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You just finished your MBA degree and your boss wants “to show you off” in an upcoming staff meeting because she feels you have a lot of potential. Needless to say, you feel some pressure.
She says the company’s machines have required “high number of repairs” over the past year and feels the repair service staff needs to be “beefed up”, but she is not sure how many new staff are needed. The repair department was stood up one year ago, and she tasks you to come up with staffing estimates to make sure there are enough repair staff to meet the demand. Below is the data she gives you for the past 16 months, and then she asks, “How many repair staff positions would you forecast we need in the next 6 months?”
Year 1 – # of repair calls received. The repair staff is open from 7am to 7pm, seven days a week, and repair staff work the entire 12-hour shift. You can assume a single repair takes an average of 30 minutes for each repair.
| Month | Average # of repair calls received per day for each month the repair office was open in year 1 |
| Jan | 25 |
| Feb | 19 |
| Mar | 28 |
| Apr | 32 |
| May | 42 |
| Jun | 47 |
| Jul | 50 |
| Aug | 49 |
| Sep | 52 |
| Oct | 54 |
| Nov | 53 |
| Dec | 59 |
| Jan | 49 |
| Feb | 40 |
| Mar | 42 |
| Apr | 47 |
- Using Excel, create a linear regression equation to model the number of repairs per month.
- How many repair calls does the regression equation predict for June of year 2?
- Given your prediction for June of year 2, how many repair staff members do you forecast will be needed per 12-hour shift to handle the load?
Submit your findings in a Word document and include the Excel spread sheet.
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