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Chapter 8. Forecasting. Forecasting. Predicting future events Usually demand behavior over a time frame Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas. Strategic Role of Forecasting. Focus on supply chain management

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Chapter 8


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    1. Chapter 8 Forecasting

    2. Forecasting • Predicting future events • Usually demand behavior over a time frame • Qualitative methods • Based on subjective methods • Quantitative methods • Based on mathematical formulas

    3. Strategic Role of Forecasting • Focus on supply chain management • Short term role of product demand • Long term role of new products, processes, and technologies • Focus on Total Quality Management • Satisfy customer demand • Uninterrupted product flow with no defective items • Necessary for strategic planning

    4. Components of Forecasting Demand • Time Frame • Short-range, medium-range, long-range • Demand Behavior • Trends, cycles, seasonal patterns, random

    5. Time Frame • Short-range to medium-range • Daily, weekly monthly forecasts of sales data • Up to 2 years into the future • Long-range • Strategic planning of goals, products, markets • Planning beyond 2 years into the future

    6. Demand Behavior • Trend • gradual, long-term up or down movement • Cycle • up & down movement repeating over long time frame • Seasonal pattern • periodic oscillation in demand which repeats • Random movements follow no pattern

    7. Forms of Forecast Movement

    8. Demand Demand Random movement Time (a) Trend Time (b) Cycle Demand Demand Time (c) Seasonal pattern Time (d) Trend with seasonal pattern Forms of Forecast Movement Figure 8.1

    9. Forecasting Methods • Time series • Regression or causal modeling • Qualitative methods • Management judgment, expertise, opinion • Use management, marketing, purchasing, engineering • Delphi method • Solicit forecasts from experts

    10. Forecasting Process

    11. 1. Identify the purpose of forecast 3. Plot data and identify patterns 2. Collect historical data 6. Check forecast accuracy with one or more measures 5. Develop/compute forecast for period of historical data 4. Select a forecast model that seems appropriate for data 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 8a. Forecast over planning horizon Forecasting Process Figure 8.2

    12. Time Series Methods • Statistical methods using historical data • Moving average • Exponential smoothing • Linear trend line • Assume patterns will repeat • Naive forecasts • Forecast = data from last period Demand?

    13. Moving Average • Average several periods of data • Dampen, smooth out changes • Use when demand is stable with no trend or seasonal pattern

    14. n i= 1  Di MAn = n where n = number of periods in the moving average Di= demand in period i Moving Average • Average several periods of data • Dampen, smooth out changes • Use when demand is stable with no trend or seasonal pattern

    15. Simple Moving Average

    16. ORDERS MONTH PER MONTH Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Simple Moving Average Example 8.1

    17. 3 i= 1 ORDERS MONTH PER MONTH  Di MA3 = Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 3 90 + 110 + 130 3 = Simple Moving Average = 110 orders for Nov Example 8.1

    18. ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 Simple Moving Average Example 8.1

    19. 5 i= 1 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE  Di MA5 = Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 5 90 + 110 + 130 + 75 + 50 5 = Simple Moving Average = 91 orders for Nov Example 8.1

    20. ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE Jan 120 – – Feb 90 – – Mar 100 – – Apr 75 103.3 – May 110 88.3 – June 50 95.0 99.0 July 75 78.3 85.0 Aug 130 78.3 82.0 Sept 110 85.0 88.0 Oct 90 105.0 95.0 Nov – 110.0 91.0 Simple Moving Average Example 8.1

    21. 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month Smoothing Effects Figure 8.2

    22. 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders Actual | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month Smoothing Effects Figure 8.2

    23. 150 – 125 – 100 – 75 – 50 – 25 – 0 – Orders 3-month Actual | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month Smoothing Effects Figure 8.2

    24. 150 – 125 – 100 – 75 – 50 – 25 – 0 – 5-month Orders 3-month Actual | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month Smoothing Effects Figure 8.2

    25. Weighted Moving Average • Adjusts moving average method to more closely reflect data fluctuations

    26. Wi Di WMAn = i = 1 where Wi = the weight for period i, between 0 and 100 percent  Wi= 1.00 Weighted Moving Average • Adjusts moving average method to more closely reflect data fluctuations

    27. Weighted Moving Average Example

    28. MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 Weighted Moving Average Example Example 8.2

    29. MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 November forecast 3 i = 1  WMA3 = Wi Di = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders Weighted Moving Average Example Example 8.2

    30. Exponential Smoothing • Averaging method • Weights most recent data more strongly • Reacts more to recent changes • Widely used, accurate method

