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7/30/2019 Disseration Title New

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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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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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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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