The Study of Waiting Line Management With Reference to Big Bazar

8/14/2019 The Study of Waiting Line Management With Reference to Big Bazar

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The Study of Waiting Line Management With Reference To Big

Bazar

Submitted in partial fulfillment of the requirements

For the award of the degree of

Master of Business Administration

In

Software Enterprise Management

Under the guidance of

Internal Guide and Supervisor

Mrs. Shipra Sharma


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ABSTRACT

Pantaloons Retail Limited is Indias leading retailer that operates

multiple retail formats in both the value and lifestyle segment of the

Indian consumer maker. Pantaloons include a chain of fashion outlets

that is Big Bazaar, a uniquely Indian hypermarket chain, Food Bazaar, a

supermarket chain, blends the look, touch and feel of Indian bazaars

with aspects of modern retail like choice, convenience and quality.

The research project deals about the waiting line management at retail

outlets like Big Bazaar. It basically stresses on the problems faced by the

organizations in managing the waiting lines throughout the day,

especially at the peak hours and festival season.

The scope of my project involves understanding the concept of waiting

lines and find out better ways to manage them. It aims at studying the

various techniques adopted for managing waiting lines at retail stores

like Big Bazaar and see whether it is effective enough to serve thenumber of footfalls.

The deliverable on the completion of my project will serves as input for

the subsequent phases and will give a better understanding of waiting

line management and its implementation.


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ACKNOWLEDGMENT

It is really a matter of pleasure for me to get an opportunity to thank allthe persons who contributed directly or indirectly for the successfulcompletion of the project report, Study of Waiting Line Management

With Reference To Big Bazaar.

First of all I am extremely thankful to my college CDAC for providingme with this opportunity and for all its cooperation and contribution. Ialso express my gratitude to my Project mentor and guide Mrs. ShipraSharma. I am highly thankful to our respected project guide for giving

me the encouragement and freedom to conduct my project.

I am also grateful to all my faculty members for their valuable guidanceand suggestions for my entire study.

I would also like to thank the Big Bazaar team for extending theirvaluable time and cooperation.

Neeta KumariRoll no. : 03611809912


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Table of ContentsExecutive summary.

1. Introduction........5

1.1.Companys Profile...5

1.2.Organisation Structure ......................................................................9

1.3.Purpose of the Study .......................................................................10

1.4.Significance of the Study................................................................ 11

1.5. Objective ........................................................................................12

1.6.Scope ............................................................................................... 13

2.1. Indian Retail Market ...................................................................... 14

2.2.Big Bazaar Retail Life Cycle .......................................................... 16

2.3.Service Time Distribution .............................................................. 17

3.Research Methodology ...................................................................... 20

4. Analysis of Data ............................................................................. 362

5.Conclusion ......................................................................................... 30

6.Bibliography.32


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List of Tables and Figures


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the length of lines and time customers spend in checkout lanes. Early

research in this area concerned identifying theoretical models to explain

how consumers perceive longer than expected waits in checkout lines.

Lewins (1943) early field theory suggested that when perceived

waiting.

Self-service machines are multiplying in grocery and discount stores at a

blistering speed. Self-service is fast becoming a viable alternative to

conducting transactions, from ATM machines to gas pumps to self-

checkout at retail stores. Customers are demanding better and fasterservice, and the scarcity and cost of labor are leading to more and more

businesses exploring this alternative. The problem confronting managers

is the limited source of available information to help them in their efforts

to choose among checkout systems. Managerial decision making

processes are more complex because front end service requirements vary

among stores. So, grocery store managers are constantly exploring

different queuing techniques and evaluating current technology to help

them make better strategic decisions in their choices of POS (point of

sales) checkout terminals.

Research shows that studies have been done in the area of how

customers time spent in waiting lines affects customers behavior andthere are limited studies on how managers choose appropriate checkout

systems for their businesses. Hkust and Hkust (2002), in their study

expressed that limited research has been conducted to determine how


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service waits can be controlled. They suggested that, to control the time

customers wait in line, researchers must determine the factors that cause

more than expected wait time in checkout lines. Researchers argue that

service waits can be controlled by two techniques: operations

management or perception management (Katz, Larson, & Larson, 1991).

By conducting this research, A Comparative Study of the Electronic

Self Checkout System and the Cashier Operated checkout System

managers will have available some added information to help them in

their decisions between choosing among checkout systems.The project was carried out in with an objective of knowing satisfaction

level of customer at Big Bazaar and do customers are aware about the

different types product and Services and different offers provide at Big

Bazaar. The total sample size taken was one hundred (100) from various

customers of Allahabad at Big Bazaar. The research shows that the

customer satisfaction at Big Bazaar is very good and so many customers

are not aware of the product and services provided by the Big Bazaar

which are not provided by other Retail stores. On the other hand we

have also the existing customers of Big Bazaar who are satisfied with

the working style of retail store, but want continuous updates about the

new offers and other products of Big Bazaar.The India Retail Industry is the largest among all the industries,

accounting for over 10 per cent of the country GDP and around 8 per

cent of the employment. The Retail Industry in India has come forth as


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one of the most dynamic and fast paced industries with several players

entering the market. But all of them have not yet tasted success because

of the heavy initial investments that are required to break even with

other companies and compete with them. The India Retail Industry is

gradually inching its way towards becoming the next boom industry.

