global supply chain management lecture 5.pptx

7/30/2019 Global Supply Chain Management Lecture 5.pptx

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Supply Chain Management: Strategy,Planning, Operations (3/e)

Sunil Chopra & Peter Meindl

Chapter 7

Lecture 5Demand Forecasting in a Supply Chain



Role of Forecasting in a Supply

Chain

The basis for all strategic and planning decisionsin a supply chain

Used for both push and pull processes

Examples: Production: scheduling, inventory, aggregate

planning

Marketing: sales force allocation, promotions, newproduction introduction

Finance: plant/equipment investment, budgetaryplanning

Personnel: workforce planning, hiring, layoffs

All of these decisions are interrelated

2Global Supply Chain Management: Lecture 5

EMBA IB



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


EMBA IB



Forecasting Methods

Qualitative: primarily subjective; rely on judgmentand opinion

Time Series: use historical demand only

Static Adaptive

Causal: use the relationship between demandand 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 8000

III, 1998 13000

IV, 1998 23000

I, 1999 34000II, 1999 10000

III, 1999 18000

IV, 1999 23000

I, 2000 38000II, 2000 12000

III, 2000 13000

IV, 2000 32000

I, 2001 41000

Forecast demand fo r th

next four quarters.


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Time Series Forecasting

0

10,000

20,000

30,000

40,000

50,000

9 7 , 2

9 7 , 3

9 7 , 4

9 8 , 1

9 8 , 2

9 8 , 3

9 8 , 4

9 9 , 1

9 9 , 2

9 9 , 3

9 9 , 4

0 0 , 1



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 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


EMBA IB



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

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


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Static Methods

Estimating level and trend

Estimating seasonal factors

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Global Supply Chain Management: Lecture 5

EMBA IB



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 seasonalfluctuations

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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EMBA IB




(Table 7.1)

Quarter Demand Dt

II, 1998 8000

III, 1998 13000

IV, 1998 23000

I, 1999 34000II, 1999 10000

III, 1999 18000

IV, 1999 23000

I, 2000 38000II, 2000 12000

III, 2000 13000

IV, 2000 32000

I, 2001 41000

Forecast demand fo r th

next four quarters.

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EMBA IB




0

10,000

20,000

30,000

40,000

50,000

9 7 , 2

9 7 , 3

9 7 , 4

9 8 , 1

9 8 , 2

9 8 , 3

9 8 , 4

9 9 , 1

9 9 , 2

9 9 , 3

9 9 , 4

0 0 , 1



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 seasonalfluctuations

Periodicity ( p)

the number of periods after which the seasonal

cycle repeats itself

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EMBA IB



Deseasonalizing Demand

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

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

S 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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EMBA IB




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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EMBA IB




Then include trend

Dt = L + t T

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 donein Excel)

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

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EMBA IB



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

D e m a n d

Dt

Dt-bar

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Estimating Seasonal Factors

Use the previous equation to calculate

deseasonalized demand for each period

St = Dt / Dt = seasonal factor for period t

In the example,D2 = 18439 + (524)(2) = 19487 D2 = 13000

S2 = 13000/19487 = 0.67

The seasonal factors for the other periods arecalculated in the same manner

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EMBA IB




(Fig. 7.4)

t Dt Dt-bar S-bar

1 8000 18963 0.42 = 8000/18963

2 13000 19487 0.67 = 13000/19487

3 23000 20011 1.15 = 23000/20011

4 34000 20535 1.66 = 34000/20535

5 10000 21059 0.47 = 10000/21059

6 18000 21583 0.83 = 18000/21583

7 23000 22107 1.04 = 23000/22107

8 38000 22631 1.68 = 38000/22631

9 12000 23155 0.52 = 12000/23155

10 13000 23679 0.55 = 13000/23679

11 32000 24203 1.32 = 32000/24203

12 41000 24727 1.66 = 41000/24727

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The overall seasonal factor for a “season” is then obtainedby averaging all of the factors for a “season”

If there are r seasonal cycles, for all periods of the form pt+i , 1<i<p, the seasonal factor for season i is

Si = [Sum(j=0 to r-1) S jp+i ]/r In the example, there are 3 seasonal cycles in the dataand p=4, so

S1 = (0.42+0.47+0.52)/3 = 0.47

S2 = (0.67+0.83+0.55)/3 = 0.68S3 = (1.15+1.04+1.32)/3 = 1.17

S4 = (1.66+1.68+1.66)/3 = 1.67

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EMBA IB



Estimating the Forecast

Using the original equation, we can forecast the next

four periods of demand:

