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WestOver Electrical Case Study Essay

Executive SummaryWestover Electrical, Inc., is a medium-size Houston manufacturer of wire windings used in making electric motors. Winding or “Coil” copper wires wound around a core, to use to create or receive electromagnetic energy. The company VP of operations Joe Wilson, has experienced an increasing problem with rejected products found in the manufacturing operations. Joe hired a consultant, Roger Gagnon to find out why the problems occur. left47688500Using different tools for Total Quality Management, it was determined that – Abraded wire, Failed Electrical Test and Broken Leads. We gave three recommendations. The first one is hiring extra labor to accommodate the additional rework that will be done before sending it to Packaging.

The second is to replace Machine/Winder 3 entirely since it generates the most defects among all the machines. The third option is for Machines 1, 2 and 3 undergo punitive maintenance since all the defects have been accounted for, for each machine. The best recommendation is the 1st one. The defects were mainly concentrated in the machines 1 and 3 so the problems were mainly caused by this 2 machines and remedying them would lessen the defects that production will generate. Problem StatementThe root cause of the increasing rejected products needs to be found to prevent further losses.

AnalysisBy using different quality management tools, we would be able to analyze the data given more accurately. Westover Electrical Winding
Operation Flow ChartThis flowchart graphically presents the process of making a wire winding.

Production of windings by Machine 1, 2 and 3
Windings sent to Quality Control
Inspection of electrical windings
Windings sent to packaging
Packaging personnel inspect their work
Defects found are corrected on the spot
Windings are packaged

This helps us to analyze the winding process to determine what the different processes are and where to exert change. Causes of Winding Effects: Fishbone Diagramcenter62738000Based on the reject log by Joe Wilson, we would come up with this fishbone diagram, illustrating the different factors that would have caused the winding defects. The problem here refers to the winding defects. Statistical Process Control Chart: Rejects through TimeBy computing the total number of rejects per date, we are able to come up with this statistical process control chart.

As the chart shows, there’s a seemingly downward trend of rejected wire windings throughout the days of January.
Using the raw data from the Quality Control, the factors that heavily influences the occurring defects are the Abraded wire, Failed Electrical Test and Broken Leads but this is using only the raw data given. Since we were tasked to assume that each defective unit is caused by only one single defect we grouped each defects by Winder 1, Winder 2 and Winder 3 from respective machines 1, 2 and 3. We were able to come up with this table Total Defects per Winder

Winder 1 77
Winder 2 42
Winder 3 142
As illustrated in the Paretto chart, the majority of the defects are found in Winder 3 and followed by Winder 1.

Recommendations1st recommendation

Production of windings by Machine 1, 2 and 3
Windings sent to Quality Control
Inspection of electrical windings
Rework of items sent from Machine 1 and 3 to lessen defects
Windings sent to packaging
Packaging personnel inspect their work
Defects found by personnel are corrected on the spot
Windings are packaged
The Assumption is 80% of rework turnout will be flawless with a margin of 20% error. Winding 3 and 1 142 + 77 = 219 x .8 = 175
Number of original defects less rework 261 -175 = 86
Percentage difference after rework 67.05% over 32.95% remaining defects This would mean hiring extra labor to accommodate the additional rework that will be done before sending it to Packaging. Generating, more costs on labor and initial costs for training.

2nd recommendation
Machine 3 should be replaced entirely since it generates the most defects among all the machines. The assumption of replacing machine 3 is that all the defects generated by machine 3 will be down to 0 % Winder 3 142 x 0 = 0

Number of original defects less rework 261 – 142 = 119
Percentage difference after rework 54.41% over 45.59% remaining defects This would mean buying a new machine to replace machine 3
3rd recommendation
Machine 1, 2 and 3 will undergo punitive maintenance since all the defects have been accounted for, for each machine. The assumption is that 65% of defects for each machine will go down. With a margin of 35% defects remaining. Winder 1, 2 and 3 261 x 65%= 170

Number of original defects less rework 261 – 170 = 91
Percentage difference after rework 65.13% over 34.87% remaining defects This would mean we will halt production to remedy the problems with the machines to reduce the defects generated by each machine Best SolutionThe best recommendation is the 1st one. The defects were mainly concentrated in the machines 1 and 3 so the problems were mainly caused by this 2 machines and remedying them would lessen the defects that production will generate. Opting for the 2nd recommendation would incur a lot more cost since a machine would require high capital costs and also there are the risks that the defects would plague the new machine 3 when time passes.

Opting for the 3rd implementation can be implemented since it will cost less than hiring new labor but the risks are that the defects might come back after a time the punitive maintenance is done. Hiring new labor is the better choice since we would incur a much lower cost than recommendation 2 and would enable us to adapt to the defects generated by our machines than recommendation 3.

ConclusionWestover Electrical, Inc. had major production problems concerning defective windings that require reworking before they can be packaged. This caused the company additional unnecessary expenses. By using different total quality management tools, we were able to determine which cause of the defect to focus our resources on. In this way, we were able to point the company to the right direction in order to solve the problem. Using total quality management tools were a great help since not only did they provide an easier way to visualize data, but also organized the data in such a way that we were able to pin-point the root causes of the main problem.


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