Compute the trend and seasonal factor from a linear regression equation

1.One local firm sells radio frequency inventory tags. Its monthly sales for a seven-month period are shown as the following:

a. What would you do first if you have the data in table 1-3? What’s your conclusion from your first step?

b. Forecast September sales volume using each of the following:

(1) The naive approach;

(2) A five-month moving average;

(3) A weighted average using .60 for August, .30 for July, and .10 for June.

(4) Exponential smoothing with a smoothing constant of .20, assuming a March forecast of 19. (5) A linear trend equation.

c. What does use of the term sales rather than demand presume? (Hint: Think about the difference between sales and demand.)

2. You are given two years of demand data shown in table

a. Explain why an averaging technique would not be appropriate for forecasting here.

b. Compute the trend and seasonal factor from a linear regression equation.

c. Describe the main difference between what you did in part. b with the method of “decomposition using least squares regression”, that we discussed in the class.

3. Giving the following data, make a forecast for May using exponential smoothing with trend method. Assume that forecast for April is 800 units and the initial trend is 30 units in that period. (alpha = .3 and delta = .1)

4. Your shop has just received an order for 50 chain saws, which are to be shipped at the start of week 8. You can find all necessary information such as components, lead time and on hand inventories in the following table

a. Develop a product structure tree, an assembly time chart, and a master schedule.

b. Develop the material requirements plan for component F using lot-for-lot ordering for all items.

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