Part One

According to Park & Kim (2000) the term “Six Sigma” is derived from the normal distribution used in statistics. It uses the “bell curve” distribution that fits a number of real world situations which predicts the 3.4 defects-per-million over the long run for processes that have at least six standard deviations between the process average and the nearest specification limit. In normal distribution, extremely large and small values are rare and occur near the tail ends. Most values are clustered around the mean. In normal distribution, 68 percent of all values lie within one standard deviation, 95.45 percent within two standard deviations and 99.7 within three standard deviations called six sigma. In other words only one out of a thousand values will fall outside of six sigma. Nonetheless, is the relentless pursuit in the reduction of variation in all critical processes in order to arrive to a continuous breakthrough of improvements that creates an impact in the bottom-line and/or top-line of an organization and that customer satisfaction is improved.

Probability distribution is a statistical model that indicates the possible outcomes of a particular course of action as well as the statistical likelihood of each event. Probability distribution is important because it is used in predicting change and anticipating the outcomes in advance. It can also be used in creating scenario analyses which is aimed at building theoretically distinct possibilities for the expectations of a particular future event. For example a company can build three scenarios: likely, worst and best case. The likely scenario will have values towards the middle of the distribution, the worst would have values from the lower end and best scenario will have values in the upper end. Nevertheless probability distribution can be used to predict future sales level in other words, forecasting of sales. Businesses are better placed at predicting precise values for future sales. Probability distribution is also an important tool in risk evaluation for any company to make decisions on pursuing new business lines. Probability distribution is related to six sigma because they both act as measuring sticks in predicting the outcome of sales or distribution in a company. Once a company knows its future outcomes it will be able to decide on ways of achieving customer satisfaction. Probability distributions help to ascertain specific probability values in the distribution and lead the six sigma team down the hypothesis testing roadmap.

Discrete distribution is used to calculate probability for a countable number of occurrences of an event whereas continuous distribution is used to estimate the probability for a continuous range of values.Continous data can occupy any value over a continuous range but it is not for the case in discrete where data are restricted to defined separate values. Suppose a math teacher orders that all students must weigh between 120 and 200 pounds. The weight of a student would be an example of a continuous distribution since a student’s weight could only take any value between 120 and 200 pounds. Suppose a coin is flipped and the number of heads are counted, the number of heads could be an integer value between 0 and plus infinity. Nonetheless it could be any number 0 and plus infinity. We cannot get 2.5 heads. Thus, the number of heads must be discrete distribution.

Six sigma is connected to quality and productivity because it informs a company how gauges how good its products, services and processes are through the measurement of quality level. Under the leadership of top-level management, it is seen as management initiative that is aimed at creating innovations that are of high quality and that which satisfies the customer. Nonetheless it provides an environment for solving CTQ hurdles through combined team efforts. Since competition in productivity and quality of products has been on the rise, second hand quality goods will be depleted but six sigma combined with scientific, smart, systematic and statistical approaches paves way for flexibility in business management (Park& Kim, 2000).

Part Two

1. It is essential for managers to be trained in Six Sigma principles so that they the management can prove that they are committed to the program.
2. Customer satisfaction result from meeting the customer’s needs in a number of key areas.
3. Data-driven decision making is an integral part of the program
4. Six sigma methodologies  (none of the above)
5. As a company’s six sigma level rises the cost of quality increases.

References

Park, S. and Kim, K. (2000). A study of Six Sigma for R&D part, Quality Revolution, 1(1),         Korean Society for Quality Management (p.51–65).