F13 = (L+13T)S1 = [18439+(13)(524)](0.47) = 11868F14 = (L+14T)S2 = [18439+(14)(524)](0.68) = 17527

F15 = (L+15T)S3 = [18439+(15)(524)](1.17) = 30770

F16 = (L+16T)S4 = [18439+(16)(524)](1.67) = 44794

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EMBA IB



Adaptive Forecasting

The estimates of level, trend, and seasonality are

adjusted after each demand observation

General steps in adaptive forecasting

Moving average Simple exponential smoothing

Trend-corrected exponential smoothing (Holt’s

model)

Trend- and seasonality-corrected exponential

smoothing (Winter’s model)

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EMBA IB



Basic Formula for


Ft+1 = (Lt + l T)St+1 = forecast for period t+l in period t

Lt = Estimate of level at the end of period t

Tt = Estimate of trend at the end of period t

St = Estimate of seasonal factor for period t Ft = Forecast of demand for period t (made period t -1

or earlier)

Dt = Actual demand observed in period t

Et = Forecast error in period t At = Absolute deviation for period t = |Et |

MAD = Mean Absolute Deviation = average value of At

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EMBA IB



General Steps in


Initialize: Compute initial estimates of level (L0), trend

(T0), and seasonal factors (S1,…,Sp). This is done as

in static forecasting.

Forecast: Forecast demand for period t+1 using thegeneral equation

Estimate error: Compute error Et+1 = Ft+1- Dt+1

Modify estimates: Modify the estimates of level (Lt +1),

trend (Tt +1), and seasonal factor (St+p+1), given theerror Et +1 in the forecast

Repeat steps 2, 3, and 4 for each subsequent period

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EMBA IB



Moving Average Used when demand has no observable trend or seasonality Systematic component of demand = level

The level in period t is the average demand over the last Nperiods (the N-period moving average)

Current forecast for all future periods is the same and is basedon the current estimate of the level

Lt = (Dt + Dt-1 + … + Dt-N+1) / N

Ft+1 = Lt and Ft+n = Lt

After observing the demand for period t +1, revise the

estimates as follows:Lt+1 = (Dt+1 + Dt + … + Dt-N+2) / N

Ft+2 = Lt+1

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EMBA IB



Moving Average Example

From Tahoe Salt example (Table 7.1)

At the end of period 4, what is the forecast demand for periods5 through 8 using a 4-period moving average?

L4 = (D4+D3+D2+D1)/4 = (34000+23000+13000+8000)/4 =

19500F5 = 19500 = F6 = F7 = F8

Observe demand in period 5 to be D5 = 10000

Forecast error in period 5, E5 = F5 - D5 = 19500 - 10000 =

9500Revise estimate of level in period 5:

L5 = (D5+D4+D3+D2)/4 = (10000+34000+23000+13000)/4 =20000

F6 = L5 = 2000029


EMBA IB



Simple Exponential Smoothing

Used when demand has no observable trend or seasonality

Systematic component of demand = level

Initial estimate of level, L0, assumed to be the average of all

historical data

L0 = [Sum(i=1 to n)Di]/n

Current forecast for all future periods is equal to the current

estimate of the level and is given as follows:

Ft+1 = Lt and Ft+n = Lt

After observing demand Dt+1, revise the estimate of the level:

Lt+1 = aDt+1 + (1-a)Lt

Lt+1 = Sum(n=0 to t+1)[a(1-a)nDt+1-n ]

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EMBA IB



Simple Exponential Smoothing

Example

From Tahoe Salt data, forecast demand for period 1 usingexponential smoothing

L0 = average of all 12 periods of data

= Sum(i=1 to 12)[Di]/12 = 22083

F1 = L0 = 22083

Observed demand for period 1 = D1 = 8000Forecast error for period 1, E1, is as follows:

E1 = F1 - D1 = 22083 - 8000 = 14083

Assuming a = 0.1, revised estimate of level for period 1:

L1 = aD1 + (1-a)L0 = (0.1)(8000) + (0.9)(22083) = 20675

F2 = L1 = 20675Note that the estimate of level for period 1 is lower than in period 0

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EMBA IB



Trend-Corrected Exponential

Smoothing (Holt’s Model)

Appropriate when the demand is assumed to have a leveland trend in the systematic component of demand but noseasonality

Obtain initial estimate of level and trend by running a linear

regression of the following form:Dt = at + b

T0 = a

L0 = b

In period t, the forecast for future periods is expressed asfollows:

Ft+1 = Lt + Tt

Ft+n = Lt + nTt

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EMBA IB



Trend-Corrected Exponential

Smoothing (Holt’s Model)

After observing demand for period t, revise the estimates for leveland trend as follows:

Lt+1 = aDt+1 + (1-a)(Lt + Tt)

Tt+1 = b(Lt+1 - Lt) + (1-b)Tt

a = smoothing constant for levelb = smoothing constant for trend

Example: Tahoe Salt demand data. Forecast demand for period 1using Holt’s model (trend corrected exponential smoothing)

Using linear regression,

L0 = 12015 (linear intercept)

T0 = 1549 (linear slope)

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EMBA IB

H lt’ M d l E l



Holt’s Model Example

(continued)

Forecast for period 1:

F1 = L0 + T0 = 12015 + 1549 = 13564

Observed demand for period 1 = D1 = 8000

E1 = F1 - D1 = 13564 - 8000 = 5564

Assume a = 0.1, b = 0.2

L1 = aD1 + (1-a)(L0+T0) = (0.1)(8000) + (0.9)(13564) =13008

T1 = b(L1 - L0) + (1-b)T0 = (0.2)(13008 - 12015) +(0.8)(1549)

= 1438

F2 = L1 + T1 = 13008 + 1438 = 14446

F5 = L1 + 4T1 = 13008 + (4)(1438) = 1876034


EMBA IB



Trend- and Seasonality-Corrected

Exponential Smoothing

Appropriate when the systematic component of demand is assumed to have a level, trend, and

seasonal factor

Systematic component = (level+trend)(seasonal

factor)

Assume periodicity p

Obtain initial estimates of level (L0), trend (T0),

seasonal factors (S1,…,Sp) using procedure for staticforecasting

In period t, the forecast for future periods is given by:

Ft+1 = (Lt+Tt)(St+1) and Ft+n = (Lt + nTt)St+n 35


EMBA IB



Trend- and Seasonality-Corrected

Exponential Smoothing (continued)

After observing demand for period t+1, revise estimates for level,trend, and seasonal factors as follows:

Lt+1 = a(Dt+1/St+1) + (1-a)(Lt+Tt)

Tt+1 = b(Lt+1 - Lt) + (1-b)Tt

St+p+1 = g(Dt+1/Lt+1) + (1-g)St+1

a = smoothing constant for level

b = smoothing constant for trend

g = smoothing constant for seasonal factor

Example: Tahoe Salt data. Forecast demand for period 1 using

Winter’s model. Initial estimates of level, trend, and seasonal factors are obtained

as in the static forecasting case

36


EMBA IB



Trend- and Seasonality-Corrected Exponential

Smoothing Example (continued)

L0 = 18439 T0 = 524 S1=0.47, S2=0.68, S3=1.17, S4=1.67

F1 = (L0 + T0)S1 = (18439+524)(0.47) = 8913

The observed demand for period 1 = D1 = 8000

Forecast error for period 1 = E1 = F1-D1 = 8913 - 8000 = 913

Assume a = 0.1, b=0.2, g=0.1; revise estimates for level and trendfor period 1 and for seasonal factor for period 5

L1 = a(D1/S1)+(1-a)(L0+T0) = (0.1)(8000/0.47)+(0.9)(18439+524)=18769

T1 = b(L1-L0)+(1-b)T0 = (0.2)(18769-18439)+(0.8)(524) = 485

S5 = g(D1/L1)+(1-g)S1 = (0.1)(8000/18769)+(0.9)(0.47) = 0.47

F2 = (L1+T1)S2 = (18769 + 485)(0.68) = 13093

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EMBA IB



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

s = 1.25MAD

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EMBA IB



Measures of Forecast Error

Mean absolute percentage error (MAPE)

MAPEn = (Sum(t=1 to n)[|Et/ Dt|100])/n

Bias

Shows whether the forecast consistently under- or

overestimates demand; should fluctuate around 0biasn = Sum(t=1 to n)[Et]

Tracking signal

Should be within the range of +6

Otherwise, possibly use a new forecasting method

TSt = bias / MADt

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EMBA IB

F ti D d t T h



Forecasting Demand at Tahoe

Salt

Moving average

Simple exponential smoothing

Trend-corrected exponential smoothing

Trend- and seasonality-corrected exponentialsmoothing

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EMBA IB



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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EMBA IB

global supply chain management lecture 5.pptx

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