Operations Management 5th Edition Ch 11 - Forecasting by Robert Russell & Bernard W. Taylor

7/22/2019 Operations Management 5th Edition Ch 11 - Forecasting by Robert Russell & Bernard W. Taylor

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Copyright 2006 John Wiley & Sons, Inc.Beni Asllani

University of Tennessee at Chattanooga

Forecasting

Operations Management - 5thEdition

Chapter 11

Roberta Russell & Bernard W. Taylor, III


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Copyright 2006 John Wiley & Sons, Inc. 11-2

Lecture Outline

Strategic Role of Forecasting in Supply

Chain Management and TQM Components of Forecasting Demand

Time Series Methods

Forecast Accuracy Regression Methods


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Forecasting

Predicting the Future

Qualitative forecast methods subjective

Quantitative forecastmethods

based on mathematicalformulas


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Forecasting and Supply Chain

Management Accurate forecasting determines how much

inventory a company must keep at various pointsalong 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


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Forecasting and TQM

Accurate forecasting customer demand is akey to providing good quality service

Continuous replenishment and JITcomplement TQM eliminates the need for buffer inventory, which, in

turn, reduces both waste and inventory costs, aprimary goal of TQM

smoothes process flow with no defective items meets expectations about on-time delivery, which is

perceived as good-quality service


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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



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 demandoccurring periodically



Time(a) Trend

Time(d) Trend with seasonal pattern

Time(c) Seasonal pattern

Time(b) Cycle

Demand

Deman

d

Deman

d

Demand

Randommovement

Forms of Forecast Movement



Forecasting Methods

Qualitative

use management judgment, expertise, and opinion to

predict future demand Time series

statistical techniques that use historical demand datato predict future demand

Regression methods attempt to develop a mathematical relationship

between demand and factors that cause its behavior



Qualitative Methods

Management, marketing, purchasing,

and engineering are sources for internalqualitative forecasts

Delphi method

involves soliciting forecasts abouttechnological advances from experts



Forecasting Process

6. Check forecast

accuracy with one or

more measures

4. Select a forecast

model that seems

appropriate for data

5. Develop/compute

forecast for period of

historical data

8a. Forecast over

planning horizon

9. Adjust forecast based

on additional qualitative

information and insight

10. Monitor results

and measure forecast

accuracy

8b. Select new

forecast model or

adjust parameters of

existing model

7.

Is accuracy of

forecast

acceptable?

1. Identify the

purpose of forecast

3. Plot data and identify

patterns

2. Collect historical

data

No

Yes



Time Series

Assume that what has occurred in the past willcontinue to occur in the future

Relate the forecast to only one factor - time

Include

moving average

exponential smoothing linear trend line



Moving Average

Naive forecast demand the current period is used as next

periods forecast

Simple moving average stable demand with no pronounced

behavioral patterns Weighted moving average

weights are assigned to most recent data



Moving Average:

Nave Approach

Jan 120Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90

ORDERS

MONTH PER MONTH

-120

90

100

75

110

50

75

130

110

90Nov -

FORECAST



Simple Moving Average

MA n=

n

i= 1Di

n

where

n = number of periods inthe moving average

Di= demand in period i



3-month Simple Moving Average

Jan 120Feb 90

Mar 100

Apr 75

May 110

June 50

July 75Aug 130

Sept 110

Oct 90

Nov -

ORDERS

MONTH PER MONTHMA 3=

3

i= 1

Di

3

=90 + 110 + 130

3

= 110 ordersfor Nov

103.3

88.3

95.0

78.378.3

85.0

105.0

110.0

MOVING

AVERAGE



5-month Simple Moving Average

Jan 120Feb 90

Mar 100

Apr 75

May 110

June 50

July 75Aug 130

Sept 110

Oct 90

Nov -

ORDERS

MONTH PER MONTH

MA 5=

5

i= 1

Di

5

=90 + 110 + 130+75+50

5

= 91 orders

for Nov

99.0

85.082.0

88.0

95.0

91.0

MOVING

AVERAGE


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Smoothing Effects

150

125

100

75

50

25

0 | | | | | | | | | | |

Jan Feb Mar Apr May June July Aug Sept Oct Nov

Actual

Orders

Month

5-month

3-month


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Weighted Moving Average

WMA n=i= 1

WiDi

where

Wi= the weight for period i,

between 0 and 100

percent

Wi= 1.00

Adjusts

moving

average

method to

more closely

reflect datafluctuations


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Weighted Moving Average Example

