The accounting department manager noticed that supplier invoice mistakes seemed to be related to the number of weeks that employees had been employed on that job. The data available in the worksheet below were gathered from the process. The mistakes included missing information, incorrect amounts, data entry errors, incorrect account numbers, etc.

Supplier Invoice Processing Errors versus weeks of Employment

Weeks. On job	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
% Invoice Errors	15.8	16.3	19.4	18.8	12.0	13.2	15.9	15.3	11.0	7.6	2.6	3.0	3.8	4.1	4.0	4.3	4.0	3.9	4.2	4.9

1. Does the data confirm or refute the managers suspicions about error rates versus number of weeks employed? Explain. Do you notice anything odd in the data?
2. Draw and label a Run Chart depicting the data. What more, if anything, can the manager learn from the Run Chart that was not evident from the data?
3. The department manager recently read something about standard deviation (sigma), statistical process control, and control charts. She asked you if this information should be plotted into a Control Chart. What would you tell her?
4. How should the manager go about trying to better understand the cause of the significant difference in error rates before week 10 versus after week 10? What additional process tools should she use to further understand the problem and identify potential solutions? Explain why you selected those tools.
5. What additional information should the manager gather before deciding if it is worthwhile to launch a COPQ improvement project to reduce the number of invoice errors?