Designed to help those that are preparing to take the PMP or CAPM Certification Exam, each post within this series presents a comparison of common concepts that appear on the PMP and CAPM exams.
Many authors have studied the economic design of the control chart for a single assignable cause.
But, in practice, multiple assignable causes are more logical and realistic.
Identifying root causes is the key to preventing similar recurrences.
With a view to monitoring and controlling manufacturing processes in industries, control charts are widely used and needed to be designed economically to achieve minimum quality costs.
Common causes are poor light, humidity, vibration, poor food on cafeteria, absence of a real quality program, poor supervision, poor instruction, procedures not suited to the requirements, poor arrangements for comfort of workers.
They are faults of the system, so they usually stay there until they are removed by management.So if we really want to get rid of quality issues, the art of root causes analysis should be managed accordingly to be able to identify the real root causes, and determine if they are common (management to fix) or special (employee to fix).If you are a six sigma practitioner, you will do this in the analyze phase of your DMAIC.In many cases, they can be corrected on statistical signal by employee himself.Signals tell him weather to leave the process as it is or to take action.My recommendation is to work in teams to solve issues, inviting experts, managers and operators, depending on the severity of the problem, so as to make sure you have ideas from anyone involved, but considering your effort is worth the benefit (usually it is).A flip chart or a computer shared in a big screen will help you collecting the ideas and keeping record of them.Edwards Deming, as statistician, also would like to say there were two main kind of causes, common and special.Common or Environmental causes tend to be the 85% of the cases: are called common because affect equally all workers in a section.Moreover, the economic design does not consider statistical properties like bound on type I and type II error, and average time to signal (ATS).This paper focuses on evaluating the performance of genetic algorithm (GA) in pure economic and economic statistical design of the control chart for multiple assignable causes.