Final Report on Aerosol

8/18/2019 Final Report on Aerosol

1/23

Analysis of Forecasting Demand of Aerosol

2.3.1 Forecasting

Forecasting is the process of making statements about events whose actual outcomes

(typically) have not yet been observed. A commonplace example might be estimation for

some variable of interest at some specified future date. Prediction is a similar, but more

general term. Forecasting is an important tool for the future of demand condition. his is

a prediction of future events used for planning purposes. Forecasting is needed to aid in

determining what resources are needed, scheduling existing resources and ac!uiring

additional resources.

2.3.2 Types of Forecasting

"ainly, the forecasting can be classified as#

$ualitative methods

$uantitative methods

Qualitative Methods# $ualitative forecasting techni!ues generally employ the %udgment

of experts in the appropriate field to generate forecasts. A key advantage of these

procedures is that they can be applied in situations where historical data are simply notavailable. "oreover, even when historical data are available, significant changes in

environmental conditions affecting the relevant time series may make the use of past data

irrelevant and !uestionable in forecasting future values of the time series.

Quantitative Methods# include causal methods and time&series analysis. 'ausal methods

are historical data on independent variables, such as promotional campaigns, economic

conditions and competitors actions to predict demand or supply. ime series analysis is a

statistical approach that relies heavily on historical demand data to pro%ect the future sie

of demand or supply and recognies trends and seasonal patterns.

o make our analysis we have followed the following techni!ues of forecasting.

*) +imple "oving Average

) +ingle exponential +moothing

-) +imple inear /egression

Page 0 *

http://en.wikipedia.org/wiki/Estimationhttp://en.wikipedia.org/wiki/Predictionhttp://en.wikipedia.org/wiki/Predictionhttp://en.wikipedia.org/wiki/Estimation


2/23


1) Simple Moving Average

he simple moving average forecast uses a number of the recent actual data values in

generating a forecast. he moving average forecast can be computed using the following

e!uation#

F t =

∑t =1

n

A t −i

n

¿ A t −n+…+ A t −2+ A t −1

n

1here, F t 2 Forecast for the time period t

A t −1 2 Actual value in period t −1

MA t 2n period moving average

32 number of periods (data points) in the moving average

2) Single Exponential Smooting

+ingle 4xponential +moothing largely overcomes the limitations of moving average

models. 4ach new forecast is based on the previous forecast plus a percentage of the

difference between that forecast and the actual value of the series at that point.

F t = F t −1+α ( A t −1− F t −1)

1here, F t 2 Forecast for period t

F t −1 2 Forecast for previous period

α2 +moothing constant

A t −1 2 Actual demand for previous period

Page 0


3/23


3) Simple !inear "egression

his method involves linear relationship between two variables. he ob%ect in linear

regression is to obtain an e!uation of a straight line that minimies the sum of s!uared

vertical deviations of data points from the line. he e!uation is#

y=a+bx

1here, a 2 constant and b 2 slope of the line

And,

a=´ y−b ´ x

b=n∑ xy−∑ x∑ yn∑ x2−(∑ x2 )

2.3.2 Acc#racy Test

After forecasting, applying different techni!ues, we have found out the forecasting

accuracy and the appropriate techni!ues for forecasting the sales of the product. o

calculate the forecasting accuracy, we have used the following techni!ues#

"ean absolute deviation ("A5) "ean s!uared error ("+4)

"A5 is the average absolute error, "+4 is the average of s!uared error and "AP4 is the

average percent error. he formulas used to compute "A5, "+4 are given below#

∑ ¿ Actual− Forecast ∨¿n

MAD=¿

Page 0 -


4/23


MSE=∑ ( Actual− Forecast )2

n−1

"+4 is similar to the variance of a random sample6 however, it is more sensitive to a fewlarge errors than "A5. 'onse!uently, "A5, the average of the absolute discrepancies

between the actual and fitted values in a given time series is often preferred. 7f a model

fits the past time&series data perfectly, the "A5 value would be ero. As the fit worsens,

the value of "A5 increases. 7n other words, a small value of "A5 is desirable. 7n

addition, when forecast errors are normally distributed, an estimate of the standard

deviation of the forecast error is given by *.8 times "A5. 1e also considered "AP4

test. he advantage of this measure of accuracy is that "AP4 is not dependent on the

magnitude of the values of demand.

