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Reseach Article

A Production Planning Model using Fuzzy Neural Network: A Case Study of an Automobile Industry

by Rashmi Sharma, Ashok K. Sinha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 4
Year of Publication: 2012
Authors: Rashmi Sharma, Ashok K. Sinha
10.5120/5032-7183

Rashmi Sharma, Ashok K. Sinha . A Production Planning Model using Fuzzy Neural Network: A Case Study of an Automobile Industry. International Journal of Computer Applications. 40, 4 ( February 2012), 19-22. DOI=10.5120/5032-7183

@article{ 10.5120/5032-7183,
author = { Rashmi Sharma, Ashok K. Sinha },
title = { A Production Planning Model using Fuzzy Neural Network: A Case Study of an Automobile Industry },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 4 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number4/5032-7183/ },
doi = { 10.5120/5032-7183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:24.149342+05:30
%A Rashmi Sharma
%A Ashok K. Sinha
%T A Production Planning Model using Fuzzy Neural Network: A Case Study of an Automobile Industry
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 4
%P 19-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The production forecast based on order received is adversely affected by deterrent factors in supply chain management (SCM). Although several mathematical models have been attempted by researchers, the accuracy of such forecast still need to be improved. In the present paper the various deterrent factors influencing the supply chain have been considered adequately to forecast the production plan on the order received. The model is based on the machine learning using fuzzy neural network architecture and validated with real data on automobile production. The result is compared with that obtained by conventional multiple regression model and the former is found to be quite satisfactory, the error being as low as 2.9847 e-006

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Index Terms

Computer Science
Information Sciences

Keywords

Production Plan Fuzzy Neural Network Supply Chain Management Machine Learning