In order to do business, we have to use predictions of various sorts. These range from seat-of-the-pants guesses to the results of sophisticated modeling. In every case, the key to effective forecasting is to understand the uncertainty in the forecast.

With the publication of the new VDA AIAG SPC Manual in July 2026, one of the most significant changes to statistical process control (SPC) in decades has been introduced.

Some of my colleagues have recently been pushing multivariate charts. These charts combine several different process outcome values into a single number that’s placed on a chart.

Walter Shewhart’s average and range chart had been around for more than a decade when, in 1941, W. J. Jennett came up with the chart for individual values and moving ranges (an XmR chart).

The Weibull distribution is a continuous probability distribution often used in reliability analysis to model time-to-failure and product lifespans.

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.

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.

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.

Manufacturers can’t control tariffs, supply chain volatility, labor shortages, or geopolitical instability. But they can manage operational efficiency.

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.