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.
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.
A new method to manage quality must not only improve quality, it must also address areas of waste. If we could detect defects even earlier in the process and given that increasing the number of quality gates is untenable, what else can we do?
In the quality management domain, AI undoubtedly has potential in different areas. It would be easy to think that AI could be a threat to less modern tools like statistical process control or render SPC obsolete.
Regression analysis helps quality teams improve their process standards. In simple terms, it helps these teams understand how variations in the manufacturing process affect the quality of the final product.
Advancements in technology are making SPC software more user-friendly and accessible. New features such as machine learning and predictive analytics are being added to SPC software, making it even more indispensable to an organization.
Accurate quality assessment is crucial for manufacturers aiming to deliver products that meet or exceed customer expectations. However, ensuring precise measurements within a manufacturing process can be challenging.