supplychain management ppt_ch07

– Multiplicative: (level)(trend)(seasonal factor) – Additive: level + trend + seasonal factor – Mixed: (level + trend)(seasonal factor)
Static methods Adaptive forecasting
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Forecasting Methods
Static Adaptive
– – – – Moving average Simple exponential smoothing Holt’s model (with trend) Winter’s model (with trend and seasonality)
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Forecast demand for the next four quarters.
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Time Series Forecasting (Figure 7.1)
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Estimating Level and Trend
Before estimating level and trend, demand data must be deseasonalized Deseasonalized demand = demand that would have been observed in the absence of seasonal fluctuations Periodicity (p)
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Forecasting Methods
Qualitative: primarily subjective; rely on judgment and opinion Time Series: use historical demand only
– Production: scheduling, inventory, aggregate planning – Marketing: sales force allocation, promotions, new production introduction – Finance: plant/equipment investment, budgetary planning – Personnel: workforce planning, hiring, layoffs
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Static Methods
Estimating level and trend Estimating seasonal factors
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– 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
Forecast demand for the next four quarters.
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Time Series Forecasting
60,000
40,000 20,000 0
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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
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
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Time Series Forecasting (Table 7.1)
Quarter, Year Demand Dt II, 1 8000 III, 1 13000 IV, 1 23000 I, 2 34000 II, 2 10000 III, 2 18000 IV, 2 23000 I, 3 38000 II, 3 12000 III, 3 13000 IV, 3 32000 I, 4 41000
Chapter 7 Demand Forecasting in a Supply Chain
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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
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Components of an Observation
Observed demand (O) = Systematic component (S) + Random component (R)
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Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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:
50,000 40,000 30,000 20,000 10,0Байду номын сангаас0 0
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Time Series Forecasting Methods
Goal is to predict systematic component of demand
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Static Methods
Assume a mixed model: Systematic component = (level + trend)(seasonal factor) Ft+l = [L + (t + l)T]St+l = forecast in period t for demand in period t + l L = estimate of level for period 0 T = estimate of trend St = estimate of seasonal factor for period t Dt = actual demand in period t Ft = forecast of demand in period t