Design of Experiments (DoE) is a set of statistical and engineering methods that allows for the planning, execution, and analysis of experiments in a way that maximizes the amount of knowledge gained while minimizing testing efforts. See how DoE can be used to optimize production processes.

Nonconforming product management is a daily reality in manufacturing. You can’t always prevent defects — but you can learn how to manage them so they don’t become a problem that impacts the customer, damages your company’s reputation, or brings production to a halt. Discover real-life scenarios and common mistakes that still occur in manufacturing plants — and how to avoid them.

In the automotive industry, every production stoppage, complaint, or delivery delay can cost hundreds of thousands – which is why effective supplier management is no longer optional, but essential. If you want to better understand how to build strong and stable supplier relationships, minimize risks, and meet growing customer and industry quality requirements, this article is for you.

In the world of quality control, data drives decisions. But raw numbers are often hard to interpret – and that's where the histogram comes in. This seemingly simple chart can reveal valuable insights and help engineers make informed decisions.

Artificial intelligence is revolutionizing many industries, including quality in the automotive sector. ChatGPT for Quality Engineers is a valuable tool that supports daily tasks—from creating documentation to conducting problem analysis. What are its capabilities, what are its limitations, and how can it be used effectively? Find out in this article.

Process audit? Sounds serious, but it doesn’t have to be! In reality, it’s simply about checking if everything works the way it should — and finding opportunities for improvement. With the right mindset (and a good checklist), an audit can become a powerful tool to streamline processes and boost efficiency in your company.

In the chaos of modern life, where everyone struggles with an overwhelming number of tasks, the Eisenhower Matrix emerges as an essential ally in the pursuit of efficiency and effectiveness. Why? Because it provides a clear strategy for task prioritization.

Both during a new launch and ongoing production, manufactured products must meet the required quality and quantity standards. If these requirements are not met, customer escalation may occur. The most serious form of escalation is the New Business Hold (NBH).

Artificial intelligence (AI) is playing an increasingly crucial role in Quality Management and Engineering with forecasts predicting it will revolutionize most quality-related processes in the coming years. For quality engineers, AI is becoming a tool that facilitates data processing, reduces execution time for routine tasks and supports better decision-making.

Every new launch and implementation of changes in ongoing production involves the validation and verification of process parameters. One tool worth considering in this context is a Process Parameters Audit.