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Six sigma is a philosophy and quality tool or initiative to improve the quality of products or services by improving the system and processes involved. It is a measure of goodness involving the application of statistical methods to business processes to improve operating efficiency, reduce variation and waste, and avoid defects (Onyewuenyi, 2008). Six sigma was originally developed by Motorola in 1981. As of 2010, it enjoys wide sprea application in many sectors of industry, although its application are not without controversy. Each six sigma project carried out within an organisation follows a defined sequence of steps and has quantified targets. These targets contribute to financial cost reduction or project increase) or whatever is critical to the process (cycle time, safety, delivery etc). The term "six sigma' comes from a field of statistics known as process capacity studies. Originally it is referred to the ability of manufacturing processes to produce a very high proportion of output within specification – six sigma implication goals is to improve all processes to that level of quality. // In the education industry the customers' specification expected to be met, includes the expectations of students, staff, and parents, government and employers) Six sigma projects follow two project methodologies in inspired by Deming's Plan-Do-Check-Act cycle. These methodologies comprising five phases each bear the acronyms DMAIC and DMADV. DMAIC is used for projects aimed to improving an existing business processes while DMADV is used for projects aimed at creating new products or process design. For the purpose of this paper, the DMAIC would be more relevant. // DMAIC methodology has five phases Ø Define the problems, the voice of the customers and project goals, specifically Ø Measure key aspects of the current processes and collect relevant data Ø Analyse the data to investigate and verify causes- and- effect relationship. Determine what the relationship are and attempt to ensure that all factors have been considered seek out root causes of the defect under investigation Ø Improve or optimise the current process based upon data analysed using techniques such as design of experiments, poka yoke or mistake proofing and standard work to create a new future state processes set up runs to establish process capacity Ø Control the future state process to ensure that any deviations from the target are corrected before the result in defect. Control systems are implemented such as statistical processes control. Production boards and visual workplace control. Production boards and visual workplaces and the process is continuously monitored.