University of Phoenix
Dr. Anthony Matias
March 24, 2011
The premise of this paper is to identify deficiencies in daily managerial processes by using systematic statistical process controls and make the necessary improvements. The paper will employ various examples and calculations along with supporting data to explain control limits and its importance to the statistical process control. The effects of seasonal factors and its relevance to a process will also be highlighted and how confidence intervals are important in giving insights into data sets that improve the entire statistical process control.
Process Improvement Plan
In every successful organization, management is continual finding ways to tweak operational and managerial process to ensure efficiency and maximize profitability. Through learning curves, flowcharts, and statistical process controls improvement plan enable leaders to advance the company’s business interests through refining the structure of operations as opposed to solving problems singularly. It also eliminates traditional behaviors such as placing blame and pointing fingers and focuses on how the organization on a whole can work together. A true process improvement plan uses historical knowledge and the statistical implementation of data to minimize variations, rid the process on unwanted activities that are noncontributory.
Improvements in any process start Statistical process control, which is the random testing of a sample of output from the process to determine whether the process is producing items within a preselected range (Chase, Jacobs, & Aquilano, 2006). These samples may be either fit or unfit for example taking random samples from a batch of newly made Motorola I Pad. Testing may discover that some will work accordingly whereas other will not power on. Companies set determined statistically parameters that control the... [continues]
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