Datastor Case Study - Stats Probabilitiy and Binomials
DataStor Case Study | | Team 1 |
Case Study for DataStor
Background
DataStor, a data storage device and media manufacturer, produces a compact hard drive called DS1000, which stores 1GB of data. Their primary customer is Four-D, a national reseller of the drives.
Four-D has rejected four shipments of drives from DataStor in the past 20 days. DataStor wants to understand why their shipments are being rejected.
DataStor operates three 8-hour shifts, five days a week. Each shift produces approximately 120 drives for a daily average total of 360 drives per day. The company runs quality checks called PDQ tests on one of their drives every hour of production. The test takes up to 20 minutes. Their historical “in control” process is defined with a mean of 7.0 and a standard deviation of .3.
Four-D performs their own PDQ tests on the drives that DataStor ships them. They sample ten drives at random. If any of the drives have a PDQ score of 6.2 or below, then the entire shipment will be rejected. Penalties are assessed for each unacceptable shipment.
DataStor wants to determine why their shipments are being rejected. To do this, they first want to look at their internal processes before approaching Four-D to see if there are problems/issues on their side.
Case Study Questions
1. If the DataStor DS1000 hard drive production process at DataStor Company is “in control”, what percentage of the drives produced would be considered to be in nonconformance by Four-D? In other words, what is the likelihood (probability) that the PDQ test score of a drive tested at DataStor will fall below 6.2?
The probability that a PDQ test score of a drive tested at DataStor will fall below 6.2 is .38%.
We arrived at this conclusion based on DataStor’s “in control” standard:
Mean = 7.0
Std Dev = .3
Coincidentally, the mean and the X-bar of the data sample are the same, 7.0 (6.96).
Using Normal Probabilities feature in PhStat, we determined:
Case Study for DataStor
Background
DataStor, a data storage device and media manufacturer, produces a compact hard drive called DS1000, which stores 1GB of data. Their primary customer is Four-D, a national reseller of the drives.
Four-D has rejected four shipments of drives from DataStor in the past 20 days. DataStor wants to understand why their shipments are being rejected.
DataStor operates three 8-hour shifts, five days a week. Each shift produces approximately 120 drives for a daily average total of 360 drives per day. The company runs quality checks called PDQ tests on one of their drives every hour of production. The test takes up to 20 minutes. Their historical “in control” process is defined with a mean of 7.0 and a standard deviation of .3.
Four-D performs their own PDQ tests on the drives that DataStor ships them. They sample ten drives at random. If any of the drives have a PDQ score of 6.2 or below, then the entire shipment will be rejected. Penalties are assessed for each unacceptable shipment.
DataStor wants to determine why their shipments are being rejected. To do this, they first want to look at their internal processes before approaching Four-D to see if there are problems/issues on their side.
Case Study Questions
1. If the DataStor DS1000 hard drive production process at DataStor Company is “in control”, what percentage of the drives produced would be considered to be in nonconformance by Four-D? In other words, what is the likelihood (probability) that the PDQ test score of a drive tested at DataStor will fall below 6.2?
The probability that a PDQ test score of a drive tested at DataStor will fall below 6.2 is .38%.
We arrived at this conclusion based on DataStor’s “in control” standard:
Mean = 7.0
Std Dev = .3
Coincidentally, the mean and the X-bar of the data sample are the same, 7.0 (6.96).
Using Normal Probabilities feature in PhStat, we determined: