Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6

ตัวแบบผสมเชิงเส้นสาหรับข้อมูลอนุกรมเวลาเชิงพื้นที่ที่มีฤดูกาลรวมอยู่ด้วยประยุกต์ใช้กับผลผลิตยางพาราในจังหวัดภาคใต้ของประเทศไทย

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2559The objectives of this research are to propose a linear mixed model (LMM) for spatial time series with a seasonal component, to apply the proposed model to monthly rubber yields in southern provinces of Thailand, to forecast rubber yields in southern provinces of Thailand and to compare the performance of the proposed model to the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model is a linear mixed model (LMM) with spatial effects following a conditional autoregressive model (CAR model). Seasonal dummy variables and Fourier terms are two methods used to account for the seasonal effects. A Bayesian method is used for parameter estimation. The estimated monthly yields are used to forecast the monthly rubber yields. The dependent variables are the monthly rubber yields in each province. The effects considered are spatial effects, heterogeneity effects, and seasonal effects. The data are secondary data at a provincial level. The results show that the effects influencing on the amount of rubber yields are spatial, heterogeneity, and seasonal effects. The proposed model with sesonal dummy variables is the most appropriate model copared to the LMM with Fourier sesonal effects, Holt-Withers ES and SARIMA. The mean absolute errors (MAE) are smallest in both model fitting and model validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.Rajamangala University of Technology Phra Nakho

Creating and Finding Efficiency Validation of Computer Assisted Instruction Focused on the Practiced to Hardness Test

รายงานการวิจัย-- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร,พ.ศ.2548The purposes of the research were to create and find efficiency validation of Computer Assisted Instruction Focused on the Practiced to Hardness Test, for the standard 90/90 and analyze the students’ learning achievement after using computer assisted instruction. The samples were the 20 first year, Bachelor of Engineering program in lndustrial Engineering, Rajamangala University of Technology Phra Nakhon North Bangkok Campus.Rajamangala University of technology Phranakho

Creating and Finding Efficiency Validation of Computer Assisted Instruction Focused on the Practiced to Torsion Test

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร,พ.ศ.2549The purposes of the research were to create and find efficiency validation of computer assisted instruction focused on the practice to Torsion Test, for the standard 90/90 and analyze the students' learning achievement after using computer assisted instruction. The samples were the 20 first year, Bachelor of Engineering Program in Industrial Engineering, Rajamangala University of Technology Phra Nakhon. The researcher experimented by using pretest, and then using the computer assisted instruction focus on the practice to Torsion Test in learning, next the students did the posttest. After that the researcher calculated to find the computer assisted instruction efficiency and analyzed the students' learning achievement after studying. The result revealed that the computer assisted instruction efficiency was efficient for standard 90/90. And after studying by using computer assisted instruction, the students' learning achievement increased significantly at 0.05.Rajamangala University of Technology Phra Nakho

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6

การวิเคราะห์สาเหตุของการผลิตสินค้าบกพร่องและเสียด้วยตัวแบบการถดถอยโลจิสติกส์แบบเบย์: กรณีศึกษาโรงงานผลิตชิ้นส่วนรถยนต์

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2557This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life, machine type 6, worker group 3 and 4, working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimationRajamangala University of Technology Phra Nakho

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6