1. What is your opinion of the level at which the data are being collected? What are some of the advantages of collecting data at this level?

2. Aggregate the original data for October as you see appropriate (e.g., sum up by day of week, time of day, week of the month, etc.). This will give you a new data set to work with. Analyze your data for patterns. Can you find any?

3. Use at least two different forecasting models on the new data set you developed in question 2 by aggregating the original data. Compare their forecast performance and provide an evaluation.

Jenn Kostich is the new department director for emergency services at Northwest General Hospital. One of her responsibilities is to ensure proper staffing in the emergency room (ER) by scheduling nurses to appropriate shifts. This has historically been a problem for the ER. The former director did not base nurse schedules on forecasts, but used the same fixed schedule week after week. Jenn had recently received her degree in operations management. She knew that schedules needed to be based on forecasts of demand. She needed to start by analyzing historical data in order to determine the best forecasting method to use. Jenn’s assistant provided her with information on patient arrivals in the ER by hour and day of the week for the previous month, October.

October was considered a typical month for the ER, and Jenn thought it was a good starting point. Jenn reviewed the information (shown in the chart) that she had requested and wondered where tobegin.

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Patient Arrivals in the ER Day of Week WTFSS MTW TFSS MTW TFSS M T W T FSS M TW T F Day of Month 12 345 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Time 1121 110 2 2 2 D 1 2111 00 1DD0 4 2 131 10 3 3 0 0I 10 3 0 2 0 1 01 02 3 2 121 2 2433 3 1 134 3 3 1 1 3 3 0 3 3 2 3 21 0 3 3 0 231 2 0 2 1 3 3 11 1 3 3 3 3 113 2 4342 3 1 4 242 3 31 4 I 2232 114 3 2 2 2 2 3 213 10 110o 11 1 2 2 1 13 1 02 3 13 D 414 02 22 2 3 1 02 1 2 3 2 43501 33 4 3 1 10 4 25 44 11403 4 4 423 0 3 24564 23 2 3 5542 1 3465 232 3 5 2 40 4 3 2 4 3 345S 1 4 2 2 3 63 03 2 4 4 6 4 04 2 25 45 23 2 3 5 6435 43 4 2 446 3 43 5 5 2 6 45 3 4 45 64 324 5 12 23545S 3 3 3 24 45 3 23 454 533 33 0 0 1 2 2 0 2 2 0 1445 3 2 0 2 5 4 4 3 3 20 545 3 2 3 2 2 1 1 3113 0 2 3 227 21 31432321 3023 2 30237 3 2 0 23 3 1 2 20 0 01 24 I22 1 12 2 0I I I054 5 23 3 1 3 1200 11 13 3 31 10 2 1 3112 0 21 5 451 3 3 0 5 4 54 5 3 3 151 402 2 2 5 4 340I 5 2 2 402 03 4 3 2 4 6 S 2 0 4 0 35 5 2 3 3 3 6 65 2 0 3 43 4 5 30533 4 1 565 33 3 35 4424 2 2 6 6 63 23 3 6 6 5 13 2 6 40343 3 3 33 256 4 0 2 0 245 3010 6 6 6 3 2 1 0 6 17 19 21