The total concept and idea of shopping has undergone an attention

drawing change in terms of format and consumer buying behavior,

ushering in a revolution in shopping in India. Modern retailing has

entered into the Retail market in India as is observed in the form ofbustling shopping centers, multi-storied malls and the huge complexes

that offer shopping, entertainment and food all under one roof.

A large young working population with median age of 24 years, nuclear

families in urban areas, along with increasing workingwomen population

and emerging opportunities in the services sector are going to be the key

factors in the growth of the organized Retail sector in India. The growth

pattern in organized retailing and in the consumption made by the Indian

population will follow a rising graph helping the newer businessmen to

enter the India Retail Industry.

In India the vast middle class and its almost untapped retail industry are

the key attractive forces for global retail giants wanting to enter intonewer markets, which in turn will help the India Retail Industry to grow

faster. Indian retail is expected to grow 25 per cent annually. Modern

retail in India could be worth US$ 175-200 billion by 2016. The Food


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Retail Industry in India dominates the shopping basket. The Mobile

phone Retail Industry in India is already a US$ 16.7 billion business,

growing at over 20 per cent per year. The future of the India Retail

Industry looks promising with the growing of the market, with the

government policies becoming more favorable and the emerging

technologies facilitating operations.


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Introduction

1.1.Company Profile

Big Bazaar comes under the Pantaloon Retail India Limited (PRIL).

PRIL was early to realize the potential of the huge middle-class

population in India. We started the operations with a trouser brand,

Pantaloon. In the initial stages we had small format outlets branded

Pantaloon Shopee, which were franchise operations realizing theproblems associated with franchise model, we decided to have our own

retail outlets. They launched the own retail store, Pantaloons. In

1997, they launched Big-Bazaar a hypermarket with over 1, 70,000

products as the first offering in value retailing segment. They have

introduced the concept of seamless malls in India through the new

format Central. We have wide network of Pantaloons stores spread

across the country.

Hence, apart from retailing lifestyle products, it ventured into value

retailing by launching the hypermarket chain. Big Bazaar is a chain that

stocks all home need products under one roof; spread over 30,000 square

feet of land, across different cities in India. It has been positioned as Is

se sasta aur acha kahin nahi, (Nothing cheaper and better anywhere)

indicating the value of stores. Products are cheaper than the market price


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by as much as 5 to 60%. Apparels are cheaper by 25 to 60%while the

price difference on the other products varies between 5 to 20%.

On Oct. 12, 2001, we launched Big-Bazaar as offering in the valueretailing segment. By removing inefficiencies from the distribution chain

we are able to unleash attractive savings, which are passed on to the

consumer. Big-Bazaar is Indias first hypermarket in the discount store

format. Big-Bazaar provides more than 2,00,000 items- food, grocery,

utensils, kitchen needs, home needs, bath needs, toys, stationary,

electronics & white goods which are sold at a discount to the maximum

retail price. Price is the principal value proposition at these stores.

A big driver of the Big Bazaar is the product variety. This is achieved by

selling wide range of products & through the Shop-in-Shop format. As

a result, a typical Big-Bazaar comprises shops that stocks medicines,

optical accessories, camera rolls, bakery products, dry fruits, crockery,

glassware, health & beauty products, ladies accessories, electronics

infant necessities, watches, clocks, computer accessories, food &

beverages, stationary, readymade garments, household appliances, home

furnishings, baggage We believe this is a win-win situation as the

customer is assured of product availability, the shop owner can benefit

of the in structure & we enjoy assured income without needing to stock

inventory. Also the shop-in-shop offering is able to increase the

customer traffic in to the stores. The Big-Bazaar has been positioned to


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the customer as a place where the customer can shop for each &

everything for which if goes to a market.

They have also launched private label initiative in Big-Bazaar.Understanding of the apparel industrial, decades of experience& a

vertically, integrated structure provides with more compelling reasons to

expand the number of private labels. We have launched a full range of

accessories to supplement the apparel business including imitation

jewellery, sunglasses, watches, mobile phones etc

Analysts attribute the success of PRIL to cheaper sourcing of products

and lower distribution cost. Pantaloons sourced its products through

Consolidators. There was a consolidator for each product category.

These consolidators were responsible for procuring quality goods at the

cheapest possible price, and were paid commissions on their sale at the

store. The consolidator directly dealt with manufacturers, and as a result

the distribution cost could be slashed as no intermediates were involved.

In addition to discounts on products through the year, Big Bazaar also

held events such as Kitchen Mela, Trouser Mela, etc. to attract

customers.


