Minitab Training on DOE in Practice

DOE in Practice

Acquire proficiency in addressing typical scenarios encountered in Design of Experiments (DOE), necessitating adaptations to the analysis of classical factorial and response surface designs due to unique attributes of the response variable or the data collection process. This course in minitab training equips you with techniques tailored to common experimental contexts often confronted in real-world applications, encompassing challenges like missing data and factors that are arduous to manipulate. Additionally, you will develop the expertise to factor in variables (covariates) that exert an influence on the response but remain beyond experimental control.

Dive into the realm of cost and variability reduction, while concurrently optimizing pivotal attributes of a significant product or process facet. Learn the art of identifying and quantifying the impact of factors on the likelihood of critical events, such as defects, transpiring. This course in minitab training empowers you to navigate intricate DOE situations, enabling effective decision-making and facilitating the attainment of optimal outcomes in practical settings.

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Topics Included:

Investigate the effect of a noise factors or covariates on the response

  • Utilize the signal-to-noise ratio (S/N ratio) to identify control factor configurations that minimize noise-induced variability. Minitab computes the S/N ratio for every control factor combination and subsequently calculates the mean S/N ratio for each control factor level. There are four S/N ratios to select from, based on the experimental objective and a comprehension of the desired process outcome.

Create and run a design with hard-to-change factors

A hard-to-modify factor refers to a variable that proves challenging to randomize comprehensively due to constraints like time or cost constraints.

  • Designs involving hard-to-change and easy-to-change factors utilize distinct sizes of experimental units.
  • Hard-to-change factors are allocated to large experimental units.
  • Within these large units, small experimental units are employed to investigate the easy-to-change factors.

Optimize responses while minimizing cost or variability

  • Cost optimization finds a balance between cost minimization and response optimization. For cost optimization, it's essential to have the capacity to measure or compute the cost for each treatment combination within the experiment. In Minitab, when analyzing your design, designate the cost column as the response.

Analyze a DOE with a binary response

Employ "Analyze Binary Response" to evaluate a designed experiment featuring a binary response. This analysis is applicable to three distinct types of factorial designs that incorporate a binary response.

  • 2-level factorial design
  • General full factorial design
  • Plackett-Burman design