4 - 1© 2011 pearson education 4 4 forecasting demand powerpoint presentation to accompany heizer...

4 - 1© 2011 Pearson Education

44 Forecasting DemandForecasting Demand

PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer and Render Heizer and Render Operations Management, 10e, Global Edition Operations Management, 10e, Global Edition Principles of Operations Management, 8e, Global EditionPrinciples of Operations Management, 8e, Global Edition

PowerPoint slides by Jeff Heyl


Chapter OutlineChapter Outline What Is Forecasting?

Forecasting definition

The differences between forecasting & prediction

Forecasting Time Horizons

The Influence of Product Life Cycle

Types Of Forecasts

The Strategic Importance of Forecasting

Seven Steps in the Forecasting System

Forecasting Approaches

Qualitative approach

Quantitative approach


Outline – ContinuedOutline – Continued Time-Series Forecasting

Decomposition of a Time Series

Naive Approach

Moving Averages

Exponential Smoothing

Exponential Smoothing with Trend Adjustment

Trend Projections

Seasonal Variations in Data

Cyclical Variations in Data


Associative Forecasting Methods: Regression and Correlation Analysis

Monitoring and Controlling Forecasts

Adaptive Smoothing

Focus Forecasting

Forecasting in the Service Sector


What is Forecasting?What is Forecasting?

Process of predicting a future event

Underlying basis of all business decisions Production

Inventory

Personnel

Facilities

??

4 - 6

Forecasting definition Forecasting definition

• Forecasting is defined as a planning tool that helps management in its attempts to cope with the uncertainty of the future, relying mainly on data from the past and present and analysis of trends

• There is a dereferences between forecasting and prediction

© 2011 Pearson Education

4 - 7

The Differences Between The Differences Between Forecasting & PredictionForecasting & Prediction

Forecasting Prediction

Forecasting involves the projection of the past into the future

Prediction involves judgment in management after taking all available information into account.

Forecast involves estimating the level of demand of a product on the basis of factors that generated the demand in the past

Prediction involves the anticipated change into the future. It may include even new factors that may affect future demand

Forecasting is more scientific Prediction is more intuitive


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It is relatively free from personal bias.

It is more governed by personal bias and preferences.

It is more objective It is more subjective

Error analysis is possible Prediction does not contain error analysis



Short-range forecast Up to 1 year, generally less than 3 months

Purchasing, job scheduling, workforce levels, job assignments, production levels

Medium-range forecast 1 year to 3 years

Sales and production planning, budgeting

Long-range forecast 3+ years

New product planning, facility location, research and development

Forecasting Time HorizonsForecasting Time Horizons


Influence of Product Life Influence of Product Life CycleCycle

Introduction and growth require longer forecasts than maturity and decline

As product passes through life cycle, forecasts are useful in projecting Staffing levels

Inventory levels

Factory capacity

Introduction – Growth – Maturity – DeclineIntroduction – Growth – Maturity – Decline


Product Life CycleProduct Life Cycle

Long run forecasting + 3 years

Long run forecasting + 3 years

Medium forecasting1 year – 3 years

Short run forecastingUp to 1 year

Introduction Growth Maturity Decline

Fo

rec

as

tin

g T

ime

Ho

rizo

n

Figure 2.5

Internet search engines

Sales

Drive-through restaurants

CD-ROMs

Analog TVs

iPods

Boeing 787

LCD & plasma TVs

Twitter

Avatars

Xbox 360


Types of ForecastsTypes of Forecasts

Economic forecasts Address business cycle – inflation rate,

money supply, dollar price, etc.

Technological forecasts Predict rate of technological progress

Impacts development of new products

Demand forecasts Predict sales of existing products and

services


Strategic Importance of Strategic Importance of ForecastingForecasting

Human Resources – Hiring, training, laying off workers

Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share

Supply Chain Management – Good supplier relations and price advantages


Seven Steps in ForecastingSeven Steps in Forecasting

1. Determine the use of the forecast

2. Select the items to be forecasted

3. Determine the time horizon of the forecast

4. Select the forecasting model(s)

5. Gather the data

6. Make the forecast

7. Validate and implement results


The Realities!The Realities!