MONTH WEIGHT DATA

Augus t 17% 130

September 33% 110October 50% 90

WMA 3=

3

i= 1WiDi

= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders

November Forecast


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Averaging method

Weights most recent data more strongly

Reacts more to recent changes

Widely used, accurate method

Exponential Smoothing


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Ft +1 = Dt+ (1 - )Ft

where:Ft +1= forecast for next period

Dt = actual demand for present period

Ft= previously determined forecast for

present period

= weighting factor, smoothing constant

Exponential Smoothing (cont.)


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Effect of Smoothing Constant

0.0 1.0

If = 0.20, then Ft +1 = 0.20Dt+ 0.80 FtIf = 0, then Ft+1 = 0Dt+ 1 Ft0 = Ft

Forecast does not reflect recent data

If = 1, then Ft +1 = 1Dt+ 0 Ft=DtForecast based only on most recent data


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

= 37

F3 = D2+ (1 - )F2= (0.30)(40) + (0.70)(37)

= 37.9

F13 = D12+ (1 - )F12= (0.30)(54) + (0.70)(50.84)

= 51.79

Exponential Smoothing (=0.30)

PERIOD MONTH DEMAND

1 Jan 37

2 Feb 40

3 Mar 414 Apr 37

5 May 45

6 Jun 50

7 Jul 43

8 Aug 479 Sep 56

10 Oct 52

11 Nov 55

12 Dec 54


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FORECAST, Ft+ 1

PERIOD MONTH DEMAND (= 0.3) (= 0.5)1 Jan 37

2 Feb 40 37.00 37.00

3 Mar 41 37.90 38.504 Apr 37 38.83 39.75

5 May 45 38.28 38.37

6 Jun 50 40.29 41.68

7 Jul 43 43.20 45.84

8 Aug 47 43.14 44.42

9 Sep 56 44.30 45.71

10 Oct 52 47.81 50.85

11 Nov 55 49.06 51.42

12 Dec 54 50.84 53.21

13 Jan 51.79 53.61

Exponential Smoothing(cont.)


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70

60

50

40

30

20

10

0 | | | | | | | | | | | | |

1 2 3 4 5 6 7 8 9 10 11 12 13

Actual

Orders

Month

Exponential Smoothing (cont.)

= 0.50

= 0.30


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AFt +1= Ft +1+ Tt +1where

T= an exponentially smoothed trend factor

Tt +1= (Ft +1 - Ft) + (1 - ) Ttwhere

Tt= the last period trend factor

= a smoothing constant for trend

Adjusted Exponential Smoothing


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Adjusted Exponential

Smoothing (=0.30)PERIOD MONTH DEMAND

1 Jan 37

2 Feb 40

3 Mar 414 Apr 37

5 May 45

6 Jun 50

7 Jul 43

8 Aug 479 Sep 56

10 Oct 52

11 Nov 55

12 Dec 54

T3 = (F3 - F2) + (1 - ) T2= (0.30)(38.5 - 37.0) + (0.70)(0)

= 0.45

AF3 = F3+ T3 = 38.5 + 0.45

= 38.95

T13 = (F13 - F12) + (1 - ) T12= (0.30)(53.61 - 53.21) + (0.70)(1.77)= 1.36

AF13= F13+ T13 = 53.61 + 1.36 = 54.96


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Adjusted Exponential Smoothing:Example

FORECAST TREND ADJUSTED

PERIOD MONTH DEMAND Ft+1 Tt+1 FORECAST AFt+1

1 Jan 37 37.00

2 Feb 40 37.00 0.00 37.00

3 Mar 41 38.50 0.45 38.954 Apr 37 39.75 0.69 40.44

5 May 45 38.37 0.07 38.44

6 Jun 50 38.37 0.07 38.44

7 Jul 43 45.84 1.97 47.82

8 Aug 47 44.42 0.95 45.379 Sep 56 45.71 1.05 46.76

10 Oct 52 50.85 2.28 58.13

11 Nov 55 51.42 1.76 53.19

12 Dec 54 53.21 1.77 54.98

13 Jan 53.61 1.36 54.96


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Adjusted Exponential SmoothingForecasts

