Forecasting 1 Lecture-revision

8/11/2019 Forecasting 1 Lecture-revision

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FOREC STING

Forecasting is the art and science ofpredicting future events

I know no way of judging the future but

by the past.

Patrick Henry I see that you willget an A this semester.


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Why Forecast?

Lead times require that decisions bemade in advance of uncertain events.

Forecasts of product demand,materials, labor, financing are animportant inputs to scheduling,acquiring resources, and determiningresource requirements (reduceuncertainty)

Makeproduction planningeasier


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Elements of a Good Forecast

Timely

AccurateReliable

Written


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CLASSIFICATION OF FORECASTINGPROBLEMS

According to Time Horizons

According to Forecast Method Used


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Time Horizons

Short Term (up to 3 months)

Purchase orders

Scheduling workforce levels

Production levels

Job assignments


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Medium Term (3 months to 2-3years)

Production planning

Sales planning

Budgeting

Purchasing

Distribution

Time Horizons


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Long Term (2-3 years or more)

New products

Capital expenditures

Facility location/expansion

Capacity planning

Strategic planning

Time Horizons


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Forecast Method Used

Qualitative Forecast Subjective in nature

Executive opinion:

panel consensus (a group of managers,staffs and experts make forecast based onconsensus among them under the riskofdominance)

consumer market survey (marketingdepartment sends surveyor to collectinformation from consumer)

delphimethod


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Delphi Method

l. Choose the experts to participate representing a varietyof knowledgeable people in different areas

2. Through a questionnaire (or E-mail), obtain forecasts(and any premises or qualifications for the forecasts)

from all participants3. Summarize the results and redistribute them to the

participants along with appropriate new questions

4. Summarize again, refining forecasts and conditions,

and again develop new questions5. Repeat Step 4 as necessary and distribute the final

results to all participants


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Principles of Forecasting

Forecasts are almost always wrong.

Every forecast should include an

estimate of the forecast error. Actual errors are almost always

bigger than estimated errors.

The greater the degree ofaggregation, the more accurate theforecast.


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Combining methods may improveaccuracy.

Long-term forecasts are usually lessaccurate than short-term forecasts.

Psychological biases impair forecasts.

Principles of Forecasting


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Forecasting Steps

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting techniqueStep 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Measure forecast error

Step 7 Forecast verification

The forecast


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Important Factors for selecting forecastingmethods

Time Horizons

Forecasting Objective by considering

accuracy and costs Products life cycle

Demand patterns


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Time(a) Trend

Time(d) Trend with seasonal pattern

Time(c) Seasonal pattern

Time(b) Cycle

Demand

Demand

Demand

Demand

Randommovement

Demand Patterns


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Cost if in-accuracy of forecast versus costof making forecast

Accuracy of forecasting

high low


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Accuracy of forecast

Accuracy of forecast is determined by thevalue of forecast error

Forecast error = DtFt et where

Dt= data at period t and Ft= forecast atperiod t


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PeriodData

Dt

Forecast

FtDtFt DtFt (DtFt)

2t

tt

D

FD

1 146 142 4 4 16 2,7%

2 150 148 2 2 4 1,3%

3 144 149 5 5 25 3,5%

4 147 148 1 1 1 0.7%5 152 149 3 3 9 2,0%

6 153 153 0 0 0 0,0%

7 142 150 8 8 64 5,6%

8 139 146 7 7 49 5,0%

9 147 141 6 6 36 4,1%

10 152 144 8 8 64 5,3%

Total 2 44 268 30,2%Note: DtFtet n = Number of data


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a. Mean Square Error (MSE)

MSE=

n

t

n

et

1

2

= 268/10 = 26,8

b. Mean Absolute Percentage Error (MAPE)

MAPE =

n

t tD

tF

tD

n1

100 = 2,3010100

% =

3,02

c. Mean Absolute Deviation (MAD)

MAD=n

n

t

et

1= 44 /10 = 4,4


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a. Bias of Forecasting Error (BFE)

BFE=n

n

t

et

1= 2/10 = 0,2

e. The Running Sum of Forecast Erro

(RSFE)

RSFE =

n

t

F tDt

1)( atau

=

n

t

et

1= 2


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Time series Methods

Simple AverageSimple Average: Forecasting Method by averaging all

the past data

SA = Dt+ Dt-1+ Dt-2+

. + Dt- (N1)N

Where :Dt : The present data

Dt-1 : The previous data (one period ahead)Dt-(N-1): The oldest dataN : number of data /total period


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ExampleThe present demand (Dt) = 150 Bakpias

The demand 1 period ahead (Dt-1) = 100 Bakpias

The demand 2 periods ahead (Dt-2) = 130 Bakpias

The demand 3 periods ahead (Dt-3) = 110 Bakpias

The demand 4 periods ahead (Dt-4) = 170 BakpiasThe demand 5 periods ahead (Dt-5) = 180 Bakpias

SA = 150 + 100 + 130 + 110 + 170 + 180 = 140 Bakpias

6


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Simple Moving Average

Moving average is a sequence of average value derived from

the sequential data (demand) by removing the first data andadding the next data Averaging is made based on the period of moving

We use a moving average when demand has no discernabletrend or seasonality.

In this case, Systematic component of demand = level

Example : With moving average of 5 (MA = 5),the average of five

data from five periods becomes the forecast of thesixth period of the data selected


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Moving average forecast with MA = 5

MonthDemand (ton)

(Dt)

Forecast

(Ft)

Jan 107,6Feb 185,9

Mar 196,6

Apr 216,6

May 233,4

Jun 229,7 188,02Jul 234,8 212,44

Agt 202,9

Note :

188,02 = 107,6 + 185,9 + 196,6 + 216,6 + 233,4

5

212,44 = 185,9 + 196,6 + 216,6 + 233,4 + 229,7

5


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Moving average forecast with MA = 6

Month

Demand

(ton)

(Dt)

Forecast

(Ft)

Jan 107,6

Feb 185,9

Mar 196,6

Apr 216,6

May 233,4

Jun 229,7Jul 234,8 193,27

Agt 202,9 214,47

Note :

193,27 = 107,6+ 185,9+ 196,6+ 216,6+ 233,4 + 229,7

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214,47 = 185,9 + 196,6 + 216,6 + 233,4 + 229,7 +234,8

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Forecasting 1 Lecture-revision

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