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A MAJOR PROJECT REPORT

ON

A study on optimization of billing process in Coupon Mall, Bhilai

Submitted in fulfilment for the award of the degree

Master of Business Administration

Chhattisgarh Swami Vivekanand Technical University, Bhilai

Submitted by,

AMARNATH DHAMGAYA

MBA – Semester IV

(Session 2010-11)

Approved By,Dr. Sumeet Gupta

Head of the Department

Guided By,Dr. Sumeet GuptaHOD MBA, SSITM

Shri Shankaracharya Institute of Technology and Management

Junwani, Bhilai (C.G.) - 490020

ANNEXURE II: DECLARATION BY THE CANDIDATE

I the undersigned solemnly declare that the report of the thesis work entitled A study on optimization of

billing process in Cupon Mall, Bhilai is based on my own work carried out during the course of my study

under the supervision of Dr. Sumeet Gupta.

I assert that the statements made and conclusions drawn are an outcome of my research work. I

further declare that to the best of my knowledge and belief the report does not contain any part of any

work which has been submitted for the award of MBA degree in this University of India.

_________________(Signature of the Candidate)

Amarnath DhamgayaRoll No:5353609003

ANNEXURE III: CERTIFICATE FROM THE SUPERVISOR

This is to certify that the work incorporated in the thesis A study on optimization of billing process in

Cupon Mall, Bhilai is a record of research work carried out by Amarnath Dhamgaya bearing Enrollment No.:

55353609003 under my guidance and supervision for the fulfillment for the award of MBA Degree of

Chhattisgarh Swami Vivekanand Technical University, Bhilai (C.G.), India.

To the best of my knowledge and belief the thesis

i) Embodies the work of the candidate him/herself,

ii) Has duly been completed,

iii) Is up to the desired standard both in respect of contents and language for external viva.

_________________

(Signature of the Supervisor)Dr. Sumeet GuptaHOD MBA, SSITM

ANNEXURE IV: TABLE OF CONTENTS (SAMPLE FORMAT)

Declaration by the StudentCertificate from the Supervisor / CompanyAcknowledgmentsChapter 1. Introduction to the study Chapter 2. Company ProfileChapter 3. Literature ReviewChapter 4. Research Methodology Chapter 5. Data Tabulation, Analysis and ResultsChapter 6. Findings of the studyChapter 7. Recommendations Chapter 8. Limitations Chapter 9. Conclusions

References Appendices

a. Research Progress Reportb. Questionnaire usedc. Any other appendix

CHAPTER 1: INTRODUCTION

Abstract: This essay is intended to introduce general idea about queuing theory and its simplest model

M/M/1 to first year undergraduate students with some calculus and basic probability distribution

background. In this essay I first try to provide the reader the basic sense of the queuing theory and its

applications in practice. The second major body of this essay presents M/M/1 model the simplest in queuing

theory. Optimization is used to find out the best possible alternative way for doing anything with some

given constraints. Queuing theory is applied to optimize the number of servers in the billing process so that

the queue length, waiting time of customer and the ideal time of the server is minimized. By this study we

try to suggest the best suitable shift for the server at billing process and the number of billing counter

required to open during that shift so that the queue length is minimized and hence the waiting time of the

customer in the queue is reduced.

CHAPTER 2: COMPANY PROFILE

Introduction of Company

Prateek Lifestyle opens Coupon mall-

Bangalore, June 27 Prateek Lifestyle, the retail arm of Prateek (a player in apparel manufacturing and

design), has announced its first retail venture – Coupon, a chain of value lifestyle malls. The first ‘Coupon’

mall has been set up in Bangalore.

Spread over four floors, the 50,000-sq. ft mall promises to offer around 140 brands in categories such as

apparel, footwear, accessories and home furnishing at discounted prices. Mr Pradeep Agarwal, Managing

Director, Prateek, said, “Through this venture we will provide the best of national and international brands

across categories at the best of prices with discounts ranging from 30 per cent to 55 per cent. We will also

soon launch our private label.” Mr Agarwal said the plan is to open a chain of Coupon malls across the

country to tap the organised value retailing segment, which is currently estimated at Rs 6,000-8,000 crore.

The second Coupon mall will come up in Ahmedabad by mid-August. Prateek Lifestyle will roll out 12 malls

by March 2008 in 10-12 cities such as Kolkata, Chennai, Hyderabad, Mumbai, Delhi and Pune. It hopes to

have 50 malls by 2010. “By the end of March 2008, with 12 malls, we hope to have revenues of Rs 250-300

crore,” said Mr Agarwal. The total investment in this venture is about Rs 500 crore.

