Statistical quality control of SQC for short is described by the text
1st:
Statistical quality control of SQC for short is described by the text as a set of statistical tools used by quality assurance professionals to evaluate an organizations production of quality. The quality control process is needed to evaluate either final products or processes that create. The reason statistical quality control has utility is because it is not feasible for a company to do one hundred percent verification of all outgoing products. In some cases the quality inspection can even be destructive for example quality test of breaking limits of chalk (Sharma, 2006). There are several advantages to statistical quality control to include reduction in costs, greater efficiency, and application ease, detection of faults and assurance of specifications (Sharma, 2006).
Even with the numerous advantages of statistical quality control there are limitations just like anything else. The main limitation is you are not getting a one hundred percent verification of goods or products. This is not feasible in every case but with statistical quality control you get a good sample but there is no way to know that one hundred percent of products leaving a facility are good to go.
‘Quality control is attained most efficiently, of course, not by the inspection operation itself but by getting at the causes’. Comment on the statement
I believe this statement is getting at process quality control or pinpointing shortfalls in a process that leads to degraded quality. If you are able to pinpoint shortfalls and execute change in order to rectify in the end you can not only ensure good products are going out but save on overall cost.
References
Sharma, J. K. (2006). Business Statistics (2nd ed.). Pearson India. https://learning-oreilly-com.ezproxy2.apus.edu/library/view/business-statistics-second/9789332503434/
Chandra, M.J. (2001). Statistical Quality Control (1st ed.). CRC Press. https://doi.org/10.1201/9781420038675
2nd:
American consumers have long become expectant of high-quality goods delivered at an affordable price. When we order something, we want it to work as advertised, look as advertised, and last as advertised. If we order more than one of the same items, we expect both ‘things’ to work identical, look identical, and last the same amount of time. This can only be accomplished through the application of quality control. Our text defines quality control as “a series of inspections and measurements that determine whether quality standards are being met” (Sharma, 2006). The purpose of this process is to establish standards of quality that are achievable and to establish control measures at all levels to implement a standard of quality. This can be measured by inspecting specific parameters like raw materials or products and services and comparing them to previously established standards (Sharma, 2006).
Statistical quality control (SQC) is an integral part of the quality control process that focuses on monitoring standards. This is done by taking measurements, identifying deficiencies, and implementing recommended changes into the production process to correct the problem that was identified. SQC is a simple and effective system that uses statistics to measure the extent of the quality of products being produced without inspecting every individual item and to coordinate improvement efforts for the maintenance and production quality of various groups within an organization (Sharma, 2006). Although SQC is a proven method to manage quality control, it is not without fault. Because of the nature of statistical quality control, there are occasional outliers that are missed due to the random sampling of the production cycle. These outliers are the ‘ones that got away so to speak’. An example of what I mean by this is the occasional item that you buy at the store that is defective once you get it home and open it. This is not the norm, but it still happens to me more often than I care to admit.
Regarding the statement ‘Quality control is attained most efficiently, of course, not by the inspection operation itself but by getting at the causes’, I feel that this most assuredly is in reference to finding the root cause of an error and applying the fix at this basic level. This in turn allows an organization to self-correct which increases efficiency in production costs and production volume which has a positive effect on the profitability of a company.
An example of this would be an assembly line for an automaker that is producing door panels. The quality control process should identify door panels that are out of parameter, but if the QC team does not identify which machine setting is wrong and take corrective action to stop producing door panels that will not fit the specific vehicle they are made for, then a lot of money is wasted. This would include raw materials, man-hours, equipment hours, and even the electricity to maintain the production line for that period. But by shutting down the production line and fixing the mechanical or technical inputs that are causing the error in the first place, the production of door panels that meet the quality control standards established can then be produced in volume.
References:
Sharma, J. K. (2006). Business Statistics (Vol. 2nd). Pearson India. Retrieved from https://learning-oreilly-com.ezproxy1.apus.edu/library/view/business-statistics-second/9789332503434/xhtml/chapter018.xhtml#head18.2.1
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