Pub Date : 2012-09-01DOI: 10.30016/JGS.201209.0003
Tiejun Dai, Xiaotong Huang
As initial value plays an important role in discrete GM model fitting accuracy, initial value of discrete GM model is selected by designing calculation program to reduce fitting error. Output results can show that the fitting equation of minimum relative error may pass through any point of 1-AGO series, and its fitting accuracy is better than that of traditional grey model and original discrete grey model. The calculating method has an application value.
{"title":"Selection of Discrete GM Model Initial Value by Designing Calculation Program","authors":"Tiejun Dai, Xiaotong Huang","doi":"10.30016/JGS.201209.0003","DOIUrl":"https://doi.org/10.30016/JGS.201209.0003","url":null,"abstract":"As initial value plays an important role in discrete GM model fitting accuracy, initial value of discrete GM model is selected by designing calculation program to reduce fitting error. Output results can show that the fitting equation of minimum relative error may pass through any point of 1-AGO series, and its fitting accuracy is better than that of traditional grey model and original discrete grey model. The calculating method has an application value.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70061273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-01DOI: 10.30016/JGS.201209.0001
Chih-Jiun Lin, Chien-Yan Hsieh
The purpose of this study was to construct a wine evaluation model by using Analytical Hierarchy Process (AHP). Samples totaling 30 in number were subjected to find the main components and analyze the volatile elements. The elements studied included five components: dry extract, pH, titratable acidity, alcohol level and residual sugar. The price of the wine will also be included. Analytical Hierarchy Process is built basically on these six elements. This thesis will use interviews and evaluation of experts' questionnaires to sift the evaluation index. Next, the AHP is applied to conduct professional evaluation, and then get the relative weights of indicators under the examination of evaluation index. The result will give the ratings of the wines from poor to excellent. It is hoped that this study may lead to helpful for assist consumer in choosing a wine. The model will be able to commend adaptive the wine to consumer and provide the information of the wine. It will be very useful in hospitality industry.
{"title":"A Study of Using Analytical Hierarchy Process and Grey Relational Grade in Wine Evaluation","authors":"Chih-Jiun Lin, Chien-Yan Hsieh","doi":"10.30016/JGS.201209.0001","DOIUrl":"https://doi.org/10.30016/JGS.201209.0001","url":null,"abstract":"The purpose of this study was to construct a wine evaluation model by using Analytical Hierarchy Process (AHP). Samples totaling 30 in number were subjected to find the main components and analyze the volatile elements. The elements studied included five components: dry extract, pH, titratable acidity, alcohol level and residual sugar. The price of the wine will also be included. Analytical Hierarchy Process is built basically on these six elements. This thesis will use interviews and evaluation of experts' questionnaires to sift the evaluation index. Next, the AHP is applied to conduct professional evaluation, and then get the relative weights of indicators under the examination of evaluation index. The result will give the ratings of the wines from poor to excellent. It is hoped that this study may lead to helpful for assist consumer in choosing a wine. The model will be able to commend adaptive the wine to consumer and provide the information of the wine. It will be very useful in hospitality industry.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-06-01DOI: 10.30016/JGS.201206.0005
Yu-Lung Tsai, Huang Li, Kun-Yan Lee
Stock investment has become the tool for the majority of people who want to accumulate wealth through investment banking. However, due to it is difficult to master the ups and downs of the stock prices, investors often end in loss. How to use an effective and reasonable investment approach to protect capital safety and sell or temporarily stop buying when the stock market declines has become more important. Stock investors dream of maintaining or buying the stocks when the stock market goes up, and earn more money than lose money in the stock market. Prof. Deng purposed the grey system theory, which constructs correlation analysis and system modeling to the uncertainty of the system model and information integrity. Also, the methods of forecasting and decision-making are used to explore and understand the system. Originally, it was used in the field of automatic control, but this paper applies the grey system theory, to stock price forecasting. By using the closing prices of the first five weeks, we can forecast six week's closing price. Several stocks in 2011 are used as simulation predictions, and we found that the average errors are between 2% to 7%. Hence, by using the grey system theory, the new stock price prediction model can be established for investors.
