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Texas A&M Industrial Engineering
ISEN 220 Introduction to Production and Manufacturing Systems Dr. Gary Gaukler 12/3/2018 Texas A&M Industrial Engineering 1
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?? What is Forecasting? Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities ?? 2
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Forecasting Approaches
Qualitative Methods Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet 3
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Forecasting Approaches
Quantitative Methods Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions 4
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∑ demand in previous n periods
Moving Average Method MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Moving average = ∑ demand in previous n periods n 5
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Moving Average Example
January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Sales Moving Average
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Graph of Moving Average
Moving Average Forecast | | | | | | | | | | | | J F M A M J J A S O N D Shed Sales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales 7
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Potential Problems With Moving Average
Increasing n smooths the forecast but makes it less sensitive to changes Does not forecast trends well Requires extensive historical data 8
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Exponential Smoothing
Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data 9
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Exponential Smoothing
New forecast = last period’s forecast + a (last period’s actual demand – last period’s forecast) Ft = Ft – 1 + a(At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast a = smoothing (or weighting) constant (0 a 1) 10
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 11
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