Forecasts are seldom perfect

Most techniques assume an underlying stability in the system

The aggregated forecasts are more accurate than individual forecasts


Forecasting ApproachesForecasting Approaches

Used when situation is vague and little data exist New products

New technology

Involves intuition, experience e.g., forecasting sales on

Internet

Qualitative MethodsQualitative Methods


Forecasting ApproachesForecasting Approaches

Used when situation is ‘stable’ and historical data exist Existing products

Current technology

Involves mathematical techniques e.g., forecasting sales of color

televisions

Quantitative MethodsQuantitative Methods


Overview of Qualitative Overview of Qualitative MethodsMethods

Sales force composite

Delphi method

Consumer Market Survey


Sales Force CompositeSales Force Composite

Each salesperson projects his or her sales

Combined at district and national levels

Salesperson know customers’ wants

Tends to be overly optimistic


Delphi MethodDelphi Method Iterative group

process, continues until agreement is reached

3 types of participants Decision makers

Staff

Respondents

Staff(Administering

survey)

Decision Makers(Evaluate

responses and make decisions)

Respondents(People who can make valuable

judgments)


Consumer Market SurveyConsumer Market Survey

Ask customers about purchasing plans

What consumers say, and what they actually do are often different

Sometimes difficult to answer


Overview of Quantitative Overview of Quantitative ApproachesApproaches

1. Naive approach

2. Simple average

3. Moving averages

4. Exponential smoothing

5. Trend projection

6. Linear regression

time-series models

associative model


Forecast based only on past values, no other variables important

Assumes that factors influencing past and present will continue influence in future

Time Series ForecastingTime Series Forecasting


Trend

Seasonal

Cyclical

Random

Time Series ComponentsTime Series Components

4 - 25

COMPONENTS OF TIME COMPONENTS OF TIME SERIES DEMAND SERIES DEMAND

• .1 average: the mean of the observations over time

• 2. trend: a gradual increase or decrease in the average over time

• 3. seasonal influence: predictable short-term cycling behavior due to time of day, week, month, season, year, etc.

• 4. cyclical movement: unpredictable long-term cycling behavior due to business cycle or product/service life cycle

• 5. random error: remaining variation that cannot be explained by the other four components



Components of DemandComponents of DemandD

eman

d f

or

pro

du

ct o

r se

rvic

e

| | | |1 2 3 4

Time (years)

Average demand over 4 years

Trend component

Actual demand line

Random variation

Figure 4.1

Seasonal peaks


Persistent, overall upward or downward pattern

Changes due to population, technology, age, culture, etc.

Typically several years duration

Trend ComponentTrend Component


Regular pattern of up and down fluctuations

Due to weather, customs, etc.

Occurs within a single year

Seasonal ComponentSeasonal Component

Number ofPeriod Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12Year Week 52


Repeating up and down movements

Affected by business cycle, political, and economic factors

Multiple years duration

Often causal or associative relationships

Cyclical ComponentCyclical Component

0 5 10 15 20


Erratic, unsystematic, ‘residual’ fluctuations

Due to random variation or unforeseen events

Short duration and non- repeating

Random ComponentRandom Component

M T W T F


Naive ApproachNaive Approach

It is assume that demand in next period is the same as demand in most recent period e.g., If January sales were 68 units ,

then February sales will be 68

This simple way is the most cost- effective and efficient forecasting model

It can be a good starting point

4 - 32

Naive approach Naive approach

• Suppose the demand for York factory last year was 1000 cars

• According to the naive approach the demand of the next year is 1000 car


4 - 33

Simple Average Method Simple Average Method

• It is assume that demand in the next

period is the mean of the actual demand in last

periods


4 - 34

Simple average exampleSimple average example

• A XYZ television supplier found a demand of 200 sets in July, 225 sets in August & 245 sets in September. Find the demand forecast for the month of October using simple average method.

• F = D1+D2+D3/3=

• 200+225+245/3=223.3

• 224 units© 2011 Pearson Education


MA is a series of arithmetic means

Used if little or no trend

Used often for smoothing

Provides overall impression of data over time

Moving Average MethodMoving Average MethodMAMA

Moving average =∑ demand in previous n periods

n

4 - 36

Moving Average MethodMoving Average Methodexample example

• Donna’s garden supply wants a 3 – month moving average forecast including a forecast for next January, for shed sales



