Operation management 6e by russel and taylor ch12

12-2Lecture Outline  Strategic Role of Forecasting in Supply Chain Management  Time Series Methods  Forecast Accuracy  Time Series Forecasting Using Excel... 12-4Forecasting and Sup

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Beni AsllaniUniversity of Tennessee at Chattanooga

Forecasting

Operations Management - 6th Edition

Chapter 12

Roberta Russell & Bernard W Taylor, III

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Lecture Outline

 Strategic Role of Forecasting in Supply Chain Management

 Time Series Methods

 Forecast Accuracy

 Time Series Forecasting Using Excel

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Forecasting

 Predicting the future

 Qualitative forecast methods

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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

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Forecasting

a key to providing good quality service

 Strategic Planning

accurate forecasts of future products and

markets

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Types of Forecasting Methods

 time frame

 demand behavior

 causes of behavior

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Time Frame

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

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Time (a) Trend

Time (d) Trend with seasonal pattern

Time (c) Seasonal pattern

Time (b) Cycle

Forms of Forecast Movement

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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

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Qualitative Methods

 Management, marketing, purchasing,

and engineering are sources for internal qualitative forecasts

 Delphi method

technological advances from experts

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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?

No

Yes

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Time Series

continue to occur in the future

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Moving Average

 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

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120 90 100 75 110 50 75 130 110 90 Nov -

-FORECAST

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Simple Moving Average

Di = demand in period i

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3-month Simple Moving Average

– – – 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 MOVING AVERAGE

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5-month Simple Moving Average

– – – – – 99.0 85.0 82.0 88.0 95.0 91.0 MOVING AVERAGE

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3-month

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Weighted Moving Average

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Weighted Moving Average Example

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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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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

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Tt = the last period trend factor

= a smoothing constant for trend

Adjusted Exponential Smoothing

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Adjusted Exponential Smoothing (β=0.30)

= 1.36

AF13 = F13 + T13 = 53.61 + 1.36 = 54.97

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Adjusted Exponential Smoothing:

Example

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

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Adjusted Exponential Smoothing Forecasts

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demand for period x

Linear Trend Line

b =

a = y - b x

where

n = number of periods

x = = mean of the x values

y = = mean of the y values

 xy - nxy

 x2 - nx2

 x n

 y n

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Least Squares Example

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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

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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 (1000’S PER QUARTER)

S1 = = = 0.28 D1

 D

42.0 148.7

S2 = = = 0.20 D2

 D

29.5 148.7 S4 = = = 0.37

D4

 D

55.3 148.7

S3 = = = 0.15 D3

 D

21.9 148.7

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Seasonal Adjustment (cont.)

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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 

n

MAD =

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Other Accuracy Measures

Mean absolute percent deviation (MAPD)

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Comparison of Forecasts

Exponential smoothing (  = 0.30) 4.85 9.6% 49.31 4.48 Exponential smoothing (  = 0.50) 4.04 8.5% 33.21 3.02 Adjusted 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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Tracking Signal Values

TRACKING SIGNAL

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Tracking Signal Plot

Linear trend line

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Statistical Control Charts

 = ( Dt - Ft)

2

 Using  we can calculate statistical

control limits for the forecast error

 Control limits are typically set at  3

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Statistical Control Charts

LCL = -3

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Time Series Forecasting using Excel

 Moving average

 Exponential smoothing

 Adjusted exponential smoothing

 Linear trend line

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Exponentially Smoothed and Adjusted

Exponentially Smoothed Forecasts

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Demand and exponentially

smoothed forecast

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Data Analysis option

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Computing a Forecast with

Seasonal Adjustment

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OM Tools

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Regression Methods

 Linear regression

dependent variable to an independent

variable in the form of a linear equation

 Correlation

between independent and dependent

variables

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b = slope of the line

x = = mean of the x data

y = = mean of the y data

 xy -

nxy

 x2 - nx2

 x n

 y n

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Linear Regression Example

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Linear Regression Example (cont.)

 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 60,000 – 50,000 50,000 – 40,000 40,000 – 30,000 30,000 – 20,000 20,000 – 10,000 10,000 –

Linear regression line,

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Correlation and Coefficient of

Determination

 Measure of strength of relationship

 Varies between -1.00 and +1.00

 Percentage of variation in dependent

variable resulting from changes in the independent variable

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Regression Analysis with Excel

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(cont.)

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(cont.)

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Multiple Regression with Excel

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