    31. Exponential Smoothing Ft +1 = Dt + (1 - )Ft where Ft +1 = forecast for next period Dt= actual demand for present period Ft= previously determined forecast for present period = weighting factor, smoothing constant • Averaging method • Weights most recent data more strongly • Reacts more to recent changes • Widely used, accurate method

    32. Effect of Smoothing Constant 0.0  1.0If = 0.20, then Ft +1 = 0.20Dt + 0.80 Ft If = 0, then Ft+1 = 0Dt + 1 Ft0 = FtForecast does not reflect recent data If = 1, then Ft +1 = 1Dt + 0 Ft=DtForecast based only on most recent data

    33. Exponential Smoothing

    34. PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 Exponential Smoothing Example 8.3

    35. PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 F2 = D1 + (1 - )F1 = (0.30)(37) + (0.70)(37) = 37 F3 = D2 + (1 - )F2 = (0.30)(40) + (0.70)(37) = 37.9 F13 = D12 + (1 - )F12 = (0.30)(54) + (0.70)(50.84) = 51.79 Exponential Smoothing Example 8.3

    36. FORECAST, Ft + 1 PERIOD MONTH DEMAND ( = 0.3) 1 Jan 37 – 2 Feb 40 37.00 3 Mar 41 37.90 4 Apr 37 38.83 5 May 45 38.28 6 Jun 50 40.29 7 Jul 43 43.20 8 Aug 47 43.14 9 Sep 56 44.30 10 Oct 52 47.81 11 Nov 55 49.06 12 Dec 54 50.84 13 Jan – 51.79 Exponential Smoothing Example 8.3

    37. FORECAST, Ft + 1 PERIOD MONTH DEMAND ( = 0.3) ( = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61 Exponential Smoothing Example 8.3

    38. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Orders | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month Exponential Smoothing Forecasts Figure 8.3

    39. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Actual Orders | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month Exponential Smoothing Forecasts Figure 8.3

    40. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Actual Orders  = 0.30 | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month Exponential Smoothing Forecasts Figure 8.3

    41. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Actual  = 0.50 Orders  = 0.30 | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month Exponential Smoothing Forecasts Figure 8.3

    42. Adjusted Exponential Smoothing AFt +1 = Ft +1 + Tt +1 where T = an exponentially smoothed trend factor Tt +1 = (Ft +1 - Ft) + (1 - ) Tt where Tt= the last period trend factor = a smoothing constant for trend

    43. Adjusted Exponential Smoothing Example

    44. PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 Adjusted Exponential Smoothing Example Example 8.4

    45. PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 T3 = (F3 - F2) + (1 - ) T2 = (0.30)(38.5 - 37.0) + (0.70)(0) = 0.45 AF3 = F3 + T3 = 38.5 + 0.45 = 38.95 T13 = (F13 - F12) + (1 - ) T12 = (0.30)(53.61 - 53.21) + (0.70)(1.77) = 1.36 AF13 = F13 + T13 = 53.61 + 1.36 = 54.96 Adjusted Exponential Smoothing Example Example 8.4

    46. Adjusted Exponential Smoothing Example FORECAST TREND ADJUSTED PERIOD MONTH DEMAND Ft +1 Tt +1 FORECAST AFt +1 1 Jan 37 37.00 – – 2 Feb 40 37.00 0.00 37.00 3 Mar 41 38.50 0.45 38.95 4 Apr 37 39.75 0.69 40.44 5 May 45 38.37 0.07 38.44 6 Jun 50 38.37 0.07 38.44 7 Jul 43 45.84 1.97 47.82 8 Aug 47 44.42 0.95 45.37 9 Sep 56 45.71 1.05 46.76 10 Oct 52 50.85 2.28 58.13 11 Nov 55 51.42 1.76 53.19 12 Dec 54 53.21 1.77 54.98 13 Jan – 53.61 1.36 54.96 Example 8.4

    47. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Demand | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Period Adjusted Exponential Smoothing Forecasts Example 8.4

    48. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Actual Demand Forecast ( = 0.50) | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Period Adjusted Exponential Smoothing Forecasts Example 8.4

    49. 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – Adjusted forecast ( = 0.30) Actual Demand Forecast ( = 0.50) | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Period Adjusted Exponential Smoothing Forecasts Example 8.4

    50. Linear Trend Line y = a + bx where a = intercept (at period 0) b = slope of the line x = the time period y = forecast for demand for period x