January 10February 12March 13April 16May 19June 23July 26

Actual 3-MonthMonth Shed Sales Moving Average

(12 + 13 + 16)/3 = 13 2/3

(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3

Moving Average ExampleMoving Average Example

101012121313

(1010 + 1212 + 1313)/3 = 11 2/3


Graph of Moving AverageGraph of Moving Average

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

J F M A M J J A S O N D

Sh

ed S

ales

30 –28 –26 –24 –22 –20 –18 –16 –14 –12 –10 –

Actual Sales

Moving Average Forecast


Used when some trend might be present Older data usually less important

Weights based on experience and intuition

Weighted Moving AverageWeighted Moving Average

Weightedmoving average =

∑ (weight for period n) x (demand in period n)

∑ weights

4 - 40

Weighted Moving Average Weighted Moving Average example example

• Donna’s garden supply wants to forecast shed sales by weighted the past 3 months ,with more weight given to recent data to make them more significant



January 10February 12March 13April 16May 19June 23July 26

Actual 3-Month WeightedMonth Shed Sales Moving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 141/3

[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 201/2

Weighted Moving AverageWeighted Moving Average

101012121313

[(3 x 1313) + (2 x 1212) + (1010)]/6 = 121/6

Weights Applied Period

33 Last month22 Two months ago11 Three months ago

6 Sum of weights


Moving Average And Moving Average And Weighted Moving AverageWeighted Moving Average

30 –

25 –

20 –

15 –

10 –

5 –

Sa

les

de

man

d

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

J F M A M J J A S O N D

Actual sales

Moving average

Weighted moving average

Figure 4.2


Increasing n smooths the forecast but makes it less sensitive to changes

Do not forecast trends well

Require extensive historical data

Potential Problems WithPotential Problems With Moving Average Moving Average


Form of weighted moving average Weights decline exponentially

Most recent data weighted most

Requires smoothing constant () Ranges from 0 to 1

Subjectively chosen

Involves little record keeping of past data

Exponential SmoothingExponential Smoothing


Exponential SmoothingExponential Smoothing

New forecast = Last period’s forecast+ (Last period’s actual demand

– Last period’s forecast)

Ft = Ft – 1 + (At – 1 - Ft – 1)

where Ft = new forecast

Ft – 1 = previous forecast

= smoothing (or weighting) constant (0 ≤ ≤ 1)


Exponential Smoothing Exponential Smoothing ExampleExample

In January , a car dealer predicted February demand for 142 ford mustangs , Actual Febrruary demand = 153 autos, using a Smoothing constant = .20 , the dealer wants to forecast march demand using the exponential smoothing model



Predicted demand = 142 Ford MustangsActual demand = 153Smoothing constant = .20

New forecast = 142 + .2(153 – 142)



Predicted demand = 142 Ford MustangsActual demand = 153Smoothing constant = .20

New forecast = 142 + .2(153 – 142)

= 142 + 2.2

= 144.2 ≈ 144 cars


Effect ofEffect of Smoothing Constants Smoothing Constants

Weight Assigned to

Most 2nd Most 3rd Most 4th Most 5th MostRecent Recent Recent Recent Recent

Smoothing Period Period Period Period PeriodConstant () (1 - ) (1 - )2 (1 - )3 (1 - )4

= .1 .1 .09 .081 .073 .066

= .5 .5 .25 .125 .063 .031


Impact of Different Impact of Different

225 –

200 –

175 –

150 –| | | | | | | | |

1 2 3 4 5 6 7 8 9

Quarter

De

ma

nd

= .1

Actual demand

= .5


Impact of Different Impact of Different

225 –

200 –

175 –

150 –| | | | | | | | |

1 2 3 4 5 6 7 8 9

Quarter

De

ma

nd

= .1

Actual demand

= .5 Chose high values of when underlying average is likely to change

Choose low values of when underlying average is stable


Choosing Choosing

The objective is to obtain the most accurate forecast no matter the technique

We generally do this by selecting the We generally do this by selecting the model that gives us the lowest forecast model that gives us the lowest forecast errorerror

Forecast error = Actual demand - Forecast value

= At - Ft


Common Measures of ErrorCommon Measures of Error

Mean Absolute Deviation (MAD)

MAD =∑ |Actual - Forecast|

n

Mean Squared Error (MSE)

MSE =∑ (Forecast Errors)2

n


Comparison of Forecast Comparison of Forecast Error Error

Rounded Absolute Rounded AbsoluteActual Forecast Deviation Forecast Deviation

Tonnage with for with forQuarter Unloaded = .10 = .10 = .50 = .50

1 180 175 5.00 175 5.002 168 175.5 7.50 177.50 9.503 159 174.75 15.75 172.75 13.754 175 173.18 1.82 165.88 9.125 190 173.36 16.64 170.44 19.566 205 175.02 29.98 180.22 24.787 180 178.02 1.98 192.61 12.618 182 178.22 3.78 186.30 4.30

