Riordan Process Improvement Plan

Topics: Statistics, Confidence interval, Normal distribution Pages: 5 (1329 words) Published: September 7, 2010
Process Improvement Plan
Time is always moving forward making it difficult to execute daily processes slowly. Travelling is a daily process that takes much time and resources. Time spent on travelling can be known as waste time as the main goal is to transport from point A to point B without analyzing or performing actions on other tasks. Multitasking is not advisable meaning a high focus should be on the road and other road users plus it is illegal. The process if done as quickly as possible can reduce the cycle time leaving extra time for more profitable processes. The activity to drive from home to office is graphically shown below in the form of a flowchart.

Currently time taken to execute the activity is not efficient. Certain processes are occupying heavier proportion from the total cycle time. A process improvement plan is drawn not only to analyze and reduce current time but also not forgetting to achieve a safe trip.

Statistical Process Control
Data below tabulates five weeks of travelling time from home to office. The next step is to deduce whether the data is efficient by running a test. Statistical process control (SPC) tests random samples from processes to determine the productivity is perfectly efficient (Chase, Jacobs & Aquilano, 2006). The test graphically depicts the upper control limit (UCL) and lower control limit (LCL) of each the average mean and average range graphs. Average of time taken and range from each week in combination with the range and average factors are requirements to calculate both limits. Graphs with the limits first, plot the weekly average mean and average range. Observation is made from the graphs to decide on whether or not all sample data is within the control limits. The sample data that either is higher than the UCL or lower than the LCL will be the overuse time. Value of data is not only under observation but also the pattern of the chart is also under monitoring. The pattern of a stable chart is sample data closely plotting around the mean data. Patterns that exhibit an increase toward the UCL or decrease toward the LCL or erratic behavior must undergo investigations (Chase et al., 2006). Sample Number| Each Unit in Sample (mins)| Total (mins)| /X (mins)| Range (mins)| 1| 70| 64| 50| 58| 43| 47| 78| 410| 58.57| 35|

2| 90| 89| 81| 66| 57| 53| 83| 519| 74.14| 37|
3| 106| 63| 73| 70| 46| 64| 96| 518| 74.00| 60|
4| 103| 82| 86| 81| 55| 52| 107| 566| 80.86| 55| 5| 97| 65| 77| 62| 53| 63| 0| 417| 59.57| 97|

A2| 0.42| | D3| 0.08| | D4| 1.92|

Note. From Chase et al., 2010.

Note. From Chase et al., 2010.
//X (mins)| 69.43| /R (mins)| 56.80|
UCL /X (mins)| 93.28| UCL /R (mins)| 109.06|
LCL /X (mins)| 45.57| LCL /R (mins)| 4.54|

The both chart depicts that the average of total time and range is within the UCL and LCL. The observation only concludes that the current data is allowable but not perfectly efficient. The pattern of the data in the average mean chart depicts a run of three plots above central line. The practice to avoid the first week’s traffic congestion is to leave from home reaching office exactly at 9.00 a.m. The second and third week changes practice as work is piling up and requires more setup time.

The pattern of the data in R chart depicts an increase. The final plot reaches a range nearly to the UCL. The reason is the zero value recording of total cycle time on Monday.
Seasonal Factors
The data above is in normal tabulation manner meaning no trips involving external variables or environmental factors intervention is taken into consideration. External...

References: Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006). Operations management for competitive
advantage (11th ed.). New York: McGraw Hill/Irwin.
NIST SEMATECH (n.d.). What are Confidence Intervals? Product and Process Comparisons.
Retrieved from http://www.itl.nist.gov/div898/handbook/prc/section1/prc14.htm
University of Phoenix. (2010). Statistical Techniques. Retrieved August
21, 2010 from University of Phoenix, QNT 561 – Applied Business Research & Statistics
Course Website.
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