The purpose of this research is to study productivity improvement at electronic parts’s assembly line finding the work stations with work station time longer than takt time. This research applied the 5W1H technique as a tool to problem analysis fundamental of hand motions to improve productivity. The study found that the assembly line consisted 15 work stations and a cycle time of 12.0 seconds which greater than the takt time which was 10.5 seconds. The work station time no.6 took longer than the takt time. The fundamental of hand motion technique was applied. By separating or eliminating the fundamental of hand motion did not affect the performance of the work stations. By being integrated the fundamental of hand motion with previous work station or next work station. As a result of research is the new cycle time of this production line is 10.0 seconds the production line capacity increased from 300 pieces per hour to 360 pieces per hour 20 percent increase

The objectives of this research are to propose a proper model for defective products in an autoparts manufacturing industry to apply the proposed model to the defective products data to investigate the factors related to the machines producing defective products and to compare the proposed model with the generalized estimating equations model (GEE). The proposed model is a generalized linear mixed model (GLMM) in which the dependent variables have a Poisson distribution. The parameter are estimated using a Bayesian method. The data were collected from 12 machines 15 days for each machine from an autoparts manufacturing factory. The data collection dates vary depending on the working steps and the product types. The results show that the proposed model fits the data. The factors related to the machines producing defective products are working steps workers and product types. According to the estimates of the factors effects the working step producing the largest number of defective products is the working step 2 followed by the working steps 3 1 and 4 respectively. The worker producing the largest of defective products is the worker 4 followed by the workers 10 5 8 6 2 3 1 7 and 9 respectively. The product type 3 has the largest number of defective products followed by the product types 2 and 1 respectively. If the population-averaged effects are focused the GEE is applied. However the subject-specific is focused the GLMM is applied.