Ability and knowledge to participate in completing the FMEA Process
Ability and knowledge to lead a team in the FMEA Process
Ability to teach and mentor Participants and Team Leads in the FMEA Process
Ability to lead an organization implementing FMEA
DOE Training Program
Design of Experiments (DOE)
|ON SITE||OFF SITE||ONLINE|
“Design of Experiments” is a 16-hour activity-based, interactive workshop to showcase the power and utility of this statistical tool while teaching how to plan and analyze an experiment. It is also to dispel the conception that DOE is reserved only to those with advanced math training. Participants can expect to be fully engaged while learning and applying principles, tools, and techniques for completing innovative DOE projects. Participants are expected to pass a quiz and complete a work-related project as part of this course.
What is DOE?
A structured method used to determine the relationship between independent factors (X’s) and the output/response (Y) (objective is to find the effect of factors (X) on the response (Y).
Why use DOE?
⦁ To allow for the simultaneous study of the effects that several factors may have on a process
⦁ To maximize information obtained from a given number of simulation runs
⦁ To determine if there is interaction between factors
When to use DOE?
⦁ When investigating or characterizing a process or product variables that influence product performance
Desired Results of Design of Experiments include:
⦁ Problem Solving
-Eliminate defective products or services
-Reduce cycle time of handling transactional processes
-Mathematical model is desired to move the process response
-Opportunity to meet differing customer requirements (specifications or VOC)
⦁ Robust Design
-Provide consistent process or product performance
-Desensitize the output response(s) to input variable changes including NOISE variables
-Design processes knowing which input variables are difficult to maintain.
-Past process data is limited, or statistical conclusions prevented good narrowing of critical factors in analyze phase.
To provide participants the skills and abilities to increase customer satisfaction, reduce cost, increase quality, improve reliability, improve speed, and make intended processes more robust, effective, and efficient. At the end of this course you should be able to:
⦁ Define how to evaluate which process inputs have a significant impact
⦁ Define the “target level” of defined inputs
⦁ Define what “Histogram” means
⦁ Define “Statistical Process Control (SPC)” and when it is used in the DOE process
⦁ Define “Regression and Correlation Analysis”
⦁ Define what a “Factor” is within the DOE process
⦁ Define what a “Level” is within the DOE process
⦁ Define what a “Response” is within the DOE process
⦁ Define what a “Significant Input” is within the DOE process
⦁ Define what “Reducing Variability” is within the DOE process
⦁ Define the difference between factors, levels and structure within a DOE
⦁ Define “Unexplained Variation”
⦁ Define “Noise Factors”
⦁ Define what “Correlation” means within the DOE process
⦁ List the Experimental Design Process Steps
⦁ Define “One-factor Experiment”
⦁ Define the use of the F-test
⦁ Define “Multi-factor Test”
⦁ Define a 22 full factorial experiment using the 8-steps for analysis of effects
⦁ Develop and implement a DOE plan for a work-related process
This training will include an introduction to Minitab statistical software. The training will follow the 9 phases defined in the process flow: Introduction; Preparation; DOE Components; DOE Purpose; Design Guidelines; Design Process; One-factor Experiments; Multi-factor Experiments; and Taguchi Methods.
|Step One: Introduction||
⦁ Definition of DOE
|Step Two: Preparation||
⦁ Toolbox Review
|Step Three: DOE Components||
|Step Four: DOE Purpose||
⦁ Compare Alternatives
|Step Five: Design Guidelines||
⦁ Error Sources
|Step Six: Design Process||
⦁ Define Problem
|Step Seven: One-factor Experiments||
⦁ SPC Chart Method
|Step Eight: Multi-factor Experiments||
⦁ Full Factorial Experiment
|Step Nine: Taguchi Methods||
⦁ Robust Design
The prerequisites for this workshop are familiarity with the concepts of process stability, basic statistical process control (SPC), and measurement systems analysis (MSA). It is recommended that the participants have access to Minitab statistical software throughout training.
COURSE RECOMMENDED PARTICIPANTS
A recommended DOE course candidate is someone with at least two years of work experience who wants to build or demonstrate his or her knowledge of contemporary quality tools and processes.
This DOE training program is intended for all industries including but not limited to:
Medical (Including Hospitals)
Tooling and Equipment
Oil and Gas
Trains & Railroad
Phones & Personal Devices
ReliaTrain offers mentoring, facilitation, and training support for Product and Process Development based on Quality and Reliability industry standards. ReliaTrain provides step-by-step methodologies that can be tailored to your organizational requirements. Let ReliaTrain experts support implementation of DOE methodology at your company.