Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 13, Number 11 (2017), pp. 7879-7887 © Research India Publications http://www.ripublication.com S* Control Chart in Screw Quality Assessment Shamshuritawati Sharif 1 , Suzilah Ismail 2 and Zurni Omar 3 1, 2, 3 School of Quantitative Sciences, UUM-College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia. 1 Orcid: 0000-0002-3305-9661 Abstract Quality control chart is a powerful tool in assessing quality of products and services. The assembled of control chart involves two phases. The first phase function as the quality standard reference which consist of well behave samples that are within the control limit. The second phase is the real samples that need to be quality tested. In this study we constructed S* control chart based on the two phases using screws data. The advantage of this chart is fulfilling the requirement of high dimensional data set where the number of dimensions is more than the number of sample sizes (p > n) as displays in screw. The samples taken involved only three screws (n = 3) but seven variables (p = 7) measuring the quality of the screws. S* control chart was successfully constructed and able to act as warning signals in detecting defects screw. Keywords : large dimension, multivariate control chart, screw production, small sample size, statistical process control INTRODUCTION Statistical process control (SPC) is known to be effective methods for acquiring the quality standard of products or services through monitoring and controlling the process (Djauhari, Lee and Ismail, 2014). Originally, the application area of SPC is focusing in the manufacturing sector. Later, the expansion involving other areas, such as environmental science (Zimmerman, Dardeau, Crozier & Wagstaff, 1996); engineering (Mason & Young, 2002) and health care (Myles, German, Wilson and Wu, 2011; Smith, 2013). One of the SPC methods is control chart and has a strong point due to the quality controller is able to decide based on valid real time results instead of doing assumptions using “last month” performance (Myles et al, 2011). A control chart can be constructed