8.Demand Forecasting in a Supply Chain

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Demand Forecasting in a Supply Chain Supply Chain Management

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Transcript of 8.Demand Forecasting in a Supply Chain

Page 1: 8.Demand Forecasting in a Supply Chain

Demand Forecastingin a Supply Chain

Supply Chain Management

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© 2007 Pearson Education 7-2

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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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

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© 2007 Pearson Education 7-4

Characteristics of Forecasts

Forecasts are always wrong. Should include expected value and measure of error.

Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)

Aggregate forecasts are more accurate than disaggregate forecasts

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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

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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

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Time Series ForecastingQuarter Demand Dt

II, 1998 8000III, 1998 13000IV, 1998 23000I, 1999 34000II, 1999 10000III, 1999 18000IV, 1999 23000I, 2000 38000II, 2000 12000III, 2000 13000IV, 2000 32000I, 2001 41000

Forecast demand for thenext four quarters.

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Time Series Forecasting

0

10,000

20,000

30,000

40,000

50,000

97,2

97,3

97,4

98,1

98,2

98,3

98,4

99,1

99,2

99,3

99,4

00,1

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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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Basic Approach toDemand 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

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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

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Static Methods

Estimating level and trend Estimating seasonal factors

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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) – the number of periods after which the seasonal cycle

repeats itself

– for demand at Tahoe Salt (Table 7.1, Figure 7.1) p = 4

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Time Series Forecasting (Table 7.1)

Quarter Demand Dt

II, 1998 8000III, 1998 13000IV, 1998 23000I, 1999 34000II, 1999 10000III, 1999 18000IV, 1999 23000I, 2000 38000II, 2000 12000III, 2000 13000IV, 2000 32000I, 2001 41000

Forecast demand for thenext four quarters.

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Time Series Forecasting(Figure 7.1)

0

10,000

20,000

30,000

40,000

50,000

97,2

97,3

97,4

98,1

98,2

98,3

98,4

99,1

99,2

99,3

99,4

00,1

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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) – the number of periods after which the seasonal cycle

repeats itself

– for demand at Tahoe Salt (Table 7.1, Figure 7.1) p = 4

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Deseasonalizing Demand

[Dt-(p/2) + Dt+(p/2) + 2Di] / 2p for p even

Dt = (sum is from i = t+1-(p/2) to t+1+(p/2))

Di / p for p odd

(sum is from i = t-(p/2) to t+(p/2)), p/2 truncated to lower integer

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Deseasonalizing Demand

For the example, p = 4 is even

For t = 3:

D3 = {D1 + D5 + Sum(i=2 to 4) [2Di]}/8

= {8000+10000+[(2)(13000)+(2)(23000)+(2)(34000)]}/8

= 19750

D4 = {D2 + D6 + Sum(i=3 to 5) [2Di]}/8

= {13000+18000+[(2)(23000)+(2)(34000)+(2)(10000)]/8

= 20625

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Deseasonalizing Demand

Then include trend

Dt = L + tT

where Dt = deseasonalized demand in period t

L = level (deseasonalized demand at period 0)

T = trend (rate of growth of deseasonalized demand)

Trend is determined by linear regression using deseasonalized demand as the dependent variable and period as the independent variable (can be done in Excel)

In the example, L = 18,439 and T = 524

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Time Series of Demand(Figure 7.3)

0

10000

20000

30000

40000

50000

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

an

d

Dt

Dt-bar

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Measures of Forecast Error

Forecast error = Et = Ft - Dt

Mean squared error (MSE)

MSEn = (Sum(t=1 to n)[Et2])/n

Absolute deviation = At = |Et|

Mean absolute deviation (MAD)

MADn = (Sum(t=1 to n)[At])/n

= 1.25MAD

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Forecasting Demand at Tahoe Salt

Moving average Simple exponential smoothing Trend-corrected exponential smoothing Trend- and seasonality-corrected exponential

smoothing

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Forecasting in Practice

Collaborate in building forecasts The value of data depends on where you are in the

supply chain Be sure to distinguish between demand and sales

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Summary of Learning Objectives

What are the roles of forecasting for an enterprise and a supply chain?

What are the components of a demand forecast? How is demand forecast given historical data using

time series methodologies? How is a demand forecast analyzed to estimate

forecast error?