In this chapter, the following content will be discussed: Qualitative methods, is time series pattern forecastable? time series, moving average, exponential smoothing formulas, classic time series decomposition model. | 1-1 Logistics Management LSM 730 Dr. Khurrum S. Mughal Lecture 22 1 Qualitative Methods Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts Delphi method involves soliciting forecasts about technological advances from experts 12-2 8-3 CR (2004) Prentice Hall, Inc. Is Time Series Pattern Forecastable? Whether a time series can be reasonably forecasted often depends on the time series’ degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference: Rule A time series is lumpy if where regular, otherwise. Time Series Assume that what has occurred in the past will continue to occur in the future Relate the forecast to only one factor - time Include moving average exponential smoothing linear trend line 12-4 Moving Average Naive forecast demand in current period is used as next period’s forecast Simple moving average uses average demand for a fixed sequence of periods stable demand . | 1-1 Logistics Management LSM 730 Dr. Khurrum S. Mughal Lecture 22 1 Qualitative Methods Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts Delphi method involves soliciting forecasts about technological advances from experts 12-2 8-3 CR (2004) Prentice Hall, Inc. Is Time Series Pattern Forecastable? Whether a time series can be reasonably forecasted often depends on the time series’ degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference: Rule A time series is lumpy if where regular, otherwise. Time Series Assume that what has occurred in the past will continue to occur in the future Relate the forecast to only one factor - time Include moving average exponential smoothing linear trend line 12-4 Moving Average Naive forecast demand in current period is used as next period’s forecast Simple moving average uses average demand for a fixed sequence of periods stable demand with no pronounced behavioral patterns Weighted moving average weights are assigned to most recent data 12-5 Moving Average: Naïve Approach 12-6 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 ORDERS MONTH PER MONTH - 120 90 100 75 110 50 75 130 110 90 Nov - FORECAST Simple Moving Average 12-7 MAn = n i = 1 Di n where n = number of periods in the moving average Di = demand in period i 3-month Simple Moving Average 12-8 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - ORDERS MONTH PER MONTH MA3 = 3 i = 1 Di 3 = 90 + 110 + 130 3 = 110 orders for Nov – – – MOVING AVERAGE 5-month Simple Moving Average 12-9 MA5 = 5 i = 1 Di 5 = 90 + 110 + 130+75+50 5 = 91 orders for Nov Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - ORDERS MONTH PER MONTH – – – – – MOVING AVERAGE 8-10 Weighted Moving Average period current in .
