Student Slides Chapter 3

8/3/2019 Student Slides Chapter 3

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McGraw-Hill/Irwin Copyright 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved.

Chapter 3

Forecasting


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Chapter 3: Learning Objectives

You should be able to:

List the elements of a good forecast

Outline the steps in the forecasting process

Describe qualitative forecasting techniques and their

advantages and disadvantages Compare and contrast qualitative and quantitative

approaches to forecasting

Briefly describe averaging techniques, trend and seasonaltechniques, and regression analysis, and solve typical

problems Describe three measures of forecast accuracy

Describe two ways of evaluating and controlling forecasts

Identify the major factors to consider when choosing aforecasting technique


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Forecast

Forecast a statement about the future valueof a variable of interest

We make forecasts about such things as weather,

demand, and resource availability Forecasts are an important element in making

informed decisions


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An Important Input to Decision Making

The primary goal operations and supply chainmanagement is to match supply to demand

A demand forecast is essential for determining how

much supply will be needed to match demand: Budget preparation

Capacity decisions (e.g., staff and equipment)

Purchasing decisions


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Forecast Uses

Plan the system Generally involves long-range plans related to:

Types of products and services to offer

Facility and equipment levels

Facility location

Plan the use of the system Generally involves short- and medium-range plans related to:

Inventory management

Workforce levels

Purchasing

Budgeting


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Elements of a Good Forecast

The forecast

should be timely

should be accurate

should be reliable should be expressed in meaningful units

should be in writing

technique should be simple to understand and use

should be cost effective


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Steps in the Forecasting Process

1. Determine the purpose of the forecast

2. Establish a time horizon

3. Select a forecasting technique

4. Obtain, clean, and analyze appropriate data

5. Make the forecast

6. Monitor the forecast


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Forecasting Approaches

Qualitative Forecasting

Qualitative techniques permit the inclusion of softinformationsuch as:

Human factors

Personal opinions Hunches

These factors are difficult, or impossible, to quantify

Quantitative Forecasting

Quantitative techniques involve either the projection of historical

data or the development of associative methods that attempt touse causal variablesto make a forecast

These techniques rely on harddata


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Judgemental Forecasting

Executive opinions

Sales force opinion

Consumer surveys

Other approaches

Delphi method


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Time-Series Behaviors


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Accounting Cost/profit estimates

Finance Cash flow and funding

Human Resources Hiring/recruiting/training

Marketing Pricing, promotion, strategy

Operations Schedules, MRP, workloads

Product/service design New products and services

Uses of Forecasts


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Assumes causal systempast ==> future

Forecasts rarely perfect because ofrandomness

Forecasts more accurate forgroups vs. individuals

Forecast accuracy decreases

as time horizon increases

I see that you will

get an A this semester.

Features of Forecasts


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Naive Forecasts

Uh, give me a minute....We sold 250 wheels last

week.... Now, next week

we should sell....

The forecast for any period equalsthe previous periods actual value.


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Simple to use Quick and easy to prepare

Easily understandable

Cannot provide high accuracy

Nave Forecasts


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Stable time series data F(t) = A(t-1)

Seasonal variations

F(t) = A(t-n)

Data with trends

F(t) = A(t-1) + (A(t-1) A(t-2))

Uses for Nave Forecasts


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Techniques for Averaging

Moving average

Weighted moving average

Exponential smoothing


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Muffins Cinemon buns Cupcakes

30 18 45

34 17 26

32 19 27

34 19 23

35 22 22

30 23 48

34 23 29

36 25 20

29 24 14

31 26 18

35 27 4731 28 26

37 29 27

34 31 24

33 33 22


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Moving Averages

Moving average

A technique that averages anumber of recent actual values, updated asnew values become available.

Weighted moving average More recent valuesin a series are given more weight in computing

the forecast.

Ft = MAn= n

At-n+ At-2 + At-1

Ft = WMAn= n

wnAt-n+ wn-1At-2 + w1At-1


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Simple Moving Average

35

37

39

41

43

45

47

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

Actual

MA3

MA5

Ft = MAn= n

At-n+ At-2 + At-1


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Exponential Smoothing

Premise--The most recent observations mighthave the highest predictive value.

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

Ft = Ft-1 + (At-1 - Ft-1)month Sales (000)

Feb 19

Mar 18

Apr 15

May 20

June 18

Jul 22

aug 20


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Weighted averaging method based on previous

forecast plus a percentage of the forecast error

A-F is the error term, is the % feedback

Ft = Ft-1 + (At-1 - Ft-1)


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Picking a Smoothing Constant

35

40

45

50

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

Period

Dem

and .1

.4

Actu

al


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Linear Trend Equation

Ft = Forecast for period t

t = Specified number of time periods

a = Value of Ft at t = 0 b = Slope of the line

Ft = a + bt

0 1 2 3 4 5

t

Ft


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Calculating a and b

b =n (ty) - t y

n t2 - ( t)2

a =y - b t

n


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Linear Trend Equation Example

t yWeek t

2Sales ty

1 1 150 150

2 4 157 314

3 9 162 4864 16 166 664

5 25 177 885

t = 15 t2 = 55 y = 812 ty = 2499( t)

2= 225


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Linear Trend Calculation

y = 143.5 + 6.3t

a =812 - 6.3(15)

5=

b = 5 (2499) - 15(812)5(55) - 225

= 12495 -12180275 - 225

= 6.3

143.5


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Techniques for Seasonality

Seasonal variations

Regularly repeating movements in series values thatcan be tied to recurring events.