Prateek Lifestyle also has plans to enter other apparel verticals – kidswear and lingerie. After ‘Coupon’, the

company’s next retail project will be the launch of kidswear range Kanz from Germany, followed by a tie-up

with a European lingerie brand. Prateek Lifestyle aims to have 100 retail outlets by the end of this financial

year in all three retail verticals. Prateek’s other divisions are Prateek Apparels, which supplies apparel to the

domestic market for clients like Westside, Arvind and Madura Garments and Munch Design Worx.

Mumbai : Prateek Lifestyle, the only domestic player in the apparel design, manufacturing and retail space

has announced the launch of its ''Coupon'' mall in Raipur.

Coupon is a unique venture by Prateek Lifestyle, the newly formed retail arm of Prateek.

Established in 1995, Prateek today is the largest manufacturer of apparel for the domestic players, and

according to the company is also one of the most successful enterprises of the Phulchand group. The group

is managed by Pradeep Agarwal, managing director, with CEO''s responsible for each vertical of Prateek

Lifestyle, Prateek Apparels and Munch Design Worx.

On the anvil are plans for entering other apparel verticals during this fiscal. Prateek has entered the kids

wear segment with the European kids wear brand Kanz, and plans to enter the lingerie segment this year

through tie-ups with leading European brands.

Prateek Lifestyle aims to have over 100 retail outlets by the end of this financial year across these three

retail verticals.

The Raipur store is the second in a chain of Value Lifestyle Malls, the first one having launched earlier in

Bangalore in June 2007.

According to Pradeep Agarwal, managing director, Prateek, the company has been synonymous with

international quality and standards of service, and that shall be extended to Coupon as well.

He said, "Through this venture, we will provide the best of national and international brands across

categories at the best of prices. We have already tied up with all major apparel brands in India. We are also

the first to get the popular My Dollar Store into Raipur. Having been known as an apparel & fashion

powerhouse, it is this unparalleled understanding of the industry that has enabled us to venture into this

space. Raipur is an important market for us as it has been showing a lot of potential on the retail front. The

consumers here are educated and wield strong buying power. We aim to expand our presence across India.

We will not restrict ourselves only to metros."

Adding further, Agarwal said Prateek Lifestyle intends to revolutionise the retail industry in India with this

new concept. Other than an international shopping experience, consumers also get to participate in various

unique and exciting offers and promotions that would be held regularly. He says that unlike other malls, at

Coupon customers get the best of products at discounted prices throughout the year.

According to a release by the company, it plans to ramp up operations and will roll out 10 to 12 outlets

nationally in six months.

Coupon will cater to men, women and children with its range of apparel, footwear and accessories, and will

have separate sections for home furnishings and general merchandise.

Spread over 2 floors with a total floor space of around 20,000sq feet, Coupon will offer an unparalleled

international retailing experience with the best of brands at best discounts across categories under one

roof.

CHAPTER 3: LITERATURE REVIEW

Introduction

In general we do not like to wait. We would like to be served instantly as we enter a service system. But from

the managers’ point of view, reduction of the waiting time usually requires extra investments. To decide

whether or not to invest, it is important to know the effect of the investment on the waiting time. So we

need models and techniques to analyse such situations. Queuing systems constitute a central tool in

modelling and performance analysis of e.g. production systems, transportation and stocking systems,

communication systems and information processing systems. Queuing models are particularly useful for the

design of these systems in terms of layout, capacities and control.

What are queue models?

Queuing models are often represented by diagrams like the one below. A source (the population) generates

new customers which arrive in the system, join the queue in front of a server (grocery store, clinic, etc)

where they wait until they are served by the server. After being served they leave the system at the sink.

The basic queuing model can be used to model, e.g., machines or operators processing orders or

communication equipment processing information. Among others, a queuing model is characterized by:

• The arrival process of customers.

Usually we assume that the inter arrival times are independent and have a common distribution. In many

practical situations customers arrive according to a Poisson stream (i.e. exponential inter arrival times).

Customers may arrive one by one, or in batches. An example of batch arrivals is the customers once at the

security door where ID card of new arrivals has to be checked.

• The behaviour of customers.

Customers may be patient and willing to wait (for a long time). Or customers may be impatient and leave

after a while without being served. For example, in call-centers, customers will hang up when they have

waited too long before an operator is available, and they possibly try again after a while.

• The service times.

Usually the service times are assumed to be iid (independent and identically distributed), and that they are

independent of the interarrival times. For example, the service times can be exponentially distributed. It can

also occur that service times are dependent of the queue length. For example, the processing rates of the

machines in a production system can be increased once the number of jobs waiting to be processed

becomes too large.

