Major problems faced by plastic manufacturing factory were high defective percentage in produced product. This study is carried out at a plastic manufacturing factory which carried out injection mould process located in Penang. Injection mould process is one process to generate various products based on plastic. In this study researchers would study those products called Finder Base as study material by obtaining defect data from the specific factory to study the process capacity of the creation by using capability process index (Cpk) and also predicting any defect in the month of November and December 2009. From analysis made, it has been discovered that Finder Base product‟s manufacturing process is not capable in the manufacturing process because during 34th months of the product take out just nine month only index‟s value Cpk ≥ 1. While defect forecast in the month of November and December 2009 method used moving average (MA) found defect number increasingly rise against previous months with 2850 defect unit forecasted in the month of November and 2815 defect unit in December 2009.

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The purpose of this research is to improve production capabilities for Small Medium Enterprise (SME) industry.Research focused on SME,which produce sauce.This research used Time and Motion technique to improve work process at SME,and the research objectives towards accomplished this study is to identify problems in the production work process and improved it in terms of production time, number of process and production layout by proposing an efficient work process to SME.This research used systematic observation; process chart and stopwatch time study as research methodology.Stat Fit and Pro Model Software are used for data testing and develop new work process. The improvement of work process was executed by eliminating and combining of work process,which reduces production time, number of process and space utilization.

In many decision problems, criteria can be expressed as linear polynomial and corresponding optimization problems are multi-objective programs of linear type. Modeling of Gauss elimination technique of inequalities is derived for numerical solution of multi-objective linear programming problem by using concept of bounds. The method is quite useful because the calculations involved are simple and take least time. The method has been illustrated by a numerical example also.

Main purpose of this article is to have a posterior distribution of the model structural parameters in the regression model with elliptical measurement error based on prior distribution and considering the previous one was gained multivariable β it is not easy to produce data from it to measure the previous distribution mean. So we use Gibbs sampling method stated by Arellano and Bolfarine(1998). Finally we stated an application of the regression model.