Assessment of Process Capability Using Weibull Analysis

For a production process, meeting the customer's specification is important.
Thus process capability should assist the company to overview their manufacturing
processes. In order to maintain the satisfaction of the customer, production has to
achieve a normal distribution for their production output, where the production itself
has to be in between the limit that the customer set. With this pattern, process
capability can be find using normal process capability indices. These normal process
capability indices however cannot be applied to non-normal distribution production data.
Thus, in a normal approach assessing a non-normal distribution production data is to
convert the distribution to be a normal distribution using specific variables as stated by
Peter J. Sherman. With this method, process capability indices for normal distribution
now can be use for the non-normal distribution of production data.
Besides using the method of transforming the non-normal distribution, a new
process capability index Spmk is established, this process capability index can straight
away finding the process capability for non-normal distribution without having to
convert it first. This study will use Weibull distribution to find a process capability on
one set of production data. Process capability able to identifY whether the
manufacturing process is at the top of the games or some improvement can be done to
improve the production rate. After knowing the process capability, further step can be
taking for example applying Six Sigma into the manufacturing processes. Six sigma
help company to improve the quality of process output by eliminate the causes of
production error or defects and minimizing the variable in manufacturing process to
deliver near-perfect products to the customer. Finding a process capability is essential
for the company to maximize profit by increasing the process capability. Without
careful measurement of process capability, company will end up losing more money
unwittingly.