70

60

50

40

30

20

10

0 | | | | | | | | | | | | |

1 2 3 4 5 6 7 8 9 10 11 12 13

Actual

Demand

Period

Forecast (= 0.50)

Adjusted forecast (= 0.30)


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y= a+ bx

where

a = intercept

b = slope of the line

x = time period

y = forecast fordemand for periodx

Linear Trend Line

b =

a =y- b x

wheren = number of periods

x = = mean of thexvalues

y = = mean of theyvalues

xy- nxy

x2- nx2

xn

yn


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Least Squares Example

x(PERIOD) y(DEMAND) xy x2

1 73 37 1

2 40 80 4

3 41 123 9

4 37 148 16

5 45 225 25

6 50 300 36

7 43 301 49

8 47 376 64

9 56 504 81

10 52 520 10011 55 605 121

12 54 648 144

78 557 3867 650


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x = = 6.5

y = = 46.42

b = = =1.72

a = y- bx

= 46.42 - (1.72)(6.5) = 35.2

3867 - (12)(6.5)(46.42)

650 - 12(6.5)2

xy- nxyx2- nx2

78

12

55712

Least Squares Example

(cont.)


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Linear trend line y= 35.2 + 1.72x

Forecast for period 13 y= 35.2 + 1.72(13) = 57.56 units

70

60

50

40

30

20

10

0

| | | | | | | | | | | | |

1 2 3 4 5 6 7 8 9 10 11 12 13

Actual

Demand

Period

Linear trend line


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Seasonal Adjustments

Repetitive increase/ decrease in demand

Use seasonal factor to adjust forecast

Seasonal factor = Si=Di

D


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Seasonal Adjustment (cont.)

2002 12.6 8.6 6.3 17.5 45.0

2003 14.1 10.3 7.5 18.2 50.1

2004 15.3 10.6 8.1 19.6 53.6

Total 42.0 29.5 21.9 55.3 148.7

DEMAND (1000S PER QUARTER)

YEAR 1 2 3 4 Total

S1= = = 0.28D1

D42.0

148.7

S2= = = 0.20D2D

29.5

148.7S4= = = 0.37

D4D55.3

148.7

S3= = = 0.15D3

D21.9

148.7


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Seasonal Adjustment (cont.)

SF1 = (S1) (F5) = (0.28)(58.17) = 16.28

SF2 = (S2) (F5) = (0.20)(58.17) = 11.63

SF3 = (S3) (F5) = (0.15)(58.17) = 8.73

SF4 = (S4) (F5) = (0.37)(58.17) = 21.53

y= 40.97 + 4.30x = 40.97 + 4.30(4) = 58.17

For 2005


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Forecast Accuracy

Forecast error

difference between forecast and actual demand

MAD mean absolute deviation

MAPD

mean absolute percent deviation

Cumulative error Average error or bias


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Mean Absolute Deviation

(MAD)

where

t= period number

Dt= demand in period t

Ft= forecast for period t

n= total number of periods

= absolute value

Dt- Ft

nMAD =


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MAD Example

1 37 37.00

2 40 37.00 3.00 3.00

3 41 37.90 3.10 3.10

4 37 38.83 -1.83 1.83

5 45 38.28 6.72 6.726 50 40.29 9.69 9.69

7 43 43.20 -0.20 0.20

8 47 43.14 3.86 3.86

9 56 44.30 11.70 11.70

10 52 47.81 4.19 4.1911 55 49.06 5.94 5.94

12 54 50.84 3.15 3.15

557 49.31 53.39

PERIOD DEMAND, Dt Ft(=0.3) (Dt- Ft) |Dt- Ft|

Dt- Ft

nMAD =

=

= 4.85

53.39

11


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Other Accuracy Measures

Mean absolute percent deviation (MAPD)

MAPD =

|Dt- Ft|

Dt

Cumulative error

E =et

Average error

E =et

n


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Comparison of Forecasts

FORECAST MAD MAPD E (E)

Exponential smoothing ( = 0.30) 4.85 9.6% 49.31 4.48Exponential smoothing ( = 0.50) 4.04 8.5% 33.21 3.02Adjusted exponential smoothing 3.81 7.5% 21.14 1.92

