Statistical Process Control (SPC) is evolving to not just detect defects, but also to predict and prevent issues. Modern factories use more sensors and collect more data, allowing SPC to analyze real-time patterns and forecast potential issues.
Design of Experiments (DOE) helps improve products and processes more efficiently, providing a comprehensive understanding of influences on the end result.
Process mapping is a method to visualize and understand manufacturing processes, similar to a flowchart. It helps identify inefficiencies and delays in the workflow. For example, it can pinpoint the source of delays in an assembly line.
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.
Data visualization transforms complex data into clear insights through charts and graphs, helping users quickly spot and address production issues and make informed decisions.
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.
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