# How to Calculate Process Sigma

### How to Calculate Process Sigma

Consider a power company for illustration purposes: A power company measures their performance in uptime of available power to their grid. Here is the five-step process to calculate your process sigma.

### Step 1: Define Your Opportunities

An opportunity is the lowest defect noticeable by a customer. This definition, of course, is debatable within the Six Sigma community. Here’s a useful snippet from the forum discussing this point:

“Typically, most products (and services) have more than one opportunity of going wrong. For example, it is estimated than in electronics assembly a diode could have the following opportunities for error: 1) wrong diode and 2) wrong polarity (inserted backwards), so for each assembly shipped, at least two defect opportunities could be assigned for each diode. Apparently, some manufacturers of large complex equipment with many components prefer to [count two opportunities in this case]. My point is that this approach dilutes Six Sigma metrics.” -Anonymous

Handpicked Content:   Customer CTQs – Defining Defect, Unit and Opportunity

Many Six Sigma professionals support the counter point. I always like to think back to the pioneer of Six Sigma, Motorola. They built pagers that did not require testing prior to shipment to the customer. Their process sigma was around six, meaning that only approximately 3.4 pagers out of a million shipped did not function properly when the customer received it. The customer does not care if the diode is backwards or is missing, just that the pager works.

Returning to our power company example, an opportunity was defined as a minute of uptime. That was the lowest (shortest) time period that was noticeable by a customer.

### Step 2: Define Your Defects

Defining what a defect is to your customer is not easy either. You need to first communicate with your customer through focus groups, surveys, or other voice of the customer tools. To Motorola pager customers, a defect was defined as a pager that did not function properly.

Returning to our power company example, a defect is defined by the customer as one minute of no power. An additional defect would be noticed for every minute that elapsed where the customer didn’t have power available.

Handpicked Content:   How Are Defects and Opportunities Determined for CTQs?

### Step 3: Measure Your Opportunities and Defects

Now that you have clear definitions of what an opportunity and defect are, you can measure them. The power company example is relatively straight forward, but sometimes you may need to set up a formal data collection plan and organize the process of data collection.

Returning to our power company example, here is the data we collected:
Opportunities (last year): 525,600 minutes
Defects (last year): 500 minutes

### Step 4: Calculate Your Yield

The process yield is calculated by subtracting the total number of defects from the total number of opportunities, dividing by the total number of opportunities, and finally multiplying the result by 100.

Returning to our power company example, the yield would be calculated as: ((525,600 – 500) / 525,600) * 100 = 99.90%

Handpicked Content:   Process Sigma Calculator

Alternatively, the yield can be calculated for you by using the iSixSigma Process Sigma Calculator – just input your process opportunities and defects.

### Step 5: Look Up Process Sigma

The final step (if not using the iSixSigma Process Sigma Calculator) is to look up your sigma on a sigma conversion table, using your process yield calculated in Step 4.

### Assumptions

No analysis would be complete without properly noting the assumptions made. In the above table, we have assumed that the standard sigma shift of 1.5 is appropriate (the process sigma calculator allows you to specify another value), the data is normally distributed, and the process is stable. In addition, the calculations are made with using one-tail values of the normal distribution.

##### Zack Swinney

1. Angela jones

Thank you for making this information easy to umferstand

2. suresh kumar

nice to understand

3. Kathryn Saab

This was really helpful! Very well written!

4. robert smith

Who created the sigma conversion tables? What is the origin? My understanding is that they were created from empirical data over time,

5. Sandeep

Simple and detailed

6. Fausto Galetto

It is said:
“Returning to our power company example, the yield would be calculated as: ((525,600 – 500) / 525,600) * 100 = 99.90%”
IS the ratio of TWO Random Variables (times!!!) normally distributed?
525,600 determination of the Total Time (RV in a year)
500 determination of the Down Time (RV)