Discussion: Quality Control in Industry

Discussion: Quality Control in Industry

Initial Disc _ 2paragraphs and 2 replies ( 1-2 paragraph each)

Control charts are monitoring schemes, widely used in operations and manufacturing environments, to determine when a process is ?in-control? and in the presence of only common-cause variation. Give an example of an application of control charts in an industrial, operations, or manufacturing setting that is different from those supplied in the overview. Discuss and share this information with your classmates.

In responding to your peers? posts, select responses that use a control chart that is different from your own. Is the process in control or out of control? Would the process be in control if Western Electric rules are considered? Explain your reasoning. Finally, consider how control charts can be applied to the final project case study.

Support your initial posts and response posts with scholarly sources cited in APA style.

1. Please Reply to the 2 post below ? 1-2 paragraph:
Control chart, also known as a Shewhart chart is a time plot measure of quality utilized to identify causes of variation (Sharpe, et al, 2015). In other words, a control chart enables data to be observed over time and leveraged for business action enabling quality improvement (Sharpe, et al, 2015).

An example of an application of control charts in a manufacturing setting would be if a car manufacturer wants to control the mean diameter of a car engine piston. An X-bar chart could be generated utilizing sample diameter measurements taken from car engine pistons being utilized for engine construction. The X-bar would plot the sample means in order to control the mean diameter of the car engine piston samples. Variations such as common-cause variation could be identified. Common-cause variation is the result of random fluctuations in the manufacturing process (Sharpe, et al, 2015). In the car engine piston scenario, common-cause variation could be a simple variation in sample diameter based on the engine piston manufacturing process. Out of control or special-cause variation could also result from the manufacturing process if quality is concentrated in short-term focus. The car manufacturer needs to make sure that a system is in place to address special-cause variation. As Sharpe, et al, states (2015), a system of analysis needs to be in place for continuous quality control.

References
Sharpe, N. R., De Veaux, R. D., & Velleman, P., F. (2015). Business Statistics (3rd ed.). Upper Saddle River, NJ. Pearson Education.

REPLY ?

Quality charts are a very useful tool in the industrial and production environments, since they can definitely help companies control waste and cost by keeping track of the specifications of the product being created, and ensuring that they are the proper measurements to meet their needs. Having products created on the line that are way out of spec introduce a lot of waste since the products will probably have to be thrown out or melted back down to start again.
Companies can find out the outliers in a data group by keeping measurements on an hourly basis on the specifications of a piston that is being produced on a production line. For instance, if a company knows that they can put out 100 pistons an hour from the output of a production line, they would try and measure 1 or 2 per hour and place the measurements into a spreadsheet or database, and see what kind of variation is taking place over a period of time. Are the greatest variations coming during the night shift? Day shift? Or is there some other trend that can be seen that can be illustrated? By knowing where the problem lies, it is much easier to try and fix the problem so that it doesn’t happen again.

There are many types and approaches to using control charts, and each one has a specific use that can target a unique industry or production company. It is very important to be able to know just how closely the target specs are being kept in order to stay in business.

“The general approach to on-line quality control is straightforward: We simply extract samples of a certain size from the ongoing production process. We then produce line charts of the variability in those samples, and consider their closeness to target specifications. If a trend emerges in those lines, or if samples fall outside pre-specified limits, then we declare the process to be out of control and take action to find the cause of the problem.” (UTA.edu, 2016, p.1)

UTA.edu. (2016). Quality Control Charts. Retrieved from http://www.uta.edu/faculty/sawasthi/Statistics/stquacon.html/.

Control charts are monitoring schemes, widely used in operations and manufacturing environments, to determine when a process is ?in-control? and in the presence of only common-cause variation. Give an example of an application of control charts in an industrial, operations, or manufacturing setting that is different from those supplied in the overview. Discuss and share this information with your classmates.

In responding to your peers? posts, select responses that use a control chart that is different from your own. Is the process in control or out of control? Would the process be in control if Western Electric rules are considered? Explain your reasoning. Finally, consider how control charts can be applied to the final project case study.

Support your initial posts and response posts with scholarly sources cited in APA style.

1. Please Reply to the 2 post below ? 1-2 paragraph:
Control chart, also known as a Shewhart chart is a time plot measure of quality utilized to identify causes of variation (Sharpe, et al, 2015). In other words, a control chart enables data to be observed over time and leveraged for business action enabling quality improvement (Sharpe, et al, 2015).

An example of an application of control charts in a manufacturing setting would be if a car manufacturer wants to control the mean diameter of a car engine piston. An X-bar chart could be generated utilizing sample diameter measurements taken from car engine pistons being utilized for engine construction. The X-bar would plot the sample means in order to control the mean diameter of the car engine piston samples. Variations such as common-cause variation could be identified. Common-cause variation is the result of random fluctuations in the manufacturing process (Sharpe, et al, 2015). In the car engine piston scenario, common-cause variation could be a simple variation in sample diameter based on the engine piston manufacturing process. Out of control or special-cause variation could also result from the manufacturing process if quality is concentrated in short-term focus. The car manufacturer needs to make sure that a system is in place to address special-cause variation. As Sharpe, et al, states (2015), a system of analysis needs to be in place for continuous quality control.

References
Sharpe, N. R., De Veaux, R. D., & Velleman, P., F. (2015). Business Statistics (3rd ed.). Upper Saddle River, NJ. Pearson Education.

REPLY ?

Quality charts are a very useful tool in the industrial and production environments, since they can definitely help companies control waste and cost by keeping track of the specifications of the product being created, and ensuring that they are the proper measurements to meet their needs. Having products created on the line that are way out of spec introduce a lot of waste since the products will probably have to be thrown out or melted back down to start again.
Companies can find out the outliers in a data group by keeping measurements on an hourly basis on the specifications of a piston that is being produced on a production line. For instance, if a company knows that they can put out 100 pistons an hour from the output of a production line, they would try and measure 1 or 2 per hour and place the measurements into a spreadsheet or database, and see what kind of variation is taking place over a period of time. Are the greatest variations coming during the night shift? Day shift? Or is there some other trend that can be seen that can be illustrated? By knowing where the problem lies, it is much easier to try and fix the problem so that it doesn’t happen again.

There are many types and approaches to using control charts, and each one has a specific use that can target a unique industry or production company. It is very important to be able to know just how closely the target specs are being kept in order to stay in business.

“The general approach to on-line quality control is straightforward: We simply extract samples of a certain size from the ongoing production process. We then produce line charts of the variability in those samples, and consider their closeness to target specifications. If a trend emerges in those lines, or if samples fall outside pre-specified limits, then we declare the process to be out of control and take action to find the cause of the problem.” (UTA.edu, 2016, p.1)

UTA.edu. (2016). Quality Control Charts. Retrieved from http://www.uta.edu/faculty/sawasthi/Statistics/stquacon.html/.

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