Quality improvement

"The devil is in the detail"

quality-improvementYou achieve quality when you control the important details. When improving quality it is important to consider all the details. However, some details are more important than others. If you can select the details that matter the most and focus on them you will achieve quality. This applies to athletes, manufacturing companies and everyone else. I work mostly with manufacturing companies and use tools within Six Sigma to improve quality. Six Sigma has much in common with Lean, meaning customer focus, process understanding and focus on measurements rather than intuition. A challenge when it comes to quality improvement can be to identify the critical factors and then to control them.

 

What is a critical factor? For an athlete, this may be sufficient sleep and rest, proper food at the right time, or repeating a detail you need to improve. For a baker yeast, temperature and time can be most important to control. In a relationship, it may be to accept each other for who you are that is the critical factor :-) How can you identify critical factors? Often, it is enough to use common sense. Other times it can be necessary to combine common sense with statistical tools. Six Sigma use Process mapping to investigate a process systematically, and identify factors that affect the outcome of each process step. Afterwards, statistical tools can determine how much the various factors contribute to the overall variation of product quality. Components of Variation (CoV), multivariate analysis and experimental design (DOE) can be used for this purpose.

How can you control a critical factor? First, you must have the correct measurement. Measuring System Analysis - MSA can be used to determine measurement uncertainty. You can get my video with a measurement system analysis. Next, you can decide the normal variation by using statistical process control - SPC. I made a video about statistical process control. You can address the process capability / robustness by comparing the normal variation with the customer requirements. Are the control limits (representing normal variation) within the specification limits? The greater the distance / margin between these limits are, the more robust process. If the margins are too small: what can be done to reduce the normal variation? What technology is available? If humidity is a critical factor, it can be controlled by installing air conditioner.

 

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