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Statistical Process Control
Activities in Daily Routine Observations from the Chart
Application of Statistical Process Control and Solving the Problem
(a) Statistical Process Control: X-bar Charts
(b) Weekly Morning Time Utilization Chart
Observations from the chart
Effect of Seasonal Factors
Seasonal Factors
Usefulness of Confidence Intervals
This paper is on process control of activities that happen on daily basis. Statistical Process Control (SPC) involves application of statistical methods and procedures (such as control charts) to analyze the inherent variability of a process or its outputs to achieve and maintain a state of statistical control, and to improve the capability, also called statistical quality control . (Business Dictionary, 2010). The total time taken from waking up till reaching the office after going through various chores is 85 minutes. The person wants to cut it down to 60 minutes. He thinks of foregoing his leisurely sipping of coffee and watching news for 20 minutes and substitute it with taking coffee in the car and listening to radio for news. That is a way a common man looks at the problem.
Sipping coffee in the car and listening to radio form for news is not the same as doing the same in comfort at home. So an alternate solution is to be chalked out. This is exactly where the Process Selection Control and design step in. Let us now see how the concept can be applied to the problem.
2. Application of Statistical Process Control and Solving the Problem
Details of total time spent on taking bath, getting ready, taking coffee and watching news and travel to office for six days in a week are given below.
Day
Time Consumed (in min)
Monday
84
Tuesday
85
Wednesday
83
Thursday
87
Friday
86
Saturday
84
Average
84.83
(a) Statistical Process Control: X-bar Charts
Let us start by calculating the average for the data points.
Where
is the individual result and 'n' is the total number of results.
minutes.
Let us now calculate the upper control limit, UCL and lower control limit LCL.
UCL (calculated) =
LCL (calculated) =
(a)The daily utilization is mostly within UCL and LCL.
(b)Only one point lies slightly above the UCL.
(c.) The utilizations are equally using above and below average.
4. Conclusion
It is quite obvious from the above chart analysis that it is not possible to reduce the overall time below 83 minutes (LCL) even after cutting off daily deviations.
5. Solution
The only solution seems to be reducing the average utilization to 60 minutes and reduce the individual utilizations proportionately. The suggested utilization after proportionate reduction will be as shown in the table below.
Activity
Earlier Utilization
(in minutes)
Suggested Utilization
(in minutes)
Taking Shower
20
14
Watching news and drinking coffee
20
14
Changing and getting ready
30
21
Driving to Office
15
11
85
60
This way, he need not forego any of his routine activities but still can manage to perform all of them in 60 minutes.
6. Effect of Seasonal Factors
Seasonal factors affect process performance data. Seasonal factors can be referred to as weather changes, body clock changes and seasonal items used like dressing. In the given process design time change would be the seasonal factor that will be affecting the process performance data. The duration of day changes basing on the season each year. Depending upon change of the duration of the day, one may spend less time preparing for work or more time based on clock settings. When the day length…
References
[1.] Business Dictionary (2010). Retrieved August 30, 2012 from www.businessdictionary.com [2.]"Statistical Process Control: Process and Quality Views"(2008) Retrieved August 30, 2012 from http://www.cheresources.com/spczz.shtml [3.]"Statistical Tutorial: Confidence Intervals"(2010) Retrieved August 30, 2012 from http://stattrek.com/AP-Statistics-4/Confidence-Interval.aspx?Tutorial=Stat
[4.] Umarporn Charusombat & Andy Sabalowsky (1997) "Confidence intervals, Tolerance Intervals and Prediction intervals" Retrieved August 30, 2012 from http://www.cee.vt.edu/ewr/environmental/teach/smprimer/intervals/interval.html#appc [5.] Chase, R.B., Jacobs, F.R., & Aquilano, N.J. (2006). Operations management for competitive advantage (11th ed). New York: McGraw Hill/Irwin
[6.]Cook, Sarah (1996). Process Improvement: A Handbook for managers. Retrieved from http://books.google.com / [7.] Bob Raczynski and Bill Curtis (2008) Software Data Violate SPC's Underlying Assumptions, IEEE Software, May/June 2008, Vol. 25, No. 3, pp. 49-51
[8.] Paul H. Selden (1997). Sales Process Engineering: A Personal Workshop. Milwaukee, WI: ASQ Quality Press.
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