In applying Six Sigma, organizations, teams, and project managers seek to implement strategies that are based on measurement and metrics. Historically, many business leaders made decisions based on intuition or experience. Despite some common beliefs in various industries, Six Sigma doesn’t remove the need for experienced leadership, and it doesn’t negate the importance of intuition in any process. Instead, Six Sigma works alongside other skills, experience, and knowledge to provide a mathematical and statistical foundation for decision making. Experience might say a process isn’t working; statistics prove that to be true. Intuition might guide a project manager to believe a certain change could improve output; Six Sigma tools help organizations validate those assumptions.

Decision Making Without Six Sigma

Without proper measurement and analysis, decision making processes in an organization might proceed as follows:

• Someone with clout in the organization has a good idea or takes interest in someone else’s idea.
• Based on past experience or knowledge, decision makers within an organization believe the idea will be successful.
• The idea is implemented; sometimes it is implemented in beta mode so expenses and risks are minimized.
• The success of the idea is weighed after implementation; problems are addressed after they impact products or processes in some way in the present or the future.

Beta testing is sometimes used in a Six Sigma approach, but the idea or change in question goes through rigorous analysis and data testing first. The disadvantage of launching ideas into beta—or to an entire population--without going through a Six Sigma methodology is that organizations can experience unintended consequences from changes, spend money on ideas that don’t end up working out as planned, and impact customer perceptions through trial-and-error periods rife with opportunities for error. In many cases, organizations that don’t rely on data make improvements without first understanding the true gain or loss associated with the change. Some improvements may appear to work on the surface without actually impacting customer satisfaction or profit in a positive way.

Decision Making With Six Sigma

The Six Sigma method lets organizations identify problems, validate assumptions, brainstorm solutions, and plan for implementation to avoid unintended consequences. By applying tools such as statistical analysis and process mapping to problems and solutions, teams can visualize and predict outcomes with a high-level of accuracy, letting leadership make decisions with less financial risk.

Six Sigma methods don’t offer a crystal ball for organizations, though. Even with expert use of the tools described in this book, problems can arise for teams as they implement and maintain solutions. That’s why Six Sigma also provides for control methods: once teams implement changes, they can control processes for a fraction of the cost of traditional quality methods by continuing the use of Six Sigma tools and statistics.

Defining 6σ

Six Sigma as a methodology for process improvement involves a vast library of tools and knowledge, which will be covered throughout this book. In this section, we’ll begin to define the statistical concept represented by 6σ. At the most basic definition, 6σ is a statistical representation for what many experts call a “perfect” process. Technically, in a Six Sigma process, there are only 3.4 defects per million opportunities. In percentages, that means 99.99966 percent of the products from a Six Sigma process are without defect. At just one sigma level below—5σ, or 99.97 percent accuracy--processes experience 233 errors per million opportunities. In simpler terms, there are going to be many more unsatisfied customers.

Real World Examples

According to the National Oceanic and Atmospheric Administration, air traffic controllers in the United States handle 28,537 commercial flights daily. 1 In a year, that is approximately 10.416 million flights. Based on a Five Sigma air traffic control process, errors of some type occur in the process for handling approximately 2,426 flights every year. With a Six Sigma process, that risk drops to 35.41 errors.

The CDC reports that approximately 51.4 million surgeries are performed in the United States in a given year. 2 Based on a 99.97 accuracy rate, doctors would make errors in 11,976 surgeries each year, or 230 surgeries a week. At Six Sigma, that drops to approximately 174 errors a year for the entire country, or just over 3 errors each week. At Five Sigma, patients are 68 times more likely to experience an error at the hands of medical providers.

While most people accept a 99.9 percent accuracy rate in even the most critical services on a daily basis, the above examples highlight how wide the gap between Six Sigma and Five Sigma really is. For organizations, it’s not just about the error rate—it’s also about the costs associated with each error.

Consider an example based on Amazon shipments. On Cyber Monday in 2013, Amazon processed a whopping 36.8 million orders. 3 Let’s assume that each order error costs the company an average of $35 (a very conservative number, considering that costs might include return shipping, labor to answer customer phone calls or emails, and labor and shipping to right a wrong order).

Cost of Amazon Order Errors, 5 σ

Total Orders - 36.8 million, Errors - 8574.4, Average Cost per Error - $35, Total Cost of Errors -  $300,104.00

Cost of Amazon Order Errors, 6 σ

Total Orders - 36.8 million, Errors - 125.12, Average Cost per Error - $35, Total Cost of Errors - $4,379.20

For this example, the cost difference in sigma levels is still over $295,000 for the Cyber Monday business. For most organizations, Six Sigma processes are a constant target. Achieving and maintaining Six Sigma “perfection” is difficult and requires continuous process improvement. But even advancing from lower levels of sigma to a Four or Five Sigma process has a drastic impact on costs and customer satisfaction. Let’s look at the Amazon Cyber Monday example at other levels of sigma

At very low levels of sigma, any process is unlikely to be profitable. The higher the sigma level, the better the bottom line is likely to be.