# Scenario: Ajax Manufacturing is an electronic test equipment

Scenario: Ajax Manufacturing is an electronic test equipment manufacturing firm that markets a certain piece of specialty test equipment.  Ajax has several competitors who currently market similar pieces of equipment.  While customers have repeatedly indicated they prefer Ajax’s test equipment, they have historically proven to be unwilling to wait for Ajax to manufacture this certain piece of equipment on demand and will purchase their test equipment from Ajax’s competitors in the event Ajax does not have the equipment available in inventory for immediate delivery.  Thus, the key to Ajax successfully maintaining market share for this particular piece of equipment has been to have it available in stock for immediate delivery.  Unfortunately, it is a rather expensive piece of equipment to maintain in inventory.

Period
Actual Number of Sold Units

Period
Actual Number of Sold Units
1
33

16
38
2
36

17
37
3
32

18
37
4
35

19
37
5
33

20
35
6
34

21
37
7
34

22
34
8
38

23
35
9
37

24
34
10
36

25
36
11
38

26
32
12
36

27
35
13
37

28
33
14
39

29
38
15
35

30
37

31
? Forecast ?

Thus, the president of Ajax Manufacturing is very interested in accurately forecasting market demand in order to ensure he has adequate inventory available to meet customer demand without incurring undue inventory costs.  His sales department has provided historical data (in the table above) regarding market demand for this certain piece of specialty electronics test equipment for the past 30 months.

Instructions for Homework Assignment: Use the provided Excel file to construct a spreadsheet for each of the four models (Models 1-4) then answer Questions 26 - 28 based upon your calculations.

Model 1: Answer the following questions.

Question 1. Calculate the projected demand for the test equipment for the time periods 4 - 31 based upon a 3-month moving average forecast model? (5 points)

Question 2. Calculate the mean absolute deviation (MAD) for the 3-month moving average forecast for each of the time periods 4 through 30 and for the model. (5 points)

Question 3. Calculate the mean squared error (MSE) for the 3-month moving average forecast for time periods 4 through 30 and for the model. (5 points)

Question 4. Calculate the mean absolute percent error (MAPE) for the 3-month moving average forecast for time periods 4 through 30 and for the model. (5 points)

Question 5. Calculate the Standard Error for the model. (5 points)

Question 6. Calculate the Bias for the model. (5 points)

Model 2: Answer the following questions.

Question 7: Calculate the projected demand for the test equipment for the time periods 4 - 31 based upon a 3-month moving average forecast model for which the weighting factor for actual demand one month ago is 3, the weighting factor for actual demand two months ago is 2, and the weighting factor for actual demand three months ago is 1? (5 points)

Question 8: Calculate the mean absolute deviation (MAD) for the 3-month weighted moving average forecast for each of the time periods 4 through 30 and for the model. (5 points)

Question 9: Calculate the mean squared error (MSE) for the 3-month weighted moving average forecast for time periods 4 through 30 and for the model. (5 points)

Question 10: Calculate the mean absolute percent error (MAPE) for the 3-month weighted moving average forecast for time periods 4 through 30 and for the model. (5 points)

Question 11: Calculate the Standard Error for the model. (5 points)

Question 12: Calculate the Bias for the model. (5 points)

Model 3: Answer the following questions.

Question 13: Calculate the projected demand for the test equipment for the time periods 1 - 31 based upon using an exponential smoothing forecast model for which alpha = 0.25. Assume the Forecast is equal to the Demand for period 1. (5 points)

Question 14: Calculate the mean absolute deviation (MAD) for the exponential smoothing forecast for each of the time periods 1 through 30 and for the model. (5 points)

Question 15: Calculate the mean squared error (MSE) for the exponential smoothing forecast for time periods 1 through 30 and for the model. (5 points)

Question 16: Calculate the mean absolute percent error (MAPE) for the exponential smoothing forecast for time periods 1 through 30 and for the model. (5 points)

Question 17: Calculate the Standard Error for the model. (5 points)

Question 18: Calculate the Bias for the model. (5 points)

Model 4: Answer the following questions.

Question 19: Calculate the projected demand for the test equipment for the time periods 1 - 31 based upon using a Regression forecast model. (5 points)

Question 20: Calculate the mean absolute deviation (MAD) for the Regression forecast for each of the time periods 1 through 30 and for the model. (5 points)

Question 21: Calculate the mean squared error (MSE) for the Regression forecast for time periods 1 through 30 and for the model. (5 points)

Question 22: Calculate the mean absolute percent error (MAPE) for the Regression forecast for time periods 1 through 30 and for the model. (5 points)

Question 23: Calculate the Standard Error for the model. (5 points)

Question 24: Calculate the Bias for the model. (5 points)

Question 25: Discuss the correlation between the two variables and is the relationship significant at the 95% level of confidence? (5 points)

Question 26: Based upon using mean absolute deviation (MAD) as a measure of forecast accuracy, which of the preceding forecast models provides the greatest degree of forecasting accuracy, and which of the preceding forecast models provides the least degree of forecasting accuracy? (5 points)

Question 27: Based upon using Bias as a measure of forecast accuracy, which of the preceding forecast models provides the greatest degree of forecasting accuracy, and which of the preceding forecast models provides the least degree of forecasting accuracy? (5 points)

Question 28: Based upon using the Standard Error as a measure of forecast accuracy, which of the preceding forecast models provides the greatest degree of forecasting accuracy, and which of the preceding forecast models provides the least degree of forecasting accuracy? (5 points)