|
| | DOE BASIC
INTRODUCTION
 | Do you believe that quality is not achieved by trial-and-error but by being
designed-in? |
| Do you use one-at-a-time factor tweaking to improve quality? |
| Do you blame your operators for poor quality? |
| Do have low Process and Product yields? |
Design of Experiments (DOE Basic) emphasizes the 2-Step Optimization of first reducing variation,
and then, adjusting to target performance in processes and products. This
course is designed on a take-back-and-do approach. The course keeps statistics
to the minimum and practical aspects to the maximum so that even engineers
with little mathematics can understand. Delegates should see immediate
applications to improve quality and reduce loss.
BENEFITS OF ATTENDING THE
COURSE
The course enables the delegates to:
| Understand and use simple methods to establish quality loss. |
| Design simple experiments and analyze data through the 2-Step Optimization. |
| Improve processes by first reducing variability and then adjusting to target. |
| Perform confirmation experiment and calculate quality improvement. |
WHO SHOULD ATTEND
DOE Basic is particularly useful for those involved in controlling
process or product parameters. It will be most appropriate for those involved
in Design, Quality, R&D, Reliability, Maintenance, Engineering, Manufacturing
and Production. Teams are encouraged to attend for maximum benefit.
BRIEF COURSE OUTLINE
| Day 1 AM |
Day 1 PM |
Quality Loss Evaluation
| Quality loss function |
| Nominal-the-best |
| Smaller-the-better |
| Larger-the-better |
|
Simple Experiments
| Simple calculations |
| Response Table |
| Response Graph |
| Prediction |
| Basis for further work |
|
| Day 2 AM |
Day 2 PM |
Basics of Experimentation
| Designing experiments |
| Conducting experiments |
| Performance analyses |
| Verifying experiments |
| Attribute analysis |
|
Robust Quality
| Factors |
| Conducting experiments |
| Additively, Interactions |
| Linear Graphs |
|
| Day 3 AM |
Day 3 PM |
Practical Experimentation
| Aim, Objective |
| Brainstorming |
| Designing experiment |
| Helicopter experiment |
|
Course conclusion
| Minimize Variation |
| Achieve Target |
| Cost-Gain calculations |
| Confirm results |
| Management report |
|
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