160 International Journal of Computer Application and Engineering Technology Volume 3-Issue1, Jan 2014.Pp. 160-171 www.ijcaet.net PROCEDURE FOR SELECTION OF SIX SIGMA REPETITIVE GROUP VARIABLES SAMPLING PLAN Dr.D.Senthilkumar Associate Professor, Department of Statistics, PSG College of Arts & Science, Coimbatore -14, Tamil Nadu, India. *email:alamsen@rediffmail.com B.Esha Raffie Research Scholar, Department of Statistics, PSG College of Arts & Science, Coimbatore – 14, Tamil Nadu, India. ABSTRACT: Six sigma techniques were applied at Motorola for the manufacturing process quality improvement initiative. This successful method has been adapted immediately to other major industries like GE and Honeywell. This process would have only 3.4 defects per million opportunities. This article proposes the six sigma repetitive group sampling variables plan where the quality characteristic follows normal distribution. Tables are also constructed for the selection of parameters of known and unknown standard deviation variables repetitive group sampling plan indexed by six sigma acceptable quality level and six sigma limiting quality level. The objective of Six Sigma repetitive group sampling variables plan is to deliver high performance, reliability, and value to the end customer. KEYWORDS: Six Sigma Repetitive Group Sampling Variables Plan, Six Sigma Acceptable Quality Level, Six Sigma Limiting Quality Level, Operating Characteristics Curve (OC) and Normal distribution. 1. INTRODUCTION Repetitive Group Sampling (RGS) plan is one of the attribute sampling plans developed by Sherman (1965). The operation of this plan is similar to that of the sequential sampling plan. Sherman has pointed out that the RGS plan will give an intermediate in sample size efficiency between the single sampling plan and the sequential sampling plan. Variables sampling plans involve comparing a statistic, such as the mean, with an acceptance limit in much the same way that the number of nonconforming items is compared to an acceptance number in attributes plans. Whenever the quality characteristic of interest is measurable, a variables sampling plan can be applied. The main advantage of the variables sampling plan is that the same operating characteristic (OC) curve can be attained with a smaller sample size than would be required by an attributes sampling plan. Thus, a variables acceptance sampling plan would require less sampling. Also, when destructive testing is employed, the variables sampling is particularly useful in reducing the costs of inspection (Lieberman and Resnikoff, 1955). Another advantage is that measurements data usually provide more information about the manufacturing process or lot than do attributes data. Generally, numerical measurements of quality characteristics are more useful than simple classification of the item as conforming or nonconforming. It is also to be emphasized that when acceptable quality levels of a process are very small, the sample size required by attributes sampling plan is very large. Under these conditions, there may be significant advantages in switching to variables inspection. Soundrarajan and Ramasamy (1984, 1986) have contributed in the construction of Repetitive Group Sampling Plans, Govindaraju (1987) has shown that the OC functions of the RGS plan of