Visualizing Weibull Distribution Parameters Confidence Intervals
The Weibull distribution is a continuous probability distribution often used in reliability analysis to model time-to-failure and product lifespans.
Exponentially Weighted Moving Average Charts
Many erroneously think of a process behavior chart as a technique for monitoring a process. Consequently, they consider other monitoring techniques as alternatives to a process behavior chart.
Some Hazardous Ideas
In the 1980s, demand for SPC classes outstripped the supply of competent instructors. Novices were teaching neophytes, and misinformation could be found everywhere. Out of this chaos, many incorrect ideas about process behavior charts became widely circulated.
When Things Go Wrong
The way we think about our process will shape the way we collect, analyze, and interpret our data when things go wrong. This in turn will shape the actions taken and the results obtained.
The Expanding Role of Design of Experiments in Modern Manufacturing
Manufacturers can’t control tariffs, supply chain volatility, labor shortages, or geopolitical instability. But they can manage operational efficiency.
The Problem With Sigma Levels
Many practitioners have been taught to describe a process using sigma levels. Yet these levels are commonly misinterpreted. This article will help you to understand the problem and learn more appropriate ways of describing your process.
Making Sense of Observational Data
Most data in business and industry belong to the category known as observational data. These data are the voice of your processes because they are the result of ordinary operations rather than an experiment.
Capability and the Pooled Variance Statistic
Performance indexes use the global standard deviation statistic to describe the past. Capability indexes use a within-subgroup measure of dispersion to characterize the process potential. However, some within-subgroup measures are better than others.
Find the Signals by Filtering Out the Noise
One of the principles for understanding data is that while some data contain signals, all data contain noise. Therefore, before you can detect the signals you’ll have to filter out the noise. This act of filtration is the essence of all data analysis techniques.
But the Limits Are Too Wide!
When administrative and managerial data are placed on an XmR chart, the first reaction will frequently be that the limits are far too wide: “We have to react before we get to that limit.”