Sales Forecast Of An Automobile Industry Page 2
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4International Journal of Computer Applications (0975 – 8887)
Volume 53– No.12, September 2012
Fuzzy model to classic linear model and verify the capabilities
of Fuzzy-neural networks in this prediction.
Bias
Sales
Yan Liu, Min Sun [7] proposed the use of fuzzy optimization
Sales Forecasted
Forecast
BP neural network as a management tool for the maintenance
Petrol Rate
of expressway pavement.
System
Inflation Rate
2. METHODOLOGY
1.
The significant parameters of the model are identified
2.
The proposed sales forecast model is represented by
artificial neural network (ANN).
Sales as input for next
3.
The ANN model is trained by past data on inputs and the
outputs.
month
4.
The model error is compared with the error obtained by
Figure 1: Sale Forecast System
multiple regression technique.
5.
Using the converged values of interlayer coefficients
2.2 Neural Network Architecture
sales forecast can be computed.
The neural network model used in the study is the multilayer
perceptron (MLP) also known as a supervised network. This
2.1 Parameters of the Model
network requires a desired output in order to learn, achieved
It is important to examine some of the significant factors that
by creating a model that correctly maps the input to the output
affect the sale of goods.
using historical data so that the model can then be used to
1.
Price of the Product- There is inverse relationship
produce the output when the desired output is unknown. This
between price and the amount consumers are willing and
uses three-layer architecture: input (known parameters),
able to buy is often referred to as the law of demansd.
hidden layer, and output layer (known value)
2.
The Consumer's Income
-For most goods, there is a
positive (direct) relationship between a consumer's
income and the amount of the goods that one is willing
Input
Hidden
Output
and able to buy.
Layer
Layer
Layer
3.
Consumer Awareness-
The percentage of population or
s
Bia
target market, who are aware of the existence of a given
brand or company.
4.
Inflation rate - The fall in the value of local currency in
Petrol rate
Sales Forecasted
the global market.
5.
Petrol/ Diesel Rate - The increasing rate of the crude oil
in international market affects the petrol / diesel rates in
Inflation rate
India.
In the context of Maruti Suzuki India
Sale of previous month
1.
Price of the product is not considered, as the company
has good market value.
2.
These days ample loan facilities are available so the
consumer’s income also does not directly affect the sale
Figure 2: Neuro fuzzy Model for Sale Forecast System
of the product.
Consumer’s awareness is evident from the past sales of
3.
2.3
Model
implementation
in
Neural
the product.
4.
Inflation rate affects the sale as it directly affects the loan
Network
installments and the resale value of the product.
A sample of data on Inflation rate, Petrol rate, actual sales of
5.
Diesel/Petrol is a consumable in the product and required
previous month are shown in the table I.
on daily basis. So it affects monthly expenditure of the
consumer.
Neural network parameters are:
The significantly affecting parameters considered in our model
Number of patterns for training - 11
are:
Nodes in input layer - 4 (Three parameters and one bias)
1.
Inflation Rate
Hidden layer - 1
2.
Petrol/Diesel Price
Nodes in hidden layer - 2
3.
Sale of the previous month.
Nodes in output layer - 1
Weights between input layers and hidden layers - 8
Weights between hidden layers and output layer - 2
Learning rate (η) - 0.3
Constant value (α) - 0.5
Number of iterations - 50
Software used – Program (m file) developed on MATLAB 7.0
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