Westover Electric
Shawn Fiedler
Westover Electric Problem
MGMT 486
After organizing and looking at the Westover Electric data, I found there to be some trends in the data as for causes for the problems when looking at charts I made. After adding up the total defects in the month and organizing them by type there were three specific defects that accounted for 80.97% of the total defects. Going from most to least number of defects by cause, 31.17% happened from abraded wire, 25.51% from broken leads, 24.29% from failed tests, 8.91% from wrong wire, 4.05% from bad wind, 3.64% from twisted wire, and 2.43% from wrong core. So after looking at the Pareto Chart I made (Figure 1.1) it looks like 80.97% of the defects were from three causes. With this information, I had to see if it was assignable or un-assignable cause. So I separated these specific defects by each machine to see if I could see some type of pattern with the data and the machine. The first defect that I looked at closer was abraded wire because it had the larges percentage of defects. Separating the amount of defects per machine, I came up with 76 defects on machine one for abraded wire, machine two had two defects, and machine one had three defects (Figure 1.2). So almost all of the defects coming from abraded wire were coming from machine three. So there must be wrong with machine three that it’s causing the wire to become abraded. Whether it’s a calibration issue or a or operator error, I cant for sure say that it’s operator error because I don’t know if the same person is working on that specific machine everyday, or if there is more than one shift on that machine. The next largest defect, broken leads; was causing 25.51% of total defects. (Figure 1.3) Machine one was causing 62 defects of the 66 total defects of broken leads. Machine two was causing two defects of the 66, and machine three was causing the other two defects. So machine two and three I would say the defects are random, and on machine one its
Westover Electric Problem
MGMT 486
After organizing and looking at the Westover Electric data, I found there to be some trends in the data as for causes for the problems when looking at charts I made. After adding up the total defects in the month and organizing them by type there were three specific defects that accounted for 80.97% of the total defects. Going from most to least number of defects by cause, 31.17% happened from abraded wire, 25.51% from broken leads, 24.29% from failed tests, 8.91% from wrong wire, 4.05% from bad wind, 3.64% from twisted wire, and 2.43% from wrong core. So after looking at the Pareto Chart I made (Figure 1.1) it looks like 80.97% of the defects were from three causes. With this information, I had to see if it was assignable or un-assignable cause. So I separated these specific defects by each machine to see if I could see some type of pattern with the data and the machine. The first defect that I looked at closer was abraded wire because it had the larges percentage of defects. Separating the amount of defects per machine, I came up with 76 defects on machine one for abraded wire, machine two had two defects, and machine one had three defects (Figure 1.2). So almost all of the defects coming from abraded wire were coming from machine three. So there must be wrong with machine three that it’s causing the wire to become abraded. Whether it’s a calibration issue or a or operator error, I cant for sure say that it’s operator error because I don’t know if the same person is working on that specific machine everyday, or if there is more than one shift on that machine. The next largest defect, broken leads; was causing 25.51% of total defects. (Figure 1.3) Machine one was causing 62 defects of the 66 total defects of broken leads. Machine two was causing two defects of the 66, and machine three was causing the other two defects. So machine two and three I would say the defects are random, and on machine one its