Overview of Quantitative Approaches

Overview of Quantitative Approaches - þ Sometimes cost...

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1 Overview of Quantitative Approaches Time Series Naive approach Moving averages Exponential smoothing Trend projection Linear regression Associate Regression
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2 þ Set of evenly spaced numerical data þ Obtained by observing response variable at regular time periods þ Forecast based only on past values þ Assumes that factors influencing past and present will continue influence in future Time Series Forecasting
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3 Trend Seasonal Cyclical Random Time Series Components
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4 Components of Demand Demand for product or service | | | | 1 2 3 4 Year Average demand over four years Seasonal peaks Trend component Actual demand Random variation Figure 4.1
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5 Naive Approach þ Assumes demand in next period is the same as demand in most recent period þ e.g., If May sales were 48, then June sales will be 48
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Unformatted text preview: þ Sometimes cost effective and efficient 6 þ MA is a series of arithmetic means þ Used if little or no trend þ Used often for smoothing þ Provides overall impression of data over time Moving Average Method Moving average = ∑ demand in previous n periods n 7 January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3 Moving Average Example (10 + 12 + 13)/3 = 11 2/3 8 Graph of Moving Average | | | | | | | | | | | | 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 Moving Average Forecast...
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