Read the “JET Copies” Case Problem on pages 678-679 of the text of the Quantitative Methods Book. Using simulation estimate the loss of revenue due to copier breakdown for one year, as follows:

1. In Excel, use a suitable method for generating the number of days needed to repair the copier, when it is out of service, according to the discrete distribution shown.
2. In Excel, use a suitable method for simulating the interval between successive breakdowns, according to the continuous distribution shown.
3. In Excel, use a suitable method for simulating the lost revenue for each day the copier is out of service.
4. Put all of this together to simulate the lost revenue due to copier breakdowns over 1 year to answer the question asked in the case study.
5. In a word processing program, write a brief description/explanation of how you implemented each component of the model. Write 1-2 paragraphs for each component of the model (days-to-repair; interval between breakdowns; lost revenue; putting it together).
6. Answer the question posed in the case study. How confident are you that this answer is a good one? What are the limits of the study? Write at least one paragraph.

To solve the "JET Copies" case problem using simulation in Excel, you need to follow these steps:

1. For generating the number of days needed to repair the copier, you can use the discrete distribution shown. Use Excel's RAND() function and IF statements to generate a random number and map it to the corresponding repair time in the distribution. For example, if the random number is between 0 and 0.1, assign a repair time of 1 day, if it's between 0.1 and 0.3, assign a repair time of 2 days, and so on.

2. To simulate the interval between successive breakdowns, you can use the continuous distribution shown. Excel provides various functions for generating random numbers from different distributions. You can use the RAND() function and map the generated random number to the corresponding interval using the inverse transformation method. For example, if the random number is x, the interval can be calculated as -5 * ln(1-x).

3. Simulating the lost revenue for each day the copier is out of service involves multiplying the number of days needed for repair by the daily revenue. Multiply the repair time generated in step 1 by the daily revenue to get the lost revenue for that particular breakdown.

4. Put all of this together to simulate the lost revenue due to copier breakdowns over 1 year. You can set up a loop in Excel to repeat steps 1 to 3 for a specified number of iterations, representing one year. Sum up the lost revenue for each breakdown to get the total lost revenue for the year.

5. In a word processing program, write a brief description/explanation of how you implemented each component of the model. Write 1-2 paragraphs for each component:
- Days-to-repair: Explain how you used the discrete distribution to generate repair times and map random numbers to specific repair times.
- Interval between breakdowns: Describe how you used the continuous distribution and the inverse transformation method to generate the interval between breakdowns.
- Lost revenue: Explain how you multiplied the repair time by the daily revenue to calculate lost revenue for each breakdown.
- Putting it together: Provide an overview of how you combined the components together, looping over one year to calculate the total lost revenue due to copier breakdowns.

6. Answer the question posed in the case study and evaluate the confidence in your answer. Discuss the limitations of the study. In this case, you can calculate the average lost revenue over the simulated year and provide a confidence interval based on the variability of the results. The confidence in your answer depends on the accuracy of the assumptions made in the simulation and the quality of the input data. Limitations can include assumptions about the distribution, accuracy of the historical data used, and assuming that the copier will continue to operate under the same conditions as in the past. It's important to acknowledge the limitations and potential sources of error in your model.