Describe the details of the Last Value method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Forecasting using template provide with excel

Part 1 (15 points):
Question 1a (5 points): Define a problem statement which reflects the challenge facing Mark as he planned for the opening of the new center.

Question 1b (5 points): Why was Mark’s initial forecast of call volume so far off? What could have been the reasons for this?

Question 1c (5 points): What could Mark have done differently to improve his initial forecast?

Part 2 (25 points):
In answering the Part 2 questions, you should download and refer to Student Data File No. 1 which contains the historical data that was used in preparing the forecast results that are reported in Part 2 of the case write-up document.

Note that you do not have to prepare any forecasts in answering this question. Hint: it will be helpful for you to review a time-series plot of the 13 weeks of data contained on Student Data File No. 1.

Question 2a (4 points): Describe the details of the Last Value method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Question 2b (4 points): Describe the details of the Averaging method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Question 2c (4points): Describe the details of the Moving Average (5 days) method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Question 2d (4points): Describe the details of the Exponential Smoothing (alpha = 0.1) method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Question 2e (4 points): Describe the details of the Exponential Smoothing (alpha = 0.7) method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

Question 2f (5 points): Based on the analysis above, provide your recommendations to Mark on daily call volume forecasting to improve the scheduling of the call enter staff.

Part 3 (50 points): (copy your Excel solutions and paste them into the Word document as a picture).

In answering the Part 3 questions, you should download and refer to Student Data File No. 2 which contains the historical data that you will need to answer the questions.

Question 3a (10 points):
Prepare a forecast of call volume for July 2015 by applying Exponential Smoothing (with alpha = 0.5) to the prior 18 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast and assume that initial call volume is 24,000.

Show your forecast below and attach the completed Excel template.
Call Volume Forecast for July 2015 (Exponential Smoothing, alpha=0.5):

Question 3b (10 points):
Apply Linear Regression to predict call volume from head count using the appropriate Excel template. Assume the headcount in July is 80000. Using this headcount number, show your forecast below and attach the completed Excel template.
Call Volume Forecast for July 2015 (Causal Forecasting based on head count of 80,000):

Question 3c (10 points):
Calculate the Mean absolute deviation value of the Exponential Smoothing model

(Question 3a) and the Average Estimation Error of the Linear Regression model

(Question 3b). Explain the difference between these two values.
Mean absolute deviation of Exponential Smoothing model, alpha=0.5:

Average Estimation Error for Causal Forecasting model based on headcount:
Explanation of the difference in values:

Question 3d (20 points):
Considering your answers to Questions 3a, 3b and 3c and all the factors that have been described above, prepare your best forecast for July 2015. Show your forecast value below and explain and justify how you came up with this forecast.

Finally, provide your recommendations to Mark on how to modify forecasting process and improve its accuracy.

Describe the details of the Last Value method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods.

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