Datastor Case Study - Stats Probabilitiy and Binomials
Case Study for DataStor
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” …show more content…
The PDQ test sample data was provided for 3 shifts per day, for each of the 5 days of the week, for 10 weeks. We chose to divide the data according to the shifts to determine whether the quality of work for a given shift could explain the rejection rate from Four-D.
The table below is based on the PDQ sample data. Shift 1 and 2 meet DataStor’s “in control” mean. Shift 3 does not meet the DataStor’s “in control” mean of 7.0.
Similarly, Shifts 1 and are aligned with the sigma X-bar, based on their groups’ PDQ sample data. Shift 3’s sample standard deviation is higher than the sample sigma x-bar. | Mean | SD | Sigma X-bar | Shift 1 | 7.00 | 0.105 | 0.106 | Shift 2 | 7.00 | 0.102 | 0.106 | Shift 3 | 6.88 | 0.155 | 0.106 |
5. What might be the source(s) of the problem at DataStor? Is the problem with rejected shipments due to an increase in drive nonconformance at DataStor, to increased quality requirements by Four-D Office Products, to damage during shipment, or is it simply due to random variation? What evidence leads you to your conclusion?
Following are the reasons for problems at DataStor: * The production of Shift 3 is not stable. The mean PDQ score is 6.88 with a std deviation of 0.155, this is higher than the sample