Excel Logistics Services Case Study.
1. First construct a run chart of the data from the receiving area
2. Construct a p-chart to obtain an estimate for the proportion defective across the set of X sample points (given)
a. Total number defective = ?
b. Total Transactions evaluated = ?
c. Proportion defective = p = a/b
3. The next step is to obtain the standard deviation of proportion defective for a sample of size 800 per page 3 where Stalk is analyzing 800 per day for 45 days.
a. Standard deviation of proportion defective for sample size 800, std deviation = sqrt (p(1-p)/800) = ?
4. The next step is to obtain the upper and lower control limits as follows:
a. UCL= p 3 (std dev) = ?
b. LCL = p-3 (std dev) = ?
5. Now you can check whether the receiving process overall is in control.
Since the process is under control, we assume that the average proportion defective is likely to be p=0.0182.
1, What is the standard deviation for a set of 8000 transactions per day?
std deviation = sqrt (p(1-p)/8000) = ?
2. Now evaluate the proportion of days that exceed 2% defectives using normal distribution curve.
1-NORM.DIST (0.02,0.0182,0.00149,1) =?
Evaluate if this is acceptable.
I would pareto out the areas for improvement in a pareto diagram.
Put control limits and a p chart in place for the top item on the pareto
total # defective = ?
Total transactions evaluated = 36,000
Proportion defective = p = total # defective / 36,000 = ?
Calculate std deviation for sample size of 800
Calculate UCL and LCL
What are your findings?
Review any out of control data points and determine if you would like to delete those and recalculate the control limits to be used in the future.
Redo the p, std deviation, and UCL, LCL.
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