In last month’s column, we looked at how process-hyphen-control algorithms work with a process that is subject to occasional upsets.

Today’s manufacturing systems have become more automated, data-driven, and sophisticated than ever before.

Over the past decade, one of the biggest advances in enterprise resource planning (ERP) has been the ability to communicate and integrate with machines and external software programs to lower costs and increase efficiency.

Imagine a manufacturing world where machines seamlessly collaborate with artificial intelligence (AI) to ensure flawless quality inspection. It’s a future that holds immense potential for revolutionizing the industry.

Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation.

You’re in an early-stage hardware startup or a tinkerer in a toolshed with a product design set to shake up the market. Not sure how to turn your idea into a product? Here’s a step-by-step guide to the product development journey.

I’ll admit it: After five decades watching U.S. companies turning to simplistic accounting tricks to remain profitable, I’m discouraged.

Many articles and some textbooks describe process behavior charts as a manual technique for keeping a process on target.

The difference between common (or random) cause and special (or assignable) cause variation is the foundation of statistical process control (SPC).

Meetings give me a rash. A really bad one. One that not even calamine lotion can soothe. The only things worse than meetings are reports. Standard daily reports, weekly reports, hourly reports. Reports on the status of reports.