Quality Management for Organizational Excellence
Name
University of Phoenix
MGT 449

Quality Management for Organizational Excellence “Quality Textiles International is an organization that weaves fabric and prepares the required sheet lengths as desired by customers. They have received recent complaints the fabric weight, measured in ounces per square inch, is not meeting customer requirements, which are very close to the control limits. Quality Textiles International’s quality assurance department checks the fabric weight on a regular basis and knows their process for fabric weight has a mean of 2.73 oz/in2 and a standard deviation of 0.31” (University of Phoenix, n.d., para. 1).
1.0 Histogram A graphical representation, which shows a visual impression and illustration of the data is known as a histogram. The plotted data and the values are arranged by the histogram in a regular manner like a graph. A histogram is quite easy to use and understand and by using a histogram representing difficult and lengthy calculations can be summarized very easily and accurately. A histogram is quite easy to read as well and requires little time to understand the histogram.
2.0 When should a person use a Histogram? When a person is uncertain what to do with a large set of measurements offered in a table, a person can use a histogram to categorize and show the data in a more user friendly layout. A histogram will make it simple to see wherever the bulk of values fall in a measurement scale and how much difference there is.
2.1 Summarize large data sets graphically:
When summarizing of large data sheets is required.
2.2 Compare process results with specification limits:
If a person includes the process specification limits to his or her histogram, a person can verify promptly whether the present method could create "good" products.
Specification limits may take the structure of length, weight, density, and quantity of materials distributed or what is significant for the product of a given method.
2.3 Communicate information graphically:
The team members can without doubt see the most recurring values. When people utilize a histogram to review large data sets, or to weigh against measurements to specification limits, a person is making use of a potent tool for communicating information.
2.0 Histogram in this scenario According to the histogram plotted here three points comes out to be those out of control or just touching the boundary lines of either upper control limit (UCL) or lower control limit (LCL), as compared to the given upper control limit (UCL) and lower control limit (LCL). The upper control limit (UCL) gives the maximum value for the given data and the lower control limits (LCL) gives the minimum value present in the given data. Three cases presented in the histogram where the upper control limit (UCL) or the lower control limit (LCL) has been crossed or just touched by the given values are tabulated as follows:
| lower | |upper | | | frequency |
|1.49 |< |1.80 | | |0 |
|3.66 |< |3.97 | | |1 |
|3.97 |< |4.28 | | |4 |

The values when plotted on the histogram, just touches either the lower control limit (LCL) or the upper control limit (UCL) and hence it is said these values are the out of control values. The upper control limit (UCL) in this scenario comes out to be 4.28 whereas, on the other hand, the lower control limit (LCL) comes out to be 1.49.
Reference
University of Phoenix. (n.d.). Quality Textiles International. Retrieved from University of Phoenix, MGT 449 Version 7 website.