• The service discipline.

Customers can be served one by one or in batches. We have many possibilities for the order in which they

enter service. We mention:

-- First come first served, i.e. in order of arrival;

-- Random order;

-- Last come first served (e.g. in a computer stack or a shunt buffer in a production line);

-- Priorities (e.g. rush orders first, shortest processing time first);

-- Processor sharing (in computers that equally divide their processing power overall jobs in the system).

• The service capacity.

There may be a single server or a group of servers helping the customers.

• The waiting room.

There can be limitations with respect to the number of customers in the system. For example, in a data

communication network, only finitely many cells can be buffered in a switch. The determination of good

buffer sizes is an important issue in the design of these networks.

A three-part code “a/b/c” notation was introduced by Kendall to characterize a range of these queuing

models. The first letter specifies the interarrival time distribution and the second one the service time

distribution. For example, for a general distribution the letter G is used, M for the exponential distribution

(M stands for Memoryless) and D for deterministic times. The third and last letter specifies the number of

servers. Some examples are M/M/1, M/M/c, M/G/1, G/M/1 and M/D/1. The notation can be extended with

an extra letter to cover other queuing models. For example, a system with exponential interarrival and

service times, one server and having waiting room only for N customers (including the one in service) is

abbreviated by the four letter code M/M/1/N.

In the basic model, customers arrive one by one and they are always allowed to enter the system, there is

always room, there are no priority rules and customers are served in order of arrival. It will be explicitly

indicated (e.g. by additional letters) when one of these assumptions does not hold.

Occupation rate:

In a single-server system G/G/1 with arrival rate λ and mean service time E(B) the amount of work arriving

per unit time equals λE(B). The server can handle 1 unit work per unit time. To avoid that the queue

eventually grows to infinity, we have to require that λE(B) < 1. Without going into details, we note that the

mean queue length also explodes when λE(B) = 1, except in the D/D/1 system, i.e., the system with no

randomness at all.

It is common to use the notation ρ = λE(B). If ρ < 1, then is called the occupation rate ρ or server utilization,

because it is the fraction of time the server is working. In a multi-server system G/G/c we have to require

that λE(B) < c. Here the occupation rate per server is ρ = λE(B)=c.

M/M/1 model

Before touching the M/M/1 queue, we need to first have a look at the Markovian system. All Markovian

systems have a common characteristic that the distribution of the interarrival times and the distribution of

the service times are exponential distributions. Therefore, the memoryless property can also be applied for

the Markovian systems. From this property we have two important conclusions:

• The state of the system can be summarized in a single variable, namely the number of customers in

the system. (If the service time distribution is not memoryless, this is not longer true, since not only the

number of customers in the system is needed, but also the remaining service time of the customer in

service.)

The M/M/1-Queue has iid interarrival times, which are exponentially distributed with parameter λ and also

identically independent distribution (iid) service times with exponential distribution with parameter µ. The

M/M/1 system is a pure birth-death system, where at any point in time at most one event occurs, with an

event either being the arrival of a new customer or the completion of a customer’s service. What makes the

M/M/1 system really simple is that the arrival rate and the service rate are not state-dependent.

The M/M/1 model is characterized by the following assumptions:

-- Jobs arrive according to a Poisson process with parameter λ t, or equivalently, the time between arrivals,

t, has an exponential distribution with parameter λ, i.e., for t ≥ 0, the probability density function is f(t) = λe-λt

-- The service time, s, has an exponential distribution with parameter µ, i.e., for s ≥ 0, the probability density

function is g(t) = µe-µt

-- There is a single server.

-- The buffer is of infinite size; and

-- The number of potential jobs is infinite.

Little’s Formula. In a steady state, the average time spent waiting in the queue,

Wq = Lq/λ, and the average time spent in the system (in queue or process), W = L / λ

Appling Little’s formula we can get W = 1 / (µ - λ) and Wq = λ / µ (µ - λ)

We can now have a look at the following simple example to better understand how M/M/1 model can be

used in practice. Assume a drive-up window at a fast food restaurant. Customers arrive at the rate of 25 per

hour. The employee can serve one customer every two minutes. Assume variable arrivals and variable

service rate.

Other Types of Queueing Models

M/M/m — exponential arrival rate and service times, with m servers (like grocery store with many checkout

lanes).

M/M/m/m — exponential arrival rate and service times, with m servers, but nobody waits in queue (if all m

servers are busy when a customer arrives, that customer gives up and leaves).

