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- 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. 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. 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. Forecasts Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design 3-5
- 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. 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. Elements of a Good Forecast 3-8 Timely AccurateReliable Written
- 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. 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. 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. 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. Forecast Variations 3-13 Trend Irregular variatio n Seasonal variations 90 89 88 Figure 3.1 Cycles
- 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. 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. 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. 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. 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. 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. ADVANTAGES: Easy to compute Easy to understand DISADVANTAGES: All values in the average are weighted equally Slow to react
- 20. Formula
- 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. If actual demand in period 6 turns out to be 38, the moving average forecast for period 7 would be
- 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. Weighted Moving Average ADVANTAGES Forecasts reflect more of from what is most recent Much more “accurate” than Moving Average
- 25. Weighted Moving Average DISADVANTAGES Arbitrary choice of weights Trial and Error is used to find weights
- 26. Weighted Moving Average
- 27. Weighted Moving Average EXAMPLE Given the following demand data: Period Demand 1 42 2 40 3 43 4 40 5 41
- 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. Weighted Moving Average
- 30. Weighted Moving Average
- 31.
- 32.
- 33. Example
- 34. Trend – A long term upward or downward movement in data. Example no. 1
- 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. 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. 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. 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. b = [10(41,358) – 55(7,407)] / [10(385) - 55(55)] = 7.51 a = [7,407 – 7.51(55)] / 10 = 699.40
- 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. 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. Regression Technique for fitting a line to a set of points
- 43. Simple Linear Regression Technique for fitting a line to a set of points. A linear relationship between two variables.
- 44. Formula:
- 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. $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. “Y=0.0506 + 0.0159x”

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