Prima Strategies

"We work with organizations to achieve operational and process excellence"

Design Of Experiment (DOE Factorial)


This course is designed to give DOE Factorial a new outlook – a breakthrough tool and yet practical in most operation. Unlike the normal experiment practices such as trial and error and testing one factor at a time (OFAT), DOE deploys intelligent technique that saves a lot of time. With only a few systematic steps it allows us to have outside the box result (innovation).


General engineering experiments can be categorized into two parts – historical data analysis (known as passive experiments) and design of experiments or DOE (known as active experiments). The historical data analysis is not actually an experiment, but rather a use of Multiple Regression technique on historical data to create potential and wonderful model.


DOE consists of purposeful changes of the inputs (factors) to a process (or activity) in order to observe changes in the outputs (responses). The process (or activity) is defined as some combination of machines, materials, methods, people, environment, and measurement which, when used together, perform a service, produce a product, or complete a task. Thus experimental design is a scientific approach which allows the experimenters to gain knowledge in order to better understand the process characteristic and to determine how the inputs make and affect the response(s).



Upon completion of this training, participants will acquire the knowledge and skills to carry out systematic experiments in their process. The participants shall be able to:-


  • Identify opportunities for DOE experimentation
  • Select good candidates for the C, N, X factors
  • Develop IPO charts prior to doing experiments
  • Choose the Lo and Hi setting of the input variables
  • Select appropriate design matrix for the experiment to be done
  • Carry out the experiment properly
  • Measure the response or output characteristics
  • Perform the statistical analysis of the experiment
  • Making statistical conclusion out the experiment



  1. Product development managers, engineers, officers
  2. Production control engineers, officers
  3. Process owners
  4. Staffs who are looking for innovation technique in their work (manufacturing or services)



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