( = 0.50, = 0.30)Linear trend line 2.29 4.9%


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Forecast Control

Tracking signal monitors the forecast to see if it is biased

high or low

1 MAD 0.8 Control limits of 2 to 5 MADs are used most

frequently

Tracking signal = =(Dt- Ft)

MAD

E

MAD


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Tracking Signal Values

1 37 37.00

2 40 37.00 3.00 3.00 3.00

3 41 37.90 3.10 6.10 3.05

4 37 38.83 -1.83 4.27 2.645 45 38.28 6.72 10.99 3.66

6 50 40.29 9.69 20.68 4.87

7 43 43.20 -0.20 20.48 4.09

8 47 43.14 3.86 24.34 4.06

9 56 44.30 11.70 36.04 5.0110 52 47.81 4.19 40.23 4.92

11 55 49.06 5.94 46.17 5.02

12 54 50.84 3.15 49.32 4.85

DEMAND FORECAST, ERROR E=PERIOD Dt Ft Dt- Ft (Dt- Ft) MAD

TS3= = 2.006.10

3.05

Tracking signal for period 3

1.00

2.00

1.623.00

4.25

5.01

6.00

7.198.18

9.20

10.17

TRACKING

SIGNAL


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Tracking Signal Plot

3210

-1-2-3

| | | | | | | | | | | | |

0 1 2 3 4 5 6 7 8 9 10 11 12

Trackingsignal(MAD)

Period

Exponential smoothing (= 0.30)

Linear trend line


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Statistical Control Charts

=(Dt- Ft)2

n- 1

Using we can calculate statisticalcontrol limits for the forecast error

Control limits are typically set at 3


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Statistical Control Charts

Errors

18.39

12.24

6.12

0

-6.12

-12.24

-18.39

| | | | | | | | | | | | |

0 1 2 3 4 5 6 7 8 9 10 11 12

Period

UCL = +3

LCL = -3


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Regression Methods

Linear regression a mathematical technique that relates a

dependent variable to an independentvariable in the form of a linear equation

Correlation

a measure of the strength of the relationshipbetween independent and dependentvariables


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Linear Regression

y= a+ bx a =y- b x

b =

where

a= intercept

b = slope of the line

x = = mean of thexdata

y = = mean of theydata

xy- nxy

x2

- nx2

xn

y

n


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Linear Regression Example

x y

(WINS) (ATTENDANCE) xy x2

4 36.3 145.2 16

6 40.1 240.6 36

6 41.2 247.2 368 53.0 424.0 64

6 44.0 264.0 36

7 45.6 319.2 49

5 39.0 195.0 25

7 47.5 332.5 49

49 346.7 2167.7 311


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Linear Regression Example (cont.)

x= = 6.125

y= = 43.36

b=

=

= 4.06

a= y- bx

= 43.36 - (4.06)(6.125)

= 18.46

498

346.9

8

xy- nxy2

x2- nx2

(2,167.7) - (8)(6.125)(43.36)

(311) - (8)(6.125)2


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| | | | | | | | | | |

0 1 2 3 4 5 6 7 8 9 10

60,000

50,000

40,000

30,000

20,000

10,000

Linear regression line,y= 18.46 + 4.06x

Wins, x

Attendance,y

Linear Regression Example (cont.)

y = 18.46 + 4.06x y = 18.46 + 4.06(7)= 46.88, or 46,880

Regression equation Attendance forecast for 7 wins


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Correlation and Coefficient of

Determination

Correlation, r

Measure of strength of relationship

Varies between -1.00 and +1.00

Coefficient of determination, r2

Percentage of variation in dependent

variable resulting from changes in the

independent variable


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Computing Correlation

nxy- xy

[nx2- (x)2] [ny2- (y)2]r =

Coefficient of determination

r2 = (0.947)2 = 0.897

r =(8)(2,167.7) - (49)(346.9)

[(8)(311) - (49)2] [(8)(15,224.7) - (346.9)2]

r= 0.947


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Multiple Regression

Study the relationship of demand to two ormore independent variables

y = 0+ 1x1 + 2x2 + kxkwhere

0 = the intercept1, , k= parameters for the

independent variables

x1, , xk= independent variables


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Documents

Operations Management 5th Edition Ch 11 - Forecasting by Robert Russell & Bernard W. Taylor