Globalization and digitalization have intensified competition in manufacturing. Some companies are using Bayesian hypothesis testing to optimize processes and make informed decisions. For example, a production manager could use it to improve the engine-cylinder-head-machining process.

Employing FMEA (Failure Modes and Effects Analysis) ensures ongoing enhancements in processes and products. By preemptively tackling potential challenges, it fortifies reliability, safety, and overall quality.

From Theodolites to Laser Trackers: A 40-Year Metrology Revolution. Witness how a technical field has evolved, enabling single-micron accuracy at your fingertips and powering smart factories for unprecedented efficiency.

Labor shortages continue to pressure manufacturers, with some dedicating up to 20% of their workforce to manual inspection. Embracing Quality 4.0 with automated in-line inspections and AI process analytics could provide significant value.

In many production settings, machine operators are tasked with monitoring processes by taking samples and making adjustments as needed to maintain productivity. However, there's a question worth exploring: What if ignoring these data-driven adjustments and allowing the process to continue unchanged is a better strategy?

In their upcoming session at The Quality Show South, Sophronia Ward, PhD and Mark Nash CSSMB, will discuss the best ways to use data, how to use software without letting it use you, and other ways to improve your manufacturing processes.

Explore top 5 ways Minitab Real-Time SPC can help improve quality and help Quality professionals save time and money

A leading aircraft manufacturer faces $27B in losses due to slow supply chain fixes, aggravated by escalating quality standards like AS9102 Rev C. The solution lies in seamless communication of requirements from buyers to suppliers, streamlining operations for all parties involved.

DOE is a method that helps manufacturers improve processes by understanding the relationship between factors and the output. It involves defining the problem, selecting the right design, conducting the experiment, analyzing the results, and implementing changes.