{"title":"The Prices Prediction of Taiwan Stock via GM(1,1) Method","authors":"Yu-Lung Tsai, Huang Li, Kun-Yan Lee","doi":"10.30016/JGS.201206.0005","DOIUrl":"https://doi.org/10.30016/JGS.201206.0005","url":null,"abstract":"Stock investment has become the tool for the majority of people who want to accumulate wealth through investment banking. However, due to it is difficult to master the ups and downs of the stock prices, investors often end in loss. How to use an effective and reasonable investment approach to protect capital safety and sell or temporarily stop buying when the stock market declines has become more important. Stock investors dream of maintaining or buying the stocks when the stock market goes up, and earn more money than lose money in the stock market. Prof. Deng purposed the grey system theory, which constructs correlation analysis and system modeling to the uncertainty of the system model and information integrity. Also, the methods of forecasting and decision-making are used to explore and understand the system. Originally, it was used in the field of automatic control, but this paper applies the grey system theory, to stock price forecasting. By using the closing prices of the first five weeks, we can forecast six week's closing price. Several stocks in 2011 are used as simulation predictions, and we found that the average errors are between 2% to 7%. Hence, by using the grey system theory, the new stock price prediction model can be established for investors.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-06-01DOI: 10.30016/JGS.201206.0004
Bor-tyng Wang, T. Sheu, Jung-Chin Liang, J. Tzeng, M. Nagai
The purpose of this paper is to apply an integrated approach to cluster the English reading performances among college students because finding the optimal teaching strategy for an individual student is difficult. The unified English reading exam with forty questions was used to clarify the different performances between day-time and extension classes which have thirty-eight students and thirty-seven students, respectively. The Grey Student-Problem(GSP) chart, which includes the equation of Rasch model, was then generated. Then the method of Grey Structural Modeling (GSM ) was used to cluster the students into appropriate groups. According to the GSP chart and GSM, students' reading performances are clustered into classes based on their levels. Finally, we used Interpretive Structural Model (ISM) to display the concept structure of each group. The results indicate that the teachers could provide adaptive teaching methods and remedial instructions based on the graphic models. Also, parents could understand their children's learning conditions better by reading the clear graphs. We suggested that the GSP chart can not only be applied to the educational field, but also be used in real-life applications, like medical data analysis, engineering, or decision-making fields.
{"title":"Clustering the English Reading Performances by Using GSP And GSM","authors":"Bor-tyng Wang, T. Sheu, Jung-Chin Liang, J. Tzeng, M. Nagai","doi":"10.30016/JGS.201206.0004","DOIUrl":"https://doi.org/10.30016/JGS.201206.0004","url":null,"abstract":"The purpose of this paper is to apply an integrated approach to cluster the English reading performances among college students because finding the optimal teaching strategy for an individual student is difficult. The unified English reading exam with forty questions was used to clarify the different performances between day-time and extension classes which have thirty-eight students and thirty-seven students, respectively. The Grey Student-Problem(GSP) chart, which includes the equation of Rasch model, was then generated. Then the method of Grey Structural Modeling (GSM ) was used to cluster the students into appropriate groups. According to the GSP chart and GSM, students' reading performances are clustered into classes based on their levels. Finally, we used Interpretive Structural Model (ISM) to display the concept structure of each group. The results indicate that the teachers could provide adaptive teaching methods and remedial instructions based on the graphic models. Also, parents could understand their children's learning conditions better by reading the clear graphs. We suggested that the GSP chart can not only be applied to the educational field, but also be used in real-life applications, like medical data analysis, engineering, or decision-making fields.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-06-01DOI: 10.30016/JGS.201206.0007
Mei-Lien Kan, Kuei-Feng Lee, Yuan-Bing Lee
The paper brings together the average daily sales of a Farmers' Association supermarket from May, 2010 to December, 2010. First, we examine whether the data meet the conditions of modeling, and we determine the average amount of daily sales data are in line with the GM (1,1) modeling of the capacity district. Also, it meets the conditions of the modeling prediction accuracy higher than 90%. Due to the traditional grey prediction GM (1,1) model only needs 4 data, but residual modification grey prediction GM (1,1) mode needs at least five data. Hence, the GM (1,1) model uses the raw data from May, 2010 to November, 2010 to predict values in December, 2010 to do error analysis. Also, we apply the GM (1,1) rolling test, and use the data of 5 groups, 6 groups and 7 groups to calculate six kinds of GM (1,1) prediction model, and we find the raw data with minimum error as the grey prediction GM (1,1) model. After determining the optimal grey prediction period, we apply six grey prediction methods and the optimal grey prediction period to predict the values of December, 2010 to do error analysis. We also use average error value as a criteria to select the best prediction model, and by using the top four of the best prediction model, we are able to predict next three months' growth trend.
