Forecasting

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Forecasting

  1. 1. Learning Objectives  List the elements of a good forecast.  Outline the steps in the forecasting process.  Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.  Compare and contrast qualitative and quantitative approaches to forecasting. 3-2
  2. 2. Learning Objectives  Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems.  Describe two measures of forecast accuracy.  Describe two ways of evaluating and controlling forecasts.  Identify the major factors to consider when choosing a forecasting technique. 3-3
  3. 3. FORECAST: - A statement about the future value of a variable of interest such as demand. - Forecasting is used to make informed decisions. - Long-range/Short-range 3-4
  4. 4. Forecasts  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design 3-5
  5. 5. Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services
  6. 6. Features of Forecasts  Assumes causal system  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals  Forecast accuracy decreases as time horizon increases 3-7 I see that you will get an A this semester.
  7. 7. Elements of a Good Forecast 3-8 Timely AccurateReliable Written
  8. 8. Types of Forecasts  Judgmental - uses subjective inputs  Time series - uses historical data assuming the future will be like the past  Associative models - uses explanatory variables to predict the future 3-9
  9. 9. Steps in the Forecasting Process 3-10 Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Obtain, clean and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast”
  10. 10. Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinion  Delphi method  Opinions of managers and staff  Achieves a consensus forecast 3-11
  11. 11. Time Series Forecasts  Trend - long-term movement in data  Seasonality - short-term regular variations in data  Cycle – wavelike variations of more than one year’s duration  Irregular variations - caused by unusual circumstances  Random variations - caused by chance 3-12
  12. 12. Forecast Variations 3-13 Trend Irregular variatio n Seasonal variations 90 89 88 Figure 3.1 Cycles
  13. 13. Naive Forecasts 3-14 Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.
  14. 14. Naïve Forecasts  Simple to use  Virtually no cost  Quick and easy to prepare  Data analysis is nonexistent  Easily understandable  Cannot provide high accuracy  Can be a standard for accuracy 3-15
  15. 15. Uses for Naïve Forecasts  Stable time series data  F(t) = A(t-1)  Seasonal variations  F(t) = A(t-n)  Data with trends  F(t) = A(t-1) + (A(t-1) – A(t-2)) 3-16
  16. 16.  A technique that averages a number of recent actual values, updated as new values become available.  In a moving average, as each new value becomes available, the forecast is updated by adding the newest value and dropping the oldest then computing again the average.  The forecast “moves” by reflecting only the most recent values.
  17. 17.  In computing a moving average, including a moving total column aids computations.  To update the moving total: (newest value – oldest value) + moving total for each update
  18. 18.  The fewer the data points in an average, the more sensitive (responsive) the average tends to be.  Moving averages based on more data points will smooth more but be less responsive to “real” changes.
  19. 19. ADVANTAGES:  Easy to compute  Easy to understand DISADVANTAGES:  All values in the average are weighted equally  Slow to react
  20. 20. Formula
  21. 21. Example Compute a three-period moving average forecast given demand for shopping carts for the last five periods. Period Demand 1 42 2 40 3 43 4 40 5 41
  22. 22.  If actual demand in period 6 turns out to be 38, the moving average forecast for period 7 would be
  23. 23. Weighted Moving Average  Similar to Moving Average, but it assigns weights to the most recent values considered  The more recent the value is, the more weight it will have  Sum of all weights should be equal to 1.00
  24. 24. Weighted Moving Average ADVANTAGES  Forecasts reflect more of from what is most recent  Much more “accurate” than Moving Average
  25. 25. Weighted Moving Average DISADVANTAGES  Arbitrary choice of weights  Trial and Error is used to find weights
  26. 26. Weighted Moving Average 
  27. 27. Weighted Moving Average EXAMPLE Given the following demand data: Period Demand 1 42 2 40 3 43 4 40 5 41
  28. 28. Weighted Moving Average A) Compute a weighted average forecast using a weight of 0.40 for the most recent period, 0.30 for the next most recent, 0.20 for the next, and 0.10 for the next. B) If the actual demand for period 6 is 39, forecast demand for period 7 using the same weights as in part A.
  29. 29. Weighted Moving Average 
  30. 30. Weighted Moving Average 
  31. 31.   
  32. 32.   
  33. 33. Example
  34. 34. Trend – A long term upward or downward movement in data. Example no. 1
  35. 35. Techniques for Trend  Linear Trend Equation F = a + bt where F = Forecast for period t a = Value of F b = Slope of the line t = Specified number of time periods from t = 0 b = ((nSumty – SumtSumy) / (nSumt – (Sumt)) a = (Sumy) – bSumt) / n
  36. 36. Example Cell phone sales for a Shanghai-based firm over the last 10 weeks are shown in the table below. Plot the data, and visually check to see if a linear trend line would be appropriate. Then determine the equation of the trend line, and predict sales for weeks 11 and 12. Week Unit sales 1 700 2 724 3 720 4 728 5 740 6 742 7 758 8 750 9 770 10 775
  37. 37. a. A plot suggests that a linear trend line would be appropriate: 660 680 700 720 740 760 780 800 1 2 3 4 5 6 7 8 9 10 Sales Week
  38. 38. b. You can use Excel template to obtain the table below. Week (t) y ty 1 700 700 2 724 1,448 3 720 2,160 4 728 2,912 5 740 3,700 6 742 4,452 7 758 5,306 8 750 6,000 9 770 6,930 10 775 7,750 7,750 41,358
  39. 39. b = [10(41,358) – 55(7,407)] / [10(385) - 55(55)] = 7.51 a = [7,407 – 7.51(55)] / 10 = 699.40
  40. 40. c. Substituting values of t into the equation, the forecasts for the next two periods are: F = 699.40 + 7.51(11) = 782.01 F = 699.40 + 7.51(12) = 789.52
  41. 41. d. For the purpose of illustration, the original data, the trend line, and the two projections (forecasts) are shown on the following graph: 660 680 700 720 740 760 780 800 1 2 3 4 5 6 7 8 9 10 11 12 Sales Week Forecasts Trend line
  42. 42. Regression  Technique for fitting a line to a set of points
  43. 43. Simple Linear Regression  Technique for fitting a line to a set of points.  A linear relationship between two variables.
  44. 44. Formula: 
  45. 45. Example  Healthy Hamburgers has a chain of 12 stores in Sydney. Sales figures and profits for the stores are given in the following table. Obtain a regression line for the data, and predict profit for a store assuming sales of $10 million.
  46. 46. $0.00 $0.05 $0.10 $0.15 $0.20 $0.25 $0.30 $0.35 $0.40 $0.45 $0.50 $0 $5 $10 $15 $20 $25 Profits, y Profits, y
  47. 47. “Y=0.0506 + 0.0159x”

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