Six Sigma

Fundamentals

Motorola’s Six Sigma process was first developed and implemented in the 1980s for manufacturing, and from 1990 the process was adapted to the nonmanufacturing areas of the company. The content of Motorola’s “Six Steps to Six Sigma” in nonmanufacturing is as follows: (1) Identify the product you create or the service you provide to external or internal customers; (2) identify the customer for your product or service and determine what he or she considers important (your customers will tell you what they require to be satisfied; failure to meet a customer’s critical requirements is a defect); (3) identify your needs (including needs from your suppliers) to provide product or service so that it satisfies the customer; (4) define the process for doing the work (map the process); (5) mistake proof the process and eliminate wasted effort and delays; and (6) ensure continuous improvements by measuring, analyzing, and controlling the improved process (establish quality and cycle time measurements and improvement goals; the common quality metric is number of defects per unit of work).

It follows from the Six Steps to Six Sigma methodology that the aim is to improve the quality of process outputs, improving customer satisfaction and at the same time reducing waste, time, and costs. To achieve that ambitious aim, Six Sigma focuses on identifying and removing the causes of failures and defects, [Page 708]reducing variation by applying a set of statistical methods and other methodologies of quality management. It follows also that the methodology is a data-driven improvement approach that step-by-step is minimizing failures and variations in a structured and systematic way. The methodology is used on well-defined projects such as a product, a service, or a process, and each Six Sigma project established has clear goals in terms of failure, cost, or time reduction.

The term Six Sigma is related to statistical modeling of variation in any process or any product and indicates a degree of process capability. When a process for example is “in statistical control,” which means that only system or common causes affect the variation, then it is known that process output with a high probability will vary within +/–3 sigma where sigma is the standard deviation of the measured output characteristic. This interval is also called the natural variation.

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