Seasonal relative Percentage of average or trend

Centered moving average

A moving average positioned at the center of the data

that were used to compute it.


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Associative Forecasting

Predictor variables- used to predict values ofvariable interest

Regression- technique for fitting a line to a set of

points Least squares line- minimizes sum of squared

deviations around the line


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Linear Model Seems Reasonable

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

0

10

20

30

40

50

0 5 10 15 20 25

X Y7 15

2 10

6 13

4 15

14 2515 27

16 24

12 20

14 27

20 4415 34

7 17

Computedrelationship


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Forecast Accuracy

Error - difference between actual value andpredicted value

Mean Absolute Deviation (MAD)

Average absolute error

Mean Squared Error (MSE)

Average of squared error

Mean Absolute Percent Error (MAPE) Average absolute percent error


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MAD, MSE, and MAPE

MAD =Actual forecast

n

MSE = Actual forecast)

- 1

2

n

(

MAPE =Actual forecast

n

/ Actual*100)(


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MAD, MSE and MAPE

MAD

Easy to compute

Weights errors linearly

MSE Squares error

More weight to large errors

MAPE

Puts errors in perspective


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Example 10

Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100

1 217 215 2 2 4 0.92

2 213 216 -3 3 9 1.41

3 216 215 1 1 1 0.46

4 210 214 -4 4 16 1.90

5 213 211 2 2 4 0.94

6 219 214 5 5 25 2.28

7 216 217 -1 1 1 0.46

8 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75

MSE= 10.86

MAPE= 1.28


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Controlling the Forecast

Control chart A visual tool for monitoring forecast errors

Forecasting errors are in control if

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


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Sources of Forecast errors

Model may be inadequate Irregular variations

Incorrect use of forecasting technique


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Tracking Signal

Tracking signal = (Actual - forecast)MAD

Tracking signal

Ratio of cumulative error to MAD

Bias Persistent tendency for forecasts to beGreater or less than actual values.

Choosing a Forecasting


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Choosing a ForecastingTechnique

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


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Operations Strategy

Forecasts are the basis for many decisions

Work to improve short-term forecasts

Accurate short-term forecasts improve

Profits Lower inventory levels

Reduce inventory shortages

Improve customer service levels

Enhance forecasting credibility


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Supply Chain Forecasts

Sharing forecasts with supply can Improve forecast quality in the supply chain

Lower costs

Shorter lead times Gazing at the Crystal Ball (reading in text)


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i i


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Linear Trend Equation

Si l i i


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Simple Linear Regression


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Time-Series Forecasting - Averaging

These Techniques work best when a seriestends to vary about an average Averaging techniques smooth variations in the data

They can handle step changes or gradual changes in

the level of a series Techniques

Moving average

Weighted moving average

Exponential smoothing


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Simple Linear Regression

Regression - a technique for fitting a line to aset of data points

Simple linear regression - the simplest form ofregression that involves a linear relationship betweentwo variables

The object of simple linear regression is to obtain anequation of a straight line that minimizes the sum ofsquared vertical deviations from the line (i.e., the least

squares criterion)


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Monitoring the Forecast

Tracking forecast errors and analyzing them can provide usefulinsight into whether forecasts are performing satisfactorily

Sources of forecast errors

The model may be inadequate

Irregular variations may have occurred

The forecasting technique has been incorrectly applied

Random error

Control charts are useful for identifying the presence of non-random error in forecasts

Tracking signals can be used to detect forecast bias


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Choosing a Forecasting Technique

Factors to consider Cost

Accuracy

Availability of historical data Availability of forecasting software

Time needed to gather and analyze data and preparea forecast

Forecast horizon


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Using Forecast Information

Reactive approach View forecasts as probable future demand

React to meet that demand

Proactive approach

Seeks to actively influence demand Advertising

Pricing

Product/service modifications

Generally requires either and explanatory model or a subjective

assessment of the influence on demand


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Operations Strategy

The better forecasts are, the more able organizationswill be to take advantage of future opportunities andreduce potential risks

A worthwhile strategy is to work to improve short-term forecasts

Accurate up-to-date information can have a significant effecton forecast accuracy:

Prices

Demand

Other important variables

Reduce the time horizon forecasts have to cover Sharing forecasts or demand data through the

supply chain can improve forecast quality

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