{"title":"Apply Differences Grey Prediction Methods in the Selling of LOHAS","authors":"Mei-Lien Kan, Kuei-Feng Lee, Yuan-Bing Lee","doi":"10.30016/JGS.201206.0007","DOIUrl":"https://doi.org/10.30016/JGS.201206.0007","url":null,"abstract":"The paper brings together the average daily sales of a Farmers' Association supermarket from May, 2010 to December, 2010. First, we examine whether the data meet the conditions of modeling, and we determine the average amount of daily sales data are in line with the GM (1,1) modeling of the capacity district. Also, it meets the conditions of the modeling prediction accuracy higher than 90%. Due to the traditional grey prediction GM (1,1) model only needs 4 data, but residual modification grey prediction GM (1,1) mode needs at least five data. Hence, the GM (1,1) model uses the raw data from May, 2010 to November, 2010 to predict values in December, 2010 to do error analysis. Also, we apply the GM (1,1) rolling test, and use the data of 5 groups, 6 groups and 7 groups to calculate six kinds of GM (1,1) prediction model, and we find the raw data with minimum error as the grey prediction GM (1,1) model. After determining the optimal grey prediction period, we apply six grey prediction methods and the optimal grey prediction period to predict the values of December, 2010 to do error analysis. We also use average error value as a criteria to select the best prediction model, and by using the top four of the best prediction model, we are able to predict next three months' growth trend.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-01DOI: 10.30016/JGS.201112.0001
Chih-Sung Lai, Hsing-Hui Chu
This study takes Nintendo Wii as an innovative example to study the key product attributes from the perspective of consumers' purchase intention. By using grey relational analysis and GM(0,N) method, the result showed convenient operation, abundant games and Chinese manipulation interface attract consumers far more than superior appearance, realistic ambience and motion sensing remote. It could be inferred that user friendly interface and entertainment are the top purchase guide for consumers while purchase intention considered. In conclusion, consumers will pay more attention to the function of home video game console, not their fashion design. Therefore, home video game developers should concentrate more on and generate really high performance products.
{"title":"The Grey Analysis on Key Product Attributes of Home Video Game Based on Purchase Intention","authors":"Chih-Sung Lai, Hsing-Hui Chu","doi":"10.30016/JGS.201112.0001","DOIUrl":"https://doi.org/10.30016/JGS.201112.0001","url":null,"abstract":"This study takes Nintendo Wii as an innovative example to study the key product attributes from the perspective of consumers' purchase intention. By using grey relational analysis and GM(0,N) method, the result showed convenient operation, abundant games and Chinese manipulation interface attract consumers far more than superior appearance, realistic ambience and motion sensing remote. It could be inferred that user friendly interface and entertainment are the top purchase guide for consumers while purchase intention considered. In conclusion, consumers will pay more attention to the function of home video game console, not their fashion design. Therefore, home video game developers should concentrate more on and generate really high performance products.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-01DOI: 10.30016/JGS.201112.0005
Li-Xia Chang, Wei-Dong Gao, Ming-wang Shi, R. Pan
This paper made a study on fashion color forecasting by applying GM (1,1) model. It took successive five years fashion colors' suggestion ratio as the time series. Results shows that the GM(1,1) model would provide promising predicting effect with minimum accuracy ratio of 56.46% and maximum accuracy of 88.68%. Besides the 1AGO data processing, we also apply 2AGO data processing to discuss the forecasting accuracy of this model. Results show that the forecasting with 2AGO data processing can predict the future color trend with maximum accuracy ratio of 92.27% and the minimum ratio 79.62%, P value 1.0000. It can be taken as the promising one prediction model for future color trends forecasting.
{"title":"Applying Grey Model for International Fashion Color Trend Forecasting","authors":"Li-Xia Chang, Wei-Dong Gao, Ming-wang Shi, R. Pan","doi":"10.30016/JGS.201112.0005","DOIUrl":"https://doi.org/10.30016/JGS.201112.0005","url":null,"abstract":"This paper made a study on fashion color forecasting by applying GM (1,1) model. It took successive five years fashion colors' suggestion ratio as the time series. Results shows that the GM(1,1) model would provide promising predicting effect with minimum accuracy ratio of 56.46% and maximum accuracy of 88.68%. Besides the 1AGO data processing, we also apply 2AGO data processing to discuss the forecasting accuracy of this model. Results show that the forecasting with 2AGO data processing can predict the future color trend with maximum accuracy ratio of 92.27% and the minimum ratio 79.62%, P value 1.0000. It can be taken as the promising one prediction model for future color trends forecasting.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-01DOI: 10.30016/JGS.201112.0006
Erl-Shyh Kao
Low temperature and high diffusion capabilities are characteristics of Supercritical Carbon Dioxide Extraction Technology; it is able to extract the thermally unstable substance from natural products while retaining the active components. Therefore, this study uses Supercritical Carbon Dioxide Fluid Extraction Technology to extract Roselle polyphenols Caffeic Acid. Roselle contains polyphenols which has a lot of bioactivity, in which the active ingredient caffeic acid possesses antioxidant, capture of free radicals and inhibition of tyrosinase functions. Different proportions of water and ethanol as a cosolvent added to the carbon dioxide supercritical fluid for modification; changing flow rates of critical pressures, critical temperatures and supercritical fluids to arrive at the related assessment factors and using the grey GM (0,N) model from the grey theory to analyze the importance of each impact factor. The results show that in antioxidant activity, ability to catch free radicals and inhibition of tyrosinase; the extraction temperature is proven to be the most important impact factor. Compared with using traditional statistical analysis, the grey theory analysis method is able to give more magnitude and association relationships from less information.
{"title":"Apply GM(0,N) in Determinants Research by Supercritical Carbondioxide Fluid Extracts for Antioxidative Composition of \"Hibiscus Sabdariffa\" Linnaeu","authors":"Erl-Shyh Kao","doi":"10.30016/JGS.201112.0006","DOIUrl":"https://doi.org/10.30016/JGS.201112.0006","url":null,"abstract":"Low temperature and high diffusion capabilities are characteristics of Supercritical Carbon Dioxide Extraction Technology; it is able to extract the thermally unstable substance from natural products while retaining the active components. Therefore, this study uses Supercritical Carbon Dioxide Fluid Extraction Technology to extract Roselle polyphenols Caffeic Acid. Roselle contains polyphenols which has a lot of bioactivity, in which the active ingredient caffeic acid possesses antioxidant, capture of free radicals and inhibition of tyrosinase functions. Different proportions of water and ethanol as a cosolvent added to the carbon dioxide supercritical fluid for modification; changing flow rates of critical pressures, critical temperatures and supercritical fluids to arrive at the related assessment factors and using the grey GM (0,N) model from the grey theory to analyze the importance of each impact factor. The results show that in antioxidant activity, ability to catch free radicals and inhibition of tyrosinase; the extraction temperature is proven to be the most important impact factor. Compared with using traditional statistical analysis, the grey theory analysis method is able to give more magnitude and association relationships from less information.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-01DOI: 10.30016/JGS.201112.0002
Yuran Liu, Yunchuan Hu, Mingliang Hou
In order to improve the prediction accuracy to uncertainty signal, the fractional order grey prediction algorithm is proposed in this paper, and the measurement method of the grey information of signals is proposed for the first time. The paper attempts to deduce the fractional order grey prediction algorithm through fractional order Taylor expansion formula and provide the measurement method of the grey information of signals by analyzing the relations between fractional-order differential orders and the prediction accuracy. Fractional order grey prediction algorithm spares the complex operation of whitening grey information and makes full use of the grey information. Experiments have proven that the prediction accuracy of fractional order grey prediction algorithm has been greatly increased as compared with GM (1,1) algorithm.