82.45 98.62





1 180 175 5.00 175 5.002 168 175.5 7.50 177.50 9.503 159 174.75 15.75 172.75 13.754 175 173.18 1.82 165.88 9.125 190 173.36 16.64 170.44 19.566 205 175.02 29.98 180.22 24.787 180 178.02 1.98 192.61 12.618 182 178.22 3.78 186.30 4.30

82.45 98.62

MAD =∑ |deviations|

n

= 82.45/8 = 10.31

For = .10

= 98.62/8 = 12.33

For = .50





1 180 175 5.00 175 5.002 168 175.5 7.50 177.50 9.503 159 174.75 15.75 172.75 13.754 175 173.18 1.82 165.88 9.125 190 173.36 16.64 170.44 19.566 205 175.02 29.98 180.22 24.787 180 178.02 1.98 192.61 12.618 182 178.22 3.78 186.30 4.30

82.45 98.62MAD 10.31 12.33

= 1,526.54/8 = 190.82

For = .10

= 1,561.91/8 = 195.24

For = .50

MSE =∑ (forecast errors)2

n





1 180 175 5.00 175 5.002 168 175.5 7.50 177.50 9.503 159 174.75 15.75 172.75 13.754 175 173.18 1.82 165.88 9.125 190 173.36 16.64 170.44 19.566 205 175.02 29.98 180.22 24.787 180 178.02 1.98 192.61 12.618 182 178.22 3.78 186.30 4.30

82.45 98.62MAD 10.31 12.33MSE 190.82 195.24


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

When a trend is present, exponential smoothing must be modified

Forecast including (FITt) = trend

Exponentially Exponentiallysmoothed (Ft) + smoothed (Tt)forecast trend


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

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

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

Step 1: Compute Ft

Step 2: Compute Tt

Step 3: Calculate the forecast FITt = Ft + Tt


Exponential Smoothing with Exponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example

ForecastActual Smoothed Smoothed Including

Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt

1 12 11 2 13.002 173 204 195 246 217 318 289 36

10

Table 4.1





1 12 11 2 13.002 173 204 195 246 217 318 289 36

10

Table 4.1

F2 = A1 + (1 - )(F1 + T1)

F2 = (.2)(12) + (1 - .2)(11 + 2)

= 2.4 + 10.4 = 12.8 units

Step 1: Forecast for Month 2





1 12 11 2 13.002 17 12.803 204 195 246 217 318 289 36

10

Table 4.1

T2 = (F2 - F1) + (1 - )T1

T2 = (.4)(12.8 - 11) + (1 - .4)(2)

= .72 + 1.2 = 1.92 units

Step 2: Trend for Month 2





1 12 11 2 13.002 17 12.80 1.923 204 195 246 217 318 289 36

10

Table 4.1

FIT2 = F2 + T2

FIT2 = 12.8 + 1.92

= 14.72 units

Step 3: Calculate FIT for Month 2





1 12 11 2 13.002 17 12.80 1.92 14.723 204 195 246 217 318 289 36

10

Table 4.1

15.18 2.10 17.2817.82 2.32 20.1419.91 2.23 22.1422.51 2.38 24.8924.11 2.07 26.1827.14 2.45 29.5929.28 2.32 31.6032.48 2.68 35.16



Figure 4.3

| | | | | | | | |

1 2 3 4 5 6 7 8 9

Time (month)

Pro

du

ct d

eman

d

35 –

30 –

25 –

20 –

15 –

10 –

5 –

0 –

Actual demand (At)

Forecast including trend (FITt)with = .2 and = .4


Trend ProjectionsTrend Projections

Fitting a trend line to historical data points to project into the medium to long-range

Linear trends can be found using the least squares technique

y = a + bx^

where y= computed value of the variable to be predicted (dependent variable)a= y-axis interceptb= slope of the regression linex= the independent variable

^


Least Squares MethodLeast Squares Method

Equations to calculate the regression variables

b =xy - nxy

x2 - nx2

y = a + bx^

a = y - bx


Least Squares ExampleLeast Squares Example

b = = = 10.54∑xy - nxy

∑x2 - nx2

3,063 - (7)(4)(98.86)

