Process control Essay

Process control is a major factor in ensuring that a process is working to its maximum potential. Sometimes process control means that one must conduct research to see how a process improvement can be implemented. Process improvement normally results in adjusting and modifying particular tasks or steps in a process to make them run more efficiently and smoothly. According to Chase, Jacobs, and Aquilliano, it is very important to put metrics in place so that you can determine if improvements are needed for a particular process (Chase, Jacobs, and Aquilliano, 2006).

Over the last five weeks, I have observed the task of getting ready for work. After collecting data for analysis, I have enough information to develop a process improvement plan. In this paper, the control limits will be reviewed and any applicable seasonal factors that could impact the historical data will be discussed. Control Limits In an article in the Journal of Science and Technology, a control chart is described as “a statistical devise used for the study and control of a repetitive process” (Radhakrishnan and Balamurugan, 2010, p 1052).

The control chart is a tool used with 6 sigma to look at ways to improve the performance of a particular process. They basically let management know when they should adjust a process or when we should leave it alone. It has upper specification limits (USL) that are used to identify the maximum amount of that could give acceptable performance. Also there is the lower specification limit (LSL), which identifies the lowest amount that could give acceptable performance of the process.

The USL and LSL are also known as the control limits that are either 3 deviations above the mean or 3 deviations below it (Chase, Jacobs, and Aquilliano, 2006). In developing a control chart for the process for getting ready for work, one must first look at the sampling plan. For weeks, data has been collected during business days to indicate how long it takes to get ready for work. This data is reviewed to find the mean, median, standard deviation, and mode. The results are as follows: mean is 74. 70588, median is 74, mode is 71, and standard deviation is 3. 981792.

This information tells us that the average time it takes to get ready is 74 minutes, whereas the time occurring the most is 71 minutes. The standard deviation in this indicates that we can go either 3. 98 to the right or left of the zero probability distribution. The chapter readings advise that in looking at the control chart, one can assume that the process is working properly when the samples stay within the control limits (Chase, Jacobs, and Aquilliano, 2006). The next indicator to look at is the capability index, which in this case shows us how well we are doing in getting ready for work in a timely manner.

The readings indicates that the more off-center the capability index, the higher the chance to of defective products. Because we are not discussing products in this process, it would be the greater the chance to get off schedule (Chase, Jacobs, and Aquilliano, 2006). The data indicates that there is a capability index of -8. 6. According to Landauer, the capability index is interpreted as: “1) if the capability index is less than one, the process is outside the control limits 2) if it is greater than one; the process is within the control limits.

The results from my data at first was a little puzzling because the index of -8. 6 would indicate that the process is outside of the control limit and would need to be adjusted. However, because the process improvement plan is to decrease the time it takes to get ready for work, the negative number is a good factor. In having this information handy, one must also consider how seasonal factors impact the process. Seasonal Factors It is a little difficult to determine seasonal factors that could impact the time it takes to get ready for work.

After thinking about this a while I think the biggest impact could be daylight saving time. When an hour of sleep is lost in the Spring, it takes a while to adjust to the change in the daylight saving time. In fact, research indicates that when one transitions into and out of daylight saving time, there could be negative impacts (Lahti, Leppamaki, Lonnqvist, and Partonen, 2008). These impacts could result in a loss of sleep, restlessness, and lack of sleep quality which could result in crankiness and negatively impact a two year old.

If my son does not wake with a positive attitude, I know it will be a challenge getting ready for work on time as I would need to devote more time calming him down. During the seasonal periods, it may be better to implement process improvements like adding a resource to assist with the process. In my process this would mean getting my husband to help out more in getting the baby ready for daycare. With the added resource, one can shorten the time that it takes to complete the process and does not run the risk of deviating off course.

Applying this same format to a production process could mean adding more employees, equipment, or even outsourcing to ensure that a project or process is completed timely. Conclusion Overall, control charts are visual measures that assist with determining if a process is in control or not. When backed by statistical data like the mean, median, mode, standard deviation, and capability index, one could gather enough data so that a process improvement decisions necessary for keeping the process in control can be made.

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