Chapter 3 Classroom Ppt 2ot45

3-1

Forecasting

Operations Management

William J. Stevenson

8th edition

3-2

Forecasting

CHAPTER

3

Forecasting

McGraw-Hill/Irwin

Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights

3-3

Forecasting

FORECAST:  

A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization  ing, finance  Human resources  Marketing  MIS  Operations  Product / service design

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Forecasting

Uses of Forecasts ing

Cost/profit estimates

Finance

Cash flow and funding

Human Resources

Hiring/recruiting/training

Marketing

Pricing, promotion, strategy

MIS

IT/IS systems, services

Operations

Schedules, MRP, workloads

Product/service design

New products and services

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Forecasting



Assumes causal system past ==> future



Forecasts rarely perfect because of randomness



Forecasts more accurate for groups vs. individuals



Forecast accuracy decreases as time horizon increases

I see that you will get an A this semester.

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Forecasting

Elements of a Good Forecast

Timely

Reliable

n i n a e M

ul f g

Accurate

Written

y s Ea

to

e s u

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Forecasting

Steps in the Forecasting Process

“The forecast”

Step 6 Monitor the forecast Step 5 Prepare the forecast Step 4 Gather and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast

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Forecasting

Types of Forecasts 

Judgmental - uses subjective inputs



Time series - uses historical data assuming the future will be like the past



Associative models - uses explanatory variables to predict the future

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Forecasting

Judgmental Forecasts 

Executive opinions



Sales force opinions



Consumer surveys



Outside opinion



Delphi method 

Opinions of managers and staff



Achieves a consensus forecast

3-10 Forecasting

Time Series Forecasts Trend - long-term movement in data  Seasonality - short-term regular variations in data  Cycle – wavelike variations of more than one year’s duration  Irregular variations - caused by unusual circumstances  Random variations - caused by chance 

3-11 Forecasting

Forecast Variations Figure 3.1 Irregular variation

Trend

Cycles 90 89 88 Seasonal variations

3-12 Forecasting

Naive Forecasts

Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.

3-13 Forecasting

Naïve Forecasts Simple to use  Virtually no cost  Quick and easy to prepare  Data analysis is nonexistent  Easily understandable  Cannot provide high accuracy  Can be a standard for accuracy 

3-14 Forecasting

Techniques for Averaging 

Moving average



Weighted moving average



Exponential smoothing

3-15 Forecasting

Moving Averages 

Moving average – A technique that averages a number of recent actual values, updated as new values become available. n

MAn = 

A  i i=1 n

Weighted moving average – More recent values in a series are given more weight in computing the forecast.

3-16 Forecasting

Simple Moving Average Actual 47 45 43

MA5

41 39 37

MA3

35 1

2

3

4

5

6

7

8 n

MAn =

9

A  i i=1 n

10 11 12

3-17 Forecasting

Exponential Smoothing

Ft = Ft-1 + (At-1 - Ft-1) •

Premise--The most recent observations might have the highest predictive value. 

Therefore, we should give more weight to the more recent time periods when forecasting.

3-18 Forecasting

Exponential Smoothing

Ft = Ft-1 + (At-1 - Ft-1) Weighted averaging method based on previous forecast plus a percentage of the forecast error  A-F is the error term,  is the % 

3-19 Forecasting

Picking a Smoothing Constant Actual

Demand

50

.4

45

.1

40 35 1

2

3

4

5

6

7

Period

8

9 10 11 12

3-20 Forecasting

Linear Trend Equation Ft

Ft = a + bt    

Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line

0 1 2 3 4 5

t

3-21 Forecasting

Calculating a and b n  (ty) -  t  y b = n t 2 - (  t) 2

 y - b t a = n

3-22 Forecasting

Linear Trend Equation Example t W eek 1 2 3 4 5  t = 15 2 (  t) = 2 2 5

2

t 1 4 9 16 25  t

2

= 55

y S a le s 150 157 162 166 177

ty 150 314 486 664 885

 y = 812

 ty = 2 4 9 9

3-23 Forecasting

Linear Trend Calculation 5 (2499) - 15(812) 12495-12180 b = = = 6.3 5(55) - 225 275 -225

812 - 6.3(15) a = = 143.5 5

y = 143.5 + 6.3t

3-24 Forecasting

Associative Forecasting 

Predictor variables - used to predict values of variable interest



Regression - technique for fitting a line to a set of points



Least squares line - minimizes sum of squared deviations around the line

3-25 Forecasting

Linear Model Seems Reasonable X 7 2 6 4 14 15 16 12 14 20 15 7

Y 15 10 13 15 25 27 24 20 27 44 34 17

Computed relationship 50 40 30 20 10 0 0

5

10

15

20

25

A straight line is fitted to a set of sample points.

3-26 Forecasting

Forecast Accuracy 

Error - difference between actual value and predicted value



Mean Absolute Deviation (MAD) 



Mean Squared Error (MSE) 



Average absolute error Average of squared error

Mean Absolute Percent Error (MAPE) 

Average absolute percent error

3-27 Forecasting

MAD, MSE, and MAPE MAD

=

 Actual

 forecast n

MSE

=

 ( Actual

 forecast)

2

n -1

MAPE =

 Actual

 forecas t n

/ Actual*100)

3-28 Forecasting

Controlling the Forecast 

Control chart A visual tool for monitoring forecast errors  Used to detect non-randomness in errors 



Forecasting errors are in control if All errors are within the control limits  No patterns, such as trends or cycles, are present 

3-29 Forecasting

Sources of Forecast errors Model may be inadequate  Irregular variations  Incorrect use of forecasting technique 

3-30 Forecasting

Tracking Signal •Tracking signal –Ratio of cumulative error to MAD

(Actual-forecast)  Tracking signal = MAD

Bias – Persistent tendency for forecasts to be Greater or less than actual values.

3-31 Forecasting

Choosing a Forecasting Technique No single technique works in every situation  Two most important factors 

Cost  Accuracy 



Other factors include the availability of: Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon 