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I
Dissertation Report
On
A STUDY OF DEMAND FORECASTING USINGARTIFICIAL NEURAL NETWORKS IN
SUPPLY CHAIN MANAGEMENT
By
SAINENI NITISH KUMAR
A0101911286
MBA Class of 2013
Under the Supervision of
DR. Rushina Singhi
ASSISTANT PROFESSOR
DEPARTMENT OF OPERATIONS
In Partial Fulfilment of the Requirements for the Degree ofMaster of Business Administration
At
AMITY BUSINESS SCHOOL
AMITY UNIVERSITY UTTAR PRADESH
SECTOR 125, NOIDA - 201303, UTTAR PRADESH, INDIA
2013
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II
DECLARATION
Title of Project Report
The Study of Demand forecasting using artificial neural networks in supply chain
management
I declare
(a)That the work presented for assessment in this Summer Internship Report is my own, that
it has not previously been presented for another assessment and that my debts (for words,
data, arguments and ideas) have been appropriately acknowledged
(b)That the work conforms to the guidelines for presentation and style set out in the relevant
documentation.
Date: SAINENI NITISH KUMAR
A0101911286
MBAClass of 2013
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III
CERTIFICATE
I, Dr.Rushina Singhi hereby certify that Saineni Nitish Kumar student of Masters of
Business Administration at Amity Business School, Amity University Uttar Pradesh has
completed the Project Report on The Study of Demand Forecasting Using Artificial
Neural Networks in Supply Chain Management, under my guidance.
Dr. Rushina Singhi
Assistant Professor
Department of Operations
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ACKNOWLEDGEMENT
I would like to thank our honourable Director, Amity Business School Dr. Sanjeev
Bansal for his blessings and guidance at this moment.
I wish to express my deep sense of gratitude to my Faculty Guide, Dr. Rushina
Singhi, Assistant Professor, Department of Operations, Amity Business School, for her able
guidance and useful suggestions, which helped me in completing the project work, in time.
Words are inadequate to thank Prof. S.S.Pal, Assistant Professor, Department of
Operations, Amity Business School, for giving me this idea to do the project on this title, and
for is valuable suggestions and continues motivation.
Finally, yet importantly, I would like to express my heartfelt thanks to my beloved
parents for their help and wishes for the successful completion of this project
SAINENI NITISH KUMAR
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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION 1
CHAPTER 2: REVIEW OF THE LITERATURE 4
CHAPTER 3: RESEARCH METHODS AND PROCEDURES 12
3.1 Purpose of the study 13
3.2 Research design 13
3.3 Research technique 13
CHAPTER 4: DATA ANALYSIS AND FINDINGS 15
4.1 Basic Principle of BP Neural Network 16
4.2 Learning Process of BP Neural Network 18
4.3 Neural Networks in super markets 21
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 25
REFERENCES 27
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LIST OF FIGURES
FIGURE NO DESCRIPTION PAGE NO
4.1.1 basic artificial neural network 16
4.1.2 basic BP network layer 17
4.2.1 basic BP algorithm 18
4.2.2 trained BP algorithm 20
4.3.1 aggregation and disaggregation algorithm 23
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ABSTRACT
A manufacturing supply chain is a network of suppliers, factories, subcontractors,
warehouses, distribution centres and retailers, through which raw materials are acquired then
transformed to produce and deliver to the end customers. Such a supply chain network must
satisfy customers demands at specified service levels and at the lowest possible cost.
Demand forecasting is the main element to effectiveness and efficiency. However, as
large number of varied models and products are marketed through a super market, several
factors affect forecasting. Traditional forecasting approaches will not provide good
estimation of demand. So, the demand was forecasted using Artificial Neural Networks
which can reduce the errors that caused during forecasting.
A poor forecasting model for the product demands in market may cause to decrease in
the competitive capability, it also lose customers and increase costs. A real case in the
product demand forecasting was studied by an artificial neural network (ANN) approach
which is demonstrated in this paper.
Interest in using artificial neural networks (ANNs) for forecasting has led to atremendous change in research activities since the last decade. While, ANNs are there
provide a great deal of promise, they also embody much uncertainty. Researchers, to date are
still not able to understand about the effects of key factors on forecasting performance of
ANNs.
At times it is very difficult to understand the human behaviour, with the changes in
human behaviour there may be demand or lack of demand for the product in the market. As
ANNs learn from their past experience so, it can greatly improve the efficiency in market
demands. This can eliminate the uncertainties in market demand and it can avoid limitations
which are human error.
This is an attempt to show that how technology is being used in management.
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VIII