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http://bp3.blogger.com/_RoZOCtRoIGc/RoJr3EfPZdI/AAAAAAAAAKU/C57NqlUBCs4/s1600-h/Pantaloon-Retail-All-Brands.png


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Organisation Structure


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1.3. Purpose of the Study

The current competitive market and the development of a dizzying array

of electronic payment technologies in recent years, however, has

dramatically raised the profile of point of sales systems as key

competitive weapons within the retail industry. Managers are frequently

confronted with the problem of deciding whether to increase the number

of cashier checkout counters or replace them with the new electronic

checkout machines.

This study addresses some of the host of factors that can help managers

to decide which customer checkout system is better or best for their store

and the customers. The study will investigate such factors as the number

of items checked out by customers in each system within a specified

time period and the average time it takes to check out each customer

within each system. The research will also investigate factors dealingwith error rates, managers and customers affective reactions and

confidence.

Furthermore, the study will assess and compare acquisition,

implementation and operation costs for each system within a specified

period of time. The answer to these questions will complement the

information managers require when making strategic decisions on their

choices of checkout system. The rationale behind the investigation of the

factors includes the average time it takes to checkout each customer that

enters the queue, the number of items checked out by each customer, the


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error rate calculated for each system, the operational cost associated with

each system, and the level of affective reactions and confidence of

customers and managers selected for this research.

1.4. Significance of the Study

The retail business has experienced a steady increase in the level of

competitiveness within the industry. Managers of retail businesses are

always confronted with the problem of improving customer checkout

systems, thereby increasing their customers satisfaction, maintaining a

good customer base, and increasing company profits. It is becoming a

widespread belief among retailers that there is a positive correlation

between profit and good customer service. Also, research has shown that

consumer buying patterns are highly influenced by how long they think

they have to wait in line to be checked out, or to receive services in a

business.

The following issues make this study significant: (1) Businesses are very

concerned about how efficiently their checkout systems work and are

always in search of ways to improve them. (2) Consumers want fast

checkout lanes: consequently the length of time a customer waits in lineto be checked out may influence the choice of a store in which he or she

shops.


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(3) Influences from current advances and changes in retail and

supermarket checkout technology have increased managerial problems

in choosing among alternative methods of checkout.

(4) Managers of retail businesses are seeking information that can help

them in their decision-making process when choosing appropriate

checkout systems. (5) There is a paucity of academic research studies

comparing differences in checkout systems. (6) Findings from this study

will add to the limited body of literature that could help managers make

better strategic decisions in their choices for selection of the better

checkout system. Finally and most importantly, there are very few retail

businesses in Jackson, Mississippi that have the electronic self-checkout

machines. Most of these stores are just starting to install, or are

contemplating replacing some of their traditional cashier operated

checkout terminals with the electronic self-checkout machines. Thisresearch will add to the level of needed information store managers

require to make better decisions among their choices of checkout

systems.

1.5. Objectives

Specifically the objectives of the report can be listed as:

To study and understand the significance of Waiting Linemanagement in the current business environment


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1.6. Scope

The scope of my project involves understanding the concept of waiting

lines and find out better ways to manage them. It aims at studying the

various techniques adopted for managing waiting lines at retail storeslike Big Bazaar and see whether it is effective enough to serve the

number of footfalls.


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2.1.Indian Retail Market

The retail market in India is estimated at about US$ 410 billion and

constitutes about 60% of private consumption and about 35% of India's

GDP. With Indian GDP expected to grow at 7-8 % in the next coming

years, the retail market is expected to touch US $860 billion by 2018. In

recent years, this sector has witnessed a lot of interest from both

domestic and global players, who have committed investments worth US

$30 billion, which will lead to increase in the share of modern retail

from the current 4.5% to almost 25% of the total retail market by 2018.

The Indian retail market is the fifth largest retail destination globally.

The current size of the Indian retail industry stands at $511 billion in

2013. Simultaneously, modern retail is likely to increase its share in the


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total retail market to 22 per cent by 2014. Organized retail in India raked

in US$ 25.44 billion turnover in 2007-08 as against US$ 16.99 billion in

2006-07, a whopping growth rate of 49.73 per cent (according to the

Credit Rating and Information Services of India). Organized retail has

increased its share from 5 per cent of total retail sales in 2011 to 8 per

cent in 2012. It is currently around 12 per cent. India has one of the

largest numbers of retail outlets in the world. Of the 12 million retail

outlets present in the country, nearly 5 million sell food and related

products.

Though the market has been dominated by unorganized players, the

entry of domestic and international organized players is set to change the

scenario. Per capita retailing space is about 2 sq. ft (compared to 16 sq.

ft in the U S). India's per capita retailing space is thus the lowest in the

world. Around 7% of the population in India is engaged in retailing, ascompared to 20% in the USA.

Statistically, the global retail industry is witnessing a CAGR of 5.5% is

slated to grow at the same rate till 2012. The above graph shows an

overall trend of the global retail revenues.


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2.2.Big Bazaar retail life cycle

The following graph shows the retail life cycle and we can say that Big

Bazaar is currently at the Growth Stage.

Introduction

Growth

Maturity

Decline

Time

Cash flow


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Service Capacity versus Waiting Line Trade-Off

Arrival and Service Profiles

2.3. Service