Course Series: Design of Experiments Using JMP Software

Design of Experiments (DOE or DOX) is perhaps the most effective and efficient way to do research today. Engineers and scientists who engage in design of experiments will be able to advance the competitiveness of their organizations by discovering deeper insights in less time and at lower cost.

Most DOEs have one or more of the following objectives:

  • determine and fix which factor is causing problems
  • determine the range allowance of the process, especially in relation to control limits and specification limits (Sensitivity Testing)
  • determine which path to take in development
  • figure out how to reduce variation
  • try out proposed ideas and see if they lead to improvement
  • test settings for lower-cost factors
  • find ways to compensate for changes in one condition or material while maintaining the integrity of all other data

Standard Courses DOE 1 and DOE 2

Design of Experiments 1 Using JMP Software and Design of Experiments 2 Using JMP Software, which are described in more detail below, are geared to engineers and scientists. These courses are the most popular of our training courses on the subject of DOEs.

Alternative Course to DOE 2

A shorter and more focused alternative to Design of Experiments 2 Using JMP Software is the Design of Experiments for Batch Processes and High-Throughput Screening Using JMP Software course, which is described below.

 

Design of Experiments 1 Using JMP Software

This course provides a solid foundation on how to apply DOE using JMP software effectively in your research. The methods in this 2-day course will advance research and improve productivity considerably over informal methods.

Who Should Attend

Engineers, scientists, and technicians who are involved in characterizing, evaluating, and improving processes and equipment

Duration

2 days

Prerequisites

Data Analysis and Statistics Using JMP for Engineers and Scientists course, or equivalent experience in statistics and JMP software

Expected Results

After completing this course, participants will be able to:

  • design experiments to maximize efficiency and analytical power while being practical
  • develop a strategy for experimentation
  • apply the scientific method
  • design, analyze, and interpret screening experiments

 

Design of Experiments 2 Using JMP Software

This course extends beyond Design of Experiments 1 with JMP Software to focus on several very effective and advanced DOE techniques. Some of these advanced methods have been made widely available through JMP software over the past 10 years.

Does batch processing in a manufacturing environment pose a challenge to designing ordinary experiments? Did you discover that your response doesn’t increase or decrease in a straight line like all those classroom examples? Do you need some direction on what to do next?

This 2-day class provides an introduction to the practice of experimentation for process optimization. Topics include response surface designs, split-plot designs, strip-plot designs, and computer-aided (optimal) designs. Topics on analysis and interpretation integrate the use of JMP software to support analysis of variance and multiple linear regression analyses.

Who Should Attend

Engineers, scientists, and technicians who are involved in characterizing, evaluating, and improving processes and equipment

Duration

2 days

Prerequisites

Design of Experiments 1 Using JMP Software course, or equivalent experience in statistics and JMP software

Expected Results

After completing this course, participants will be able to:

  • implement the Path of Steepest Ascent method of designing experiments
  • design, analyze, and interpret experiments in response surface methodology
  • understand the complexity of doing experimental design in a batch-processing environment
  • understand the costs and benefits of performing an optimal experiment
  • conduct mixture design of experiments
  • understand and apply robust optimization

 

Design of Experiments for Batch Processes and High-Throughput Screening Using JMP Software

This training course is an alternative to Design of Experiments 2 Using JMP Software. It’s geared to engineers and scientists who prefer a shorter and more focused examination of batch processing designs.

In the semiconductor and pharmaceutical industries, batch processing is the norm. Batch sizes change from step to step within the process. A single unit in one step is only part of a unit in another step. Many measurements can often be made on what is essentially one object being measured. Traditional full and fractional factorial designs applied in these batch situations often lead to misleading results. Engineers limit the randomization of a traditional DOE design, being unaware that it is then impossible to get correct statistical tests out of the results.

The correct assumption is that batch processing requires batch-processing DOEs. Design the experiments with batch processing considerations from the start. The analysis will then change accordingly with the designs.

Course Description

This course is the next step after understanding basic full and fractional factorial designs. Does batch processing in a manufacturing environment make designing ordinary experiments a challenge? Are some factors hard to vary? For example, a hard-to-vary factor would be difficult to change randomly, as suggested by most experimental designs. Are you doing experiments repeatedly over multiple process steps?

Repeated instances of hard-to-vary factors or experiments over multiple process steps are strong indicators of batch processing. It is possible to correctly take into account hard-to-vary factors and multiple processing steps in the design and analysis of DOEs.

Who Should Attend

Engineers, scientists and technicians who will design and analyze batch-processing experiments

Duration

1 day

Prerequisites

Design of Experiments 1 Using JMP Software course, or equivalent experience in statistics and JMP software

Expected Results

After completing this course, participants will be able to:

  • understand the necessity of replication in batch processing
  • design split-plot designs
  • design basic strip-plot designs
  • design complex strip-plot designs
  • build correct models for strip-plot and split-plot designs
  • analyze strip-plot and split-plot designs