raw soybeans than in tempeh. This result is interpreted as that raw soybeans contain higher amounts of phytic acid- protein complexes than tempeh. 3) Both of calcium and zinc contents were not significantly different among raw, cooked soybeans and tempeh. However, the retained Ca and Zn fraction after ultrafiltration were significantly lower in tempeh comparing with that in raw soybeans. Lower retention of Ca and Zn after ultrafiltration in tempeh may be the result of lower phytate content of tempeh, thereby less chance of forming mineral- phytate complexes. 4) Tempeh received the sensory evaluation scores between good and fair and the addition of garlic to tempeh significantly improved the odor, general desirability and total score.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.325-335
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• 2003

Robust design is to identify appropriate settings of control factors that make the system's performance robust to to changes in the noise factors that represent the source of variation. In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.65-75
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• 2003

In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.62-72
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• 1999

Taguchi´s parameter design is to determine the optimal settings of design parameters of a product or a process such that the characteristics of a product exhibit small variabilities around their targer values. His analysis of the problem has focused only on a single characteristic or response. However the quality of most products is seldom defined by a characteristic, and is rather the composite of a great number of characteristics which are often interrelated and nearly always measured in a variety of units. The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by unvariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. One existing case study is solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, the desirability functions, and EXTOPSIS model.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.77-92
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• 2008

Commonality strategy is a popular design practice in designing a product line as it enables the firm cost saving and simplification in design, manufacturing, and distribution processes. However the issue of commonality has been mostly analyzed from a profit maximizing firm's perspective and, to our knowledge, there is no literature that deals with the issue from a different perspective. in this paper, we consider the issue of commonality strategy from a social welfare maximization perspective, and argue that commonality strategy used in designing of public goods can bring certain benefits not just for a firm but also for consumers, i.e., for society as a whole. While we assume certain cost saving in production process due to economies of scale under commonality strategy, we conceptualize two different effects of commonality strategy, utility effect due to cost saving and weighted-averaging effect, and show how these two effects interplay to determine the design of common attribute and desirability of commonality strategy. We also discuss how the implementation of commonality strategy differs under different objectives of a product line designer : social welfare and firm's profit maximization.

• Journal of Korean Society for Quality Management
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• v.24 no.3
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• pp.111-132
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• 1996

Taguchi's parameter design is to determine optimal settings of design parameters of a product or a process such that the characteristics of a product exhibit small variabilities around their target values. His analysis of the problem has focused only on a single characteristic or response, but the quality of most products is seldom defined by a characteristic, and is rather the composite of a great number of characteristics which are often interrelated and nearly always measured in a variety of units. The critical problem in dealing with multiple characteristics is how to compromise the conflicts among the selected levels of the design parameters for each individual characteristic. In this paper, the EXTOPSIS Model using SN ratio which can be optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. Two existing case studies are solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, and the desirability function.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.377-390
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• 2011

A common problem encountered in product or process design is the selection of optimal parameter levels which involves simultaneous consideration of multiple response variables. This is called a multiresponse problem. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. Up to date, various methods have been proposed for the optimization, including the desirability function approach and loss function approach. In this paper, the existing studies in multiresponse optimization are reviewed and a future research direction is then proposed.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1263-1272
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• 2016

Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. However, a disadvantage of this method is that it requires an excessively large number of experiments. So combined array approach have been proposed to reduce the number of experiments. Taguchi has used the signal-to-noise ratio to find the optimum conditions in the Taguchi parameter design. In analyzing the data from the parameter design various problems tends occur by using an SN. In this paper, we propose an alternative solution for reducing the number of experiments without depending on the signal-to-noise ratio to overcome the shortcomings of the parameter design. Two examples illustrate this procedure in the two different experimental design (product array, combined array) approaches.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.2
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• pp.127-134
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• 2017

For product or process design and development, it is common to optimize multiple responses (characteristics) based on experimental data. To determine optimal conditions, we need to design the experiment, estimate a proper model for each response, and optimize the multiple responses simultaneously. There are several techniques and many research results on optimizing multiple responses simultaneously, when the experimental data are available. However, the experimental design issue for optimizing multiple responses has not been discussed yet. This paper proposes some idea on how to plan screening design when requirements for multiple performance characteristics are to be met in developing new products. A screening design procedure is developed for securing the requirements of multiple responses. Initial design factors are classified into three categories; specific, non-conflicting common, and conflicting common. After screening experiments, follow-up design region search method is suggested with respect to the most unsatisfied or important response, or overall desirability. A case study on a synthesis of melamine formaldehyde resin is presented to illustrate the procedure and to show the validity of the approach.