M/M/ — exponential arrival rate and service times, with unlimited number of 8 servers (customers never

wait in queue).

M/D/1 —service times are deterministic (e.g. a constant, fixed service time regardless of customer).

M/G/1 — exponential arrival rate, but service rate has a “general” (arbitrary) probability distribution, and a

single server.

M/G/m — same as M/G/1, but with m servers.

CHAPTER 4: RESEARCH METHODOLOGY

Section A. Objective

1. To study the current billing process.

2. To optimize the billing process using queuing theory

Section B.

RESEARCH PLAN

Research Design: Descriptive

Research Method Used Observation

Research Technique Used Queuing Theory

Data Collection Coupon Mall, Bhilai

Sampling Plan Convenience

No. of samples collected 14 days

CHAPTER 5: DATA TABULATION AND ANALYSIS

Data Tabulation

Analysis

Product Customer

1. As we see that the queue length is less and waiting time is more it shows that there are less

customer/product in the queue but they have to wait more to be served.

2. Waiting time in the system is very high as compare to waiting time in the queue which indicates that

the service time is very high.

3. As the idle time is much greater then traffic intensity it shows that the number of server is more

than the requirement.

4. In case of customer we see that the difference between waiting time in the system and waiting time

in queue is very high i.e. waiting time in the system is greater than waiting time in queue shows that

the customer have to wait more in the service window because of high service time whereas the

queue length is very small due to that the maximum utilization of the server is not possible.

5. The analysis also tells that the arrival rate of customer is very low but due to high service time the

customer in the system is high.

Counter No 1 2 3 4Traffic intensity (λ/µ) 0.04 0.04 0.04 0.03

Lq (ρ/1-ρ) 0.05 0.04 0.04 0.03Wq (Lq/λ) 0.27 0.24 0.25 0.19Ls ( 1/1-ρ) 1.05 1.04 1.04 1.03Ws (Ls/λ) 6.12 6.08 6.1 6.03

idle time (1-ρ) 0.96 0.96 0.96 0.97

Counter No 1 2 3 4Traffic intensity (λ/µ) 0.21 0.22 0.19 0.21

Lq (ρ/1-ρ) 0.26 0.28 0.23 0.27Wq (Lq/λ) 0.64 0.7 0.56 0.67Ls ( 1/1-ρ) 1.26 1.28 1.23 1.27Ws (Ls/λ) 3.1 3.16 3.02 3.13

idle time (1-ρ) 0.79 0.78 0.81 0.79

CHAPTER 6: FINDINGS

On the basis of this observational study the following are the findings:

1. The service time of the billing server 4 is slow as compare to other server.

2. Numbers of billing counter are more than that are required.

3. The delay in service is mainly due to following reasons.

a. Manual entry of the discount offered in the products.

b. The men at the server are unaware about the discount available in certain product.

c. Product barcode in not stored in the database so the manual entry of the product is

required.

d. Barcode tag is not there in many products.

e. Product change is done in the billing counter although there is an extra counter for

exchange.

f. Feedback is taken in the billing counter.

CHAPTER 7: RECOMMENDATIONS

There are some recommendations for the coupon mall on the basis of my study to improve their billing

process.

1. The database must maintain properly so that the problem of missing product or change in discounts

or offer is available at the time of billing.

2. Exchange related matter should be handled in separate counter. And the person of counter number

4 is placed in exchange counter

3. Number of counter should be reduced from 4 to 3 to get efficient utilization.

4. Feedback is taken at the time of exit not in the billing counter.

5. The entire product must have barcode tag. To minimize the delay at the time of billing.

CHAPTER 8: LIMITATIONS

Limitations of this study are:

1. Time for this study is the major limitation.2. It is very difficult to observe all the counters and the entire customer individually, alone.

CHAPTER 8: CONCLUSION

This study helps me a lot to understand about the practical implementation of queuing system and also to

understand the problem facing by the coupon mall in their billing system. By this study I try to find the

problems in the billing process of coupon mall, bhilai, and try to suggest some ways to minimize those

problem so that the level of customer satisfaction increase. For any organized retail shop customer

satisfaction is the most important factor and it helps a lot in customer retainsion.

Queuing Theory tries to answer questions about the performance of a system like e.g. the mean waiting

time in the queue, the mean system response time (waiting time in the queue plus service times), mean

utilization of the service facility, distribution of the number of customers in the queue, distribution of the

number of customers in the system and so forth. Actually the most operations systems are much more

complicate to be analyzed for which more complex models or networks of queue could work pretty well.

However, to clearly understand these queue models requires readers more advanced calculus and

probability preparation.

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