140 - (7)(42)

a = y - bx = 98.86 - 10.54(4) = 56.70

Time Electrical Power Year Period (x) Demand (y) x2 xy

2003 1 74 1 742004 2 79 4 1582005 3 80 9 2402006 4 90 16 3602007 5 105 25 5252008 6 142 36 8522009 7 122 49 854

∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063x = 4 y = 98.86


b = = = 10.54∑xy - nxy

∑x2 - nx2

3,063 - (7)(4)(98.86)

140 - (7)(42)

a = y - bx = 98.86 - 10.54(4) = 56.70

Time Electrical Power Year Period (x) Demand x2 xy

2003 1 74 1 742004 2 79 4 1582005 3 80 9 2402006 4 90 16 3602007 5 105 25 5252008 6 142 36 8522009 7 122 49 854

∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063x = 4 y = 98.86

Least Squares ExampleLeast Squares Example

The trend line is

y = 56.70 + 10.54x^


Seasonal Variations In DataSeasonal Variations In Data

The multiplicative seasonal model can adjust trend data for seasonal variations in demand


Seasonal Variations In DataSeasonal Variations In Data

1. Find average historical demand for each season

2. Compute the average demand over all seasons

3. Compute a seasonal index for each season

4. Estimate next year’s total demand

5. Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season

Steps in the process:Steps in the process:


Seasonal Index ExampleSeasonal Index Example

Jan 80 85 105 90 94Feb 70 85 85 80 94Mar 80 93 82 85 94Apr 90 95 115 100 94May 113 125 131 123 94Jun 110 115 120 115 94Jul 100 102 113 105 94Aug 88 102 110 100 94Sept 85 90 95 90 94Oct 77 78 85 80 94Nov 75 72 83 80 94Dec 82 78 80 80 94

Demand Average Average Seasonal Month 2007 2008 2009 2007-2009 Monthly Index



Jan 80 85 105 90 94Feb 70 85 85 80 94Mar 80 93 82 85 94Apr 90 95 115 100 94May 113 125 131 123 94Jun 110 115 120 115 94Jul 100 102 113 105 94Aug 88 102 110 100 94Sept 85 90 95 90 94Oct 77 78 85 80 94Nov 75 72 83 80 94Dec 82 78 80 80 94


0.957

Seasonal index = Average 2007-2009 monthly demand

Average monthly demand

= 90/94 = .957



Jan 80 85 105 90 94 0.957Feb 70 85 85 80 94 0.851Mar 80 93 82 85 94 0.904Apr 90 95 115 100 94 1.064May 113 125 131 123 94 1.309Jun 110 115 120 115 94 1.223Jul 100 102 113 105 94 1.117Aug 88 102 110 100 94 1.064Sept 85 90 95 90 94 0.957Oct 77 78 85 80 94 0.851Nov 75 72 83 80 94 0.851Dec 82 78 80 80 94 0.851




Jan 80 85 105 90 94 0.957Feb 70 85 85 80 94 0.851Mar 80 93 82 85 94 0.904Apr 90 95 115 100 94 1.064May 113 125 131 123 94 1.309Jun 110 115 120 115 94 1.223Jul 100 102 113 105 94 1.117Aug 88 102 110 100 94 1.064Sept 85 90 95 90 94 0.957Oct 77 78 85 80 94 0.851Nov 75 72 83 80 94 0.851Dec 82 78 80 80 94 0.851


Expected annual demand = 1,200

Jan x .957 = 961,200

12

Feb x .851 = 851,200

12

Forecast for 2010



140 –

130 –

120 –

110 –

100 –

90 –

80 –

70 –| | | | | | | | | | | |J F M A M J J A S O N D

Time

Dem

and

2010 Forecast

2009 Demand

2008 Demand

2007 Demand


Associative ForecastingAssociative Forecasting

Used when changes in one or more independent variables can be used to predict

the changes in the dependent variable

Most common technique is linear regression analysis

We apply this technique just as we did We apply this technique just as we did in the time series examplein the time series example


Associative ForecastingAssociative ForecastingForecasting an outcome based on predictor variables using the least squares technique

y = a + bx^

where y= computed value of the variable to be predicted (dependent variable)a= y-axis interceptb= slope of the regression linex= the independent variable though to predict the value of the dependent variable

^


How strong is the linear relationship between the variables?

