PPC Tutorial sheet 2: Forecasting

  1. Businesses, typically have long term objectives that they wish to achieve in next five-ten years, a strategy for achieving these objectives based on market realities, a forecast of demands on them during these years based on the given strategy and a plan of action to achieve these demands. Apply this analogy to your life and describe/discuss briefly, your objectives, your strategy, the short term, intermediate term and long term demands on you based on this strategy and your action plan for fulfilling the forecasted demands.

     

  2. Discuss the differences between the following type of forecasts:
    1. aggregate versus single item
    2. Short term versus long term
    3. Causal versus naïve

 

  1. Discuss the following quotation from the inventory control manager. Its not my fault we ran out of those parts. The demand forecast was wrong.

     

  2. The total cost of production for a product is related to the number of units produced. The data for the past 10 months is as follows:

Month

Production Cost (000’s of Rs.)

# of units produced

1

30

5

2

51

10

3

46

8

4

22

4

5

37

6

6

69

12

7

21

4

8

45

7

9

65

13

10

55

10

  1. Use regression analysis to determine the relationship between production cost and the units produced.
  2. If the company is planning a production level of 11,000 units for the upcoming month, what is your forecast for the total production cost that the company will incur?
  3. Compute the standard error of estimate for the regression line. What is its interpretation in the context of this problem?
  4. What % of variation in production cost is explained by the units produced?

 

  1. The following past demand history data is available for the ABC company:

Quarter

1995

1996

1997

1

5

5

10

2

15

25

  

3

25

30

  

4

5

5

  

  1. Assume that a = 0.1, b= 0.2 and g = 0.2. Develop a ratio seasonality, linear trend forecasting model. Use the data from year 1995-1996 to calculate initial parameters. Forecast demand for 1997 Q1, update parameters using the observed demand data and provide a forecast for Q2.
  2. Now develop the additive seasonality, ratio trend model. Write down the parameters that you would estimate (the base, the trend component and the seasonality component) and how these parameters would be updated after each quarter (simply write down the equations, no need to plug in numbers).
  3. How would you estimate the initial terms in this case?
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