Operation management 6e by russel and taylor ch12

Chapter 12
Forecasting
Operations
Operations Management
Management -- 66thth Edition
Edition
Roberta Russell & Bernard W. Taylor, III

Copyright 2009 John Wiley & Sons, Inc.

Beni Asllani
University of Tennessee at Chattanooga


Lecture Outline
 Strategic Role of Forecasting in Supply
Chain Management
 Components of Forecasting Demand
 Time Series Methods
 Forecast Accuracy
 Time Series Forecasting Using Excel
 Regression Methods
Copyright 2009 John Wiley & Sons, Inc.

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Forecasting
 Predicting the future
 Qualitative forecast methods


subjective

 Quantitative forecast
methods


based on mathematical
formulas

Copyright 2009 John Wiley & Sons, Inc.

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Forecasting and Supply Chain
Management
 Accurate forecasting determines how much
inventory a company must keep at various points
along its supply chain
 Continuous replenishment





supplier and customer share continuously updated data
typically managed by the supplier
reduces inventory for the company
speeds customer delivery

 Variations of continuous replenishment





quick response
JIT (just-in-time)
VMI (vendor-managed inventory)
stockless inventory

Copyright 2009 John Wiley & Sons, Inc.

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Forecasting
 Quality Management


Accurately forecasting customer demand is
a key to providing good quality service

 Strategic Planning


Successful strategic planning requires
accurate forecasts of future products and
markets

Copyright 2009 John Wiley & Sons, Inc.

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Types of Forecasting Methods
 Depend on




time frame
demand behavior
causes of behavior

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Time Frame
 Indicates how far into the future is
forecast


Short- to mid-range forecast


typically encompasses the immediate future

 daily up to two years


Long-range forecast


usually encompasses a period of time longer
than two years

Copyright 2009 John Wiley & Sons, Inc.

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Demand Behavior
 Trend


a gradual, long-term up or down movement of
demand

 Random variations


movements in demand that do not follow a pattern

 Cycle


an up-and-down repetitive movement in demand

 Seasonal pattern


an up-and-down repetitive movement in demand
occurring periodically

Copyright 2009 John Wiley & Sons, Inc.

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Demand

Demand

Forms of Forecast Movement

Random
movement
Time
(b) Cycle

Demand

Demand

Time
(a) Trend

Time
(c) Seasonal pattern

Copyright 2009 John Wiley & Sons, Inc.

Time
(d) Trend with seasonal pattern

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Forecasting Methods
 Time series


statistical techniques that use historical demand data
to predict future demand

 Regression methods


attempt to develop a mathematical relationship
between demand and factors that cause its behavior

 Qualitative


use management judgment, expertise, and opinion to
predict future demand

Copyright 2009 John Wiley & Sons, Inc.

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Qualitative Methods
 Management, marketing, purchasing,
and engineering are sources for internal
qualitative forecasts
 Delphi method


involves soliciting forecasts about
technological advances from experts

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Forecasting Process
1. Identify the
purpose of forecast

2. Collect historical
data

3. Plot data and identify
patterns

6. Check forecast
accuracy with one or
more measures

5. Develop/compute
forecast for period of
historical data

4. Select a forecast
model that seems
appropriate for data

7.
Is accuracy of
forecast
acceptable?

No

8b. Select new
forecast model or
adjust parameters of
existing model

Yes
8a. Forecast over
planning horizon

9. Adjust forecast based
on additional qualitative

information and insight

Copyright 2009 John Wiley & Sons, Inc.

10. Monitor results
and measure forecast
accuracy

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Time Series
 Assume that what has occurred in the past will
continue to occur in the future
 Relate the forecast to only one factor - time
 Include




moving average
exponential smoothing
linear trend line

Copyright 2009 John Wiley & Sons, Inc.

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Moving Average

 Naive forecast


demand in current period is used as next period’s
forecast

 Simple moving average




uses average demand for a fixed sequence of
periods
stable demand with no pronounced behavioral
patterns

 Weighted moving average


weights are assigned to most recent data

Copyright 2009 John Wiley & Sons, Inc.