{"title":"A Fractional Order Grey Prediction Algorithm","authors":"Yuran Liu, Yunchuan Hu, Mingliang Hou","doi":"10.30016/JGS.201112.0002","DOIUrl":"https://doi.org/10.30016/JGS.201112.0002","url":null,"abstract":"In order to improve the prediction accuracy to uncertainty signal, the fractional order grey prediction algorithm is proposed in this paper, and the measurement method of the grey information of signals is proposed for the first time. The paper attempts to deduce the fractional order grey prediction algorithm through fractional order Taylor expansion formula and provide the measurement method of the grey information of signals by analyzing the relations between fractional-order differential orders and the prediction accuracy. Fractional order grey prediction algorithm spares the complex operation of whitening grey information and makes full use of the grey information. Experiments have proven that the prediction accuracy of fractional order grey prediction algorithm has been greatly increased as compared with GM (1,1) algorithm.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-09-01DOI: 10.30016/JGS.201109.0004
Yung-Hui Lee
In the face of the global market competition environment, the business sector is well aware that ”The key to business organizational development lies in the use of human resources”, thus turning effective and systematic ”educational training” into one of the important management policies for organizational change. In addition to training personnel, enterprises also need good assessment tools. Therefore, with the Taiwan Training Quality System (TTQS) researched and developed in Taiwan, and case company A and B as benchmarks of the actual technology industry, the TTQS was employed. The exploration through the gray correlation analysis method shall serve as a reference for enterprises in their future importation of the TTQS, thereby enabling them to engage in self-assessment based on the resources accessible to them and enhance the overall human resources. The first stage of this study is the data collection stage: the critical assessment factors of the TTQS imported by enterprises were explored through literature review and data collection, which were compiled in the questionnaire. The second stage is the empirical analysis stage: targeting the business management levels of case company A and B and the TTQS related personnel, the questionnaire was implemented. Then, the questionnaire results collected were explored through the grey relational analysis method to obtain the weighting values of the assessment indicators. The results show that the crucial success factors of the TTQS importation obtained varied significantly depending on the different industrial features and sizes. Targeting the two case companies, the importation related suggestions were proposed to enable the enterprises to engage in self-assessment before investing in human resources. In addition, the solution strategies provided in this study served as the reference for enhancing the overall human resource were enhanced.
{"title":"Applying Grey Relational Analysis to Construct the Training Quality System in Enterprise","authors":"Yung-Hui Lee","doi":"10.30016/JGS.201109.0004","DOIUrl":"https://doi.org/10.30016/JGS.201109.0004","url":null,"abstract":"In the face of the global market competition environment, the business sector is well aware that ”The key to business organizational development lies in the use of human resources”, thus turning effective and systematic ”educational training” into one of the important management policies for organizational change. In addition to training personnel, enterprises also need good assessment tools. Therefore, with the Taiwan Training Quality System (TTQS) researched and developed in Taiwan, and case company A and B as benchmarks of the actual technology industry, the TTQS was employed. The exploration through the gray correlation analysis method shall serve as a reference for enterprises in their future importation of the TTQS, thereby enabling them to engage in self-assessment based on the resources accessible to them and enhance the overall human resources. The first stage of this study is the data collection stage: the critical assessment factors of the TTQS imported by enterprises were explored through literature review and data collection, which were compiled in the questionnaire. The second stage is the empirical analysis stage: targeting the business management levels of case company A and B and the TTQS related personnel, the questionnaire was implemented. Then, the questionnaire results collected were explored through the grey relational analysis method to obtain the weighting values of the assessment indicators. The results show that the crucial success factors of the TTQS importation obtained varied significantly depending on the different industrial features and sizes. Targeting the two case companies, the importation related suggestions were proposed to enable the enterprises to engage in self-assessment before investing in human resources. In addition, the solution strategies provided in this study served as the reference for enhancing the overall human resource were enhanced.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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