2.1.$ Data Analysis Tool

7n order to get the forecasting output of aerosol, we have used "icrosoft 1ord and

"icrosoft 4xcel.

2.2 !imitations

his study has been done based on the data of sales volume and production from 9anuary

:*; to used it for the remaining !uarters. 3ot enough

statistical data has allowed little space for in depth analysis > thus to some extent might

have hampered the accuracy.

Page 0 ;


5/23


Chapter - 03

1.%A&A!'S(S

5ata from 5ecember :*;&


6/23


3.1 Simple Moving Average

he simple moving average forecast uses a number of the recent actual data values in

generating a forecast. he following figures shows the - simple moving average on the

basis of sales data which is the actual demand for :*-&:*8, and we figure the forecast

of these three years for different +CD 8:ml, -8:ml, ;8:ml, and ?::ml.



7/23


0

5000

10000

15000

20000

2500030000

35000

40000

2% ml

250 ml 2015 Actual Demand

250 ml 2015 Simple Moin! Aera!e

05000

1000015000200002500030000350004000045000

3% ml



Page 0 @


8/23


0

50000

100000

150000

200000250000

*+ ml



0

20000

40000

60000

80000

100000

120000

,%% ml

800 ml 2015 Actual Demand 800 ml 2015 Simple Moin! Aera!e

Page 0 ?


9/23


3.2 Single Exponential Smooting

=ere we took α =0.3 to do our analysis because when we divide maximum value with

minimum value it comes out more than .



10/23


0

10000

20000

30000

40000

50000

60000

2%ml

250 ml 2015 Actual Demand250 ml 2015 Sin!le eponential Smoothin!

0

10000

20000

30000

40000

50000

60000

3% ml

350 ml 2015 Actual Demand350 ml 2015 Sin!le eponential Smoothin!

Page 0 *:


11/23


0

50000

100000

150000

200000

250000

*+ ml

475 M 2015 Actual Demand

475 M 2015 Sin!le eponential Smoothin!

0

20000

40000

60000

80000100000

120000

140000

,%% ml


800 ml 2015 Sin!le eponential Smoothin!

Page 0 **


12/23


3.3 Simple !inear "egression



13/23


-

5,000

10,000

15,000

20,000

25,00030,000

35,000

40,000

250 ml

Actual Demand Simple inear .e!re''ion

-

5,000

10,000

15,000

20,000

25,00030,000

35,000

40,000

350 ml


Page 0 *-


14/23


-20,00040,00060,00080,000

100,000120,000140,000160,000

475 ml


-

10,000

20,000

30,00040,000

50,000

60,000

70,000

80,000

800 ml


Page 0 *;


15/23


3.* Forecasting Di-erences



16/23


0

10000

20000

30000

40000

50000

60000

70000

80000

Forecasting Di-erences et/een Di-erent Metods

$oreca'tin! o/ AC Simple Moin! Aera!e

Sin!le +ponential Smoothin! Simple inear .e!re''ion

Page 0 *B


17/23




18/23


0

10000

20000

30000

40000

50000

60000

70000




Page 0 *?


19/23




20/23


0

50000

100000

150000

200000

250000




Page 0 :


21/23




22/23


0

20000

40000

60000

80000

100000

120000

140000




Page 0


23/23


3. Acc#racy Test

S 250ml 350ml 475ml 800ml

+imple

"oving

Average

MAD 2802 3674 11163 7502

MS' 1212707 1778276 128008715 82131571

+ingle

4xponential

+moothing

MAD 150 8305 26204 1868

MS'

112772476 10847187 10716066 5518441

+imple

inear

/egression

MAD 538 63 3823 2774

MS' 383524 635885 1351211 1010340

7n the above chart we can see that simple linear regression is giving out better figures in

terms of comparison between it, simple moving average and single exponential

smoothing for forecasting. As we know the lower the figure the better the model. hus for

further forecasting we can use the simple linear regression.

Page 0 -

Final Report on Aerosol

Documents

Transcript of Final Report on Aerosol