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Unformatted text preview: ESI 6323 Demand Forecasting in a Supply Chain 1 Outline The role of forecasting in a supply chain Characteristics of forecasts Components of forecasts and forecasting methods Basic approach to demand forecasting Time series forecasting methods Measures of forecast error Forecasting demand at Tahoe Salt Forecasting in practice 2 Role of Forecasting in a Supply Chain The basis for all strategic and planning decisions in a supply chain Used for both push and pull processes Examples: Production: scheduling, inventory, aggregate planning Marketing: sales force allocation, promotions, new production introduction Finance: plant/equipment investment, budgetary planning Personnel: workforce planning, hiring, layoffs All of these decisions are interrelated 3 Characteristics of Forecasts Forecasts are always wrong. Should include expected value and measure of error. Longterm forecasts are less accurate than shortterm forecasts (forecast horizon is important) Aggregate forecasts are more accurate than disaggregate forecasts 4 Forecasting Methods Qualitative: primarily subjective; rely on judgment and opinion Time Series: use historical demand only Static Adaptive Causal: use the relationship between demand and some other factor to develop forecast Simulation Imitate consumer choices that give rise to demand Can combine time series and causal methods 5 Components of an Observation Observed demand (O) = Systematic component (S) + Random component (R) Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal fluctuation) Systematic component: Expected value of demand Random component: The part of the forecast that deviates from the systematic component Forecast error: difference between forecast and actual demand 6 Time Series Forecasting Forecast demand for the next four quarters. Quarter Demand D t II, 2006 8000 III, 2006 13000 IV, 2006 23000 I, 2007 34000 II, 2007 10000 III, 2007 18000 IV, 2007 23000 I, 2008 38000 II, 2008 12000 III, 2008 13000 IV, 2008 32000 I, 2009 41000 7 Time Series Forecasting 20,000 40,000 60,000 8 Forecasting Methods Static Adaptive Moving average Simple exponential smoothing Holts model ( with trend ) Winters model ( with trend and seasonality ) 9 Basic Approach to Demand Forecasting Understand the objectives of forecasting Integrate demand planning and forecasting Identify major factors that influence the demand forecast Understand and identify customer segments Determine the appropriate forecasting technique Establish performance and error measures for the forecast 10 Time Series Forecasting Methods Goal is to predict systematic component of demand Multiplicative: (level)(trend)(seasonal factor) Additive: level + trend + seasonal factor Mixed: (level + trend)(seasonal factor) Static methods Adaptive forecasting 11 Static Methods Assume a mixed model:...
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This note was uploaded on 03/02/2012 for the course ESI 6323 taught by Professor Guan during the Spring '09 term at University of Florida.
 Spring '09
 Guan

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