How to Calculate Your Baseline Sigma

If you work in a manufacturing, processing, or logistic industry, chances are Six Sigma is a common conversation topic. Since its creation in the late 1980s, employers have used the methodology to improve their business process extensively. By definition, a Sigma is a statistical term that measures the number of defects per million opportunities. Whether referring to a product being assembled, data entry, or other processes, each Sigma calculates how many final products will be faulty.

What are the Baseline Sigmas?

In industry, there are Two, Three, Four, Five, and Six Sigma. Two Sigma, being the first level of Sigma has a defect-free calculation of 93.32%. Whereas, Six Sigma, the industry leader, had a defect-free calculation of 99.99966%. While both sigmas have a production success of over 90%, their difference can be seen on a mass scale. For example, when producing 1,000,000 products, Two Sigma will have approximately 933,200 that are detected. In other terms, that’s over 2,500 a day, and over 100 per hour. When “time is money”, you need the absolute best for error prevention.

While Six Sigma has the least chance of production error, achieving it is not as simple as some would assume. Individuals and employers devote years of their lives to learning, understanding, and implementing the methodology into their practices. Yet, success is only success if it can be measured correctly. If you’re an organization that has recently begun using Six Sigma in practice, you must calculate your Baseline Sigma.

How Do You Calculate Your Baseline Sigma?

Of course, the easiest and most straightforward way to measure your Baseline Sigma is to calculate the Defect Per Million Opportunity, DPMO. Because Six Sigma focuses on achieving 3.4 errors per million products, this should be your goal. Before you begin to measure your Baseline Sigma, you must first collect three sets of data. First, the number of products you produce. Second, the number of production errors per product. Third, the total number of production errors.