运营管理11课件.ppt

Beni AsllaniUniversity of Tennessee at Chattanooga;Lecture Outline;Forecasting;Forecasting and Supply Chain Management;Forecasting and TQM;Types of Forecasting Methods;Time Frame;Demand Behavior;Time (a) Trend;Forecasting Methods;Qualitative Methods;Forecasting Process;Time Series;Moving Average;Moving Average:Na?ve Approach;Simple Moving Average ;3-month Simple Moving Average;5-month Simple Moving Average;Smoothing Effects;Weighted Moving Average;Weighted Moving Average Example;Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method;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;Effect of Smoothing Constant;F2 = ?D1 + (1 - ?)F1 = (0.30)(37) + (0.70)(37) = 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;70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 –;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;Adjusted Exponential Smoothing (β=0.30);Adjusted Exponential Smoothing: Example;Adjusted Exponential Smoothing Forecasts;y = a + bx where a = intercept b = slope of the line x = time period y = forecast for demand for period x;Least Squares Example;x = = 6.5 y = = 46.42 b = = =1.72 a = y - bx = 46.42 - (1.72)(6.5) = 35.2;Linear trend line;Seasonal Adjustments;Seasonal Adjustment (cont.);Seasonal Adjustment (cont.);Forecast Accurac