GMORS

Design of Experiments (DOE) is a well-known tool for problem solving. Generally known as Classical Design of Experiments (DOE) and Taguchi DOE (Taguchi DOE), these two methodologies are effective in solving general problems, but there are limitations in solving complex problems. When there are multiple variables, interactions and long-term or chronic manufacturing and technical problems, it may take dozens or even hundreds of trials and ultimately, satisfactory results may not be obtained.

Customers often raise product problems, defects and challenging issues in production. Our company discusses and resolves them together, and works toward solutions in order to have good product quality and stable manufacturing process. In the process of solving problems, it is often necessary to do experimental designs. Faced with high quality products, the quality and application requirements are such that the level of difficulty in production increases along with the quality requirements. When using classical design of experiment (DOE) and Taguchi DOE tools to solve complex and difficult problems, the key variables often interact with each other and it takes a relatively large number of trials over a long duration to produce limited results.

Shainin DOE

• Systematic 10-step method for problem solving
• Finding the key "X" in multivariate analysis
• Easy and smooth exchange variable search
• Paired comparison and Tukey test
• The best technique to quantify the impact of each interaction
• Process confirmation, correct control and pre-control.

GMORS adopts the Shainin Design of Experiments (Shainin DOE) method to objectively diagnose the possible factors in the variation of product quality by using various multivariate maps (hierarchical methods, etc.) to identify the input factor that affects the process variation problem the most, which is termed as Red X. Changes are made to the experimental design and the level of each factor, reducing the number of experiments and finding the root cause in an effective and economical process.

GMORS can efficiently analyze the combined effects of each interaction in a short period of time with lower cost and complexity, and through simple methodology verify the effectiveness of each process to produce significant differences before and after improvement.