Six Sigma: Theory and Application
How does an organization use statistics to improve the quality of goods and services produced? What are the benefits and weaknesses to such statistical approaches in a business environment? Six Sigma is a business management strategy originally developed by Motorola. It can be used to improve quality by reducing the possibilities of variation in products and services through implementing steps to measure, analyze, improve and control work processes. While Six Sigma originated specifically within a manufacturing environment, it has been adapted and applied to all types of businesses around the world.
Conceptually, Six Sigma is used as a tool to continuously improve an organization’s output. Six Sigma will statistically measure the level of variation within a process and determine the expected defect level as matched against a customer’s specification. Once a process is established, Six Sigma can be used as a tool to understand the variables that can impact the variation within a process. This allows an organization to develop methods that can further reduce the variation and continuously improve the process. In collecting process data, an organization can determine which process would benefit the most from an improvement effort.
As an organization improves, one of the impacts that the company will immediately receive is lower defect levels. Since the company can predict the variation within the process, they have the ability to reduce the variation within acceptable control limits and reduce the defective material losses. This in turn reduces cost and waste. In addition, when an organization can produce products with a low defect level, they will maintain higher customer satisfaction and return business. Lastly, monitoring and understanding the process variability provides a measurable way to baseline, process and track improvements.
One of the major pitfalls of Six Sigma is the assumption that the data used is accurate. It is common for an organization to immediately think that its data must be accurate when the measurement tools themselves are faulty or inaccurate. Inaccurate measurement tools will provide skewed data and lead to potential changes that were not needed. Another pitfall is that the data may be biased by the operator collecting the data. This can again lead to false improvement efforts by an organization. In addition, an organization needs to understand that improvement takes time and money. Some organizations expect results to come quickly and with minimal effort or resources. This can negatively impact the team working to improve the process and result in corners being cut and poor results. Lastly, organizations need to make sure to keep the scope of the improvement efforts manageable. It is not uncommon for new opportunities for improvement to be identified during a project. However, the team needs to maintain focus on the original scope, otherwise they will become side tracked and little will