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Moving Average:
Naïve Approach
MONTH
Jan

Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov

ORDERS
FORECAST
PER MONTH
120
90
120
100
90
75
100
110
75
50
110
75
50
130
75
110

130
90
110
90

Copyright 2009 John Wiley & Sons, Inc.

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Simple Moving Average
n

Σ

i = 1 Di

MAn =

n

where
n
Di

= number of periods
in the moving
average
= demand in period i

Copyright 2009 John Wiley & Sons, Inc.

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3-month Simple Moving Average
MOVING
AVERAGE

ORDERS
MONTH
PER
Jan
–
MONTH
120
–
Feb
–
90
103.3
Mar
88.3
100
95.0
Apr
78.3
75
78.3
May

85.0
110
105.0
June
110.0
50
July
Copyright 2009 John Wiley & Sons, Inc.

3

Σ

i=1

MA3 =
=

Di

3
90 + 110 + 130
3

= 110 orders
for Nov

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5-month Simple Moving Average
MOVING
AVERAGE

ORDERS
MONTH
PER
Jan
–
MONTH
120
–
Feb
–
90
–
Mar
–
100
99.0
Apr
85.0
75
82.0
May
88.0
110
95.0
June
91.0

50
July
Copyright 2009 John Wiley & Sons, Inc.

5

Σ

i=1

MA5 =
=

Di

5

90 + 110 + 130+75+50
5
= 91 orders
for Nov

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Smoothing Effects
150 –
125 –

5-month

Orders

100 –
75 –
50 –
3-month

25 –
Actual

0–
|
Jan

|
Feb

|
Mar

|
Apr

|
|
|
May June July
Month

Copyright 2009 John Wiley & Sons, Inc.

|
|
Aug Sept

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|
Oct

|
Nov


Weighted Moving Average
n

 Adjusts
moving
average
method to
more closely
reflect data
fluctuations

WMAn = Σ Wi Di
i=1
i=1

where

Wi = the weight for period i,
between 0 and 100
percent

Σ W = 1.00
i

Copyright 2009 John Wiley & Sons, Inc.

12-20


Weighted Moving Average Example
MONTH

WEIGHT

DATA

August
September
October

17%
33%
50%

130

110
90

November Forecast

3

WMA3 = iΣ
= 1 Wi Di

= (0.50)(90) + (0.33)(110) + (0.17)(130)
= 103.4 orders
Copyright 2009 John Wiley & Sons, Inc.

12-21


Exponential Smoothing






Averaging method
Weights most recent data more strongly
Reacts more to recent changes
Widely used, accurate method

Copyright 2009 John Wiley & Sons, Inc.

12-22


Exponential Smoothing (cont.)
Ft +1 = α Dt + (1 - α)Ft
where:
Ft +1 = forecast for next period
Dt =

actual demand for present period

Ft = previously determined forecast
for present period
α = weighting factor, smoothing constant
Copyright 2009 John Wiley & Sons, Inc.

12-23


Effect of Smoothing Constant
0.0 ≤ α ≤ 1.0
If α = 0.20, then Ft +1 = 0.20 Dt + 0.80 Ft
If α = 0, then Ft +1 = 0 Dt + 1 Ft = Ft
Forecast does not reflect recent data

If α = 1, then Ft +1 = 1 Dt + 0 Ft = Dt
Forecast based only on most recent data
Copyright 2009 John Wiley & Sons, Inc.

12-24


Exponential Smoothing (α=0.30)
PERIOD
DEMAND

MONTH

F2 = α D1 + (1 - α )F1
= (0.30)(37) + (0.70)(37)

1

Jan

37

= 37

2

Feb

40

F3 = α D2 + (1 - α )F2

3

Mar

41

4

Apr

37

5

May

45

= (0.30)(40) + (0.70)(37)
= 37.9
F13 = α D12 + (1 - α )F12
= (0.30)(54) + (0.70)(50.84)
= 51.79

6

Jun

50

7
JulJohn Wiley43

Copyright
2009
& Sons, Inc.

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