Correlation does not necessarily imply causality!

Coefficient of correlation, r, measures degree of association Values range from -1 to +1

CorrelationCorrelation


Correlation CoefficientCorrelation Coefficient

r = nxy - xy

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


Correlation CoefficientCorrelation Coefficient

r = nxy - xy

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

y

x(a) Perfect positive correlation: r = +1

y

x(b) Positive correlation: 0 < r < 1

y

x(c) No correlation: r = 0

y

x(d) Perfect negative correlation: r = -1


Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x Values range from 0 to 1

Easy to interpret

CorrelationCorrelation

For example if:

r = .901

r2 = .81


• R ---------- to what extent there is a correlation relationship between two variables (-1 to 1)

• (positive +)or( negative (-)

• (strong more than .5) or( weak less than .5 )

• R2 ----------- to what extent the change in one variable can be explained by the other variables %


Multiple Regression Multiple Regression AnalysisAnalysis

If more than one independent variable is to be used in the model, linear regression can be

extended to multiple regression to accommodate several independent variables

y = a + b1x1 + b2x2 …^

Computationally, this is quite Computationally, this is quite complex and generally done on the complex and generally done on the

computercomputer


Multiple Regression Multiple Regression AnalysisAnalysis

y = 1.80 + .30x1 - 5.0x2^

In the Nodel example, including interest rates in the model gives the new equation:

An improved correlation coefficient of r = .96 means this model does a better job of predicting the change in construction sales

Sales = 1.80 + .30(6) - 5.0(.12) = 3.00Sales = $3,000,000


Measures how well the forecast is predicting actual values

Ratio of cumulative forecast errors to mean absolute deviation (MAD) Good tracking signal has low values

If forecasts are continually high or low, the forecast has a bias error

Monitoring and Controlling Monitoring and Controlling ForecastsForecasts

Tracking SignalTracking Signal


Monitoring and Controlling Monitoring and Controlling ForecastsForecasts

Tracking signal

Cumulative errorMAD

=

Tracking signal =

∑(Actual demand in period i -

Forecast demand in period i)

∑|Actual - Forecast|/n)


Tracking SignalTracking Signal

Tracking signal

+

0 MADs

–

Upper control limit

Lower control limit

Time

Signal exceeding limit

Acceptable range


Tracking Signal ExampleTracking Signal Example

CumulativeAbsolute Absolute

Actual Forecast Cumm Forecast ForecastQtr Demand Demand Error Error Error Error MAD

1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 +15 0 15 30 10.04 100 110 -10 -10 10 40 10.05 125 110 +15 +5 15 55 11.06 140 110 +30 +35 30 85 14.2


CumulativeAbsolute Absolute

Actual Forecast Cumm Forecast ForecastQtr Demand Demand Error Error Error Error MAD

1 90 100 -10 -10 10 10 10.02 95 100 -5 -15 5 15 7.53 115 100 +15 0 15 30 10.04 100 110 -10 -10 10 40 10.05 125 110 +15 +5 15 55 11.06 140 110 +30 +35 30 85 14.2

Tracking Signal ExampleTracking Signal Example

TrackingSignal

(Cumm Error/MAD)

-10/10 = -1-15/7.5 = -2

0/10 = 0-10/10 = -1

+5/11 = +0.5+35/14.2 = +2.5

The variation of the tracking signal between -2.0 and +2.5 is within acceptable limits


Adaptive ForecastingAdaptive Forecasting

It’s possible to use the computer to continually monitor forecast error and adjust the values of the and coefficients used in exponential smoothing to continually minimize forecast error

This technique is called adaptive smoothing


Forecasting in the Service Forecasting in the Service SectorSector

Presents unusual challenges Special need for short term records

Needs differ greatly as function of industry and product

Holidays and other calendar events

Unusual events


Fast Food Restaurant Fast Food Restaurant ForecastForecast

20% –

15% –

10% –

5% –

11-12 1-2 3-4 5-6 7-8 9-1012-1 2-3 4-5 6-7 8-9 10-11

(Lunchtime) (Dinnertime)Hour of day

Per

cen

tag

e o

f sa

les

Figure 4.12


FedEx Call Center ForecastFedEx Call Center Forecast

Figure 4.12

12% –

10% –

8% –

6% –

4% –

2% –

0% –

Hour of dayA.M. P.M.

2 4 6 8 10 12 2 4 6 8 10 12

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