- Six Sigma Tutorial
- Six Sigma DMAIC process
- Six Sigma Acceptance Sampling
- Sampling Plan Variation vs Lot Size Variation in Acceptance Sampling
- AQL Based Sampling Plans
- Decision Tree for Selecting Type of Variables in Sampling Plan
- FMEA – Failure Mode and Effects Analysis
- Types Of FMEA: Design FMEA (DFMEA), Process FMEA (PFMEA)
- The FMEA Quality Lever – Where To Put The Effort
- FMEA Quiz
- Six Sigma Confidence Intervals
- Confidence Limits
- Confidence Interval Formulas
- Z Confidence Interval for Means – Example
- t Confidence Interval for a Variance – Example
- Six Sigma Defect Metrics – DPO, DPMO, PPM, DPU Conversion table
- Fishbone Diagram – Fishbone Analysis
- Cost of Quality Defects and Hidden Factory in Six Sigma
- Pareto Analysis using Pareto Chart
- Six Sigma Calculators – DPMO, DPM, Sample Size
- How to select a Six Sigma project? Download selection grid template.
- How to create Six Sigma Histogram? Download Excel template
- Scatter Plots – Free Six Sigma Scatter Plot template
- How to create, use Six Sigma SIPOC tool? Download SIPOC Template
- Quality Function Deployment (QFD) – Download free templates
- What is Decision Matrix or Decision Making Matrix ?
- The nature of Process Variation
- What is RACI or RASCI Matrix/Chart/Diagram? Download free templates
The nature of Process Variation
Understanding Process Variation thru examples
Let’s try to understand Process Variation using below examples. Take two shooters Joe and Max and understand their shooting. Joe on average is dead on whereas Max on average is way off, as shown below.
Who is more likely to be consistently on target over the long haul? How do we improve their shooting?
If Joe’s first shot were high, what might you be tempted to do with his site? Adjust it down, right?
But if his next shot would have been below the target due to his natural shooting variation, look at what would happen if you had adjusted the sight (see next picture).
The amount of process adjustment actually moves the second shot an equal amount further from where it would have been without the adjustment. This harmful and unnecessary adjusting is called tampering.
Who is a better bowler?
Lets take another example of two bowlers, Jane and Pat and see their bowling scores in the below picture. If X is the bowling score, then X (“x-bar”) is the average score.
Although Jane’s average score (x-bar) of 140 is lower than Pat’s, she is at least more consistent. When there is a lack of consistency between measured responses, there is less certainty (i.e., more risk) about what you can expect over time.
Another Example: Comfort of a Chef
In this example lets take a chef who works in his kitchen all day. On average, is the chef comfortable with the temperature?
If X is temperature, what is average temperature?
Avg(x) = (130 + 10) / 2 = 70
So the average doesn’t explain his discomfort. What would be a better measure?
The range, or difference between the largest and smallest values would be a better measure, indicating the amount of variation:
R = 130 – 10 = 120
That large of amount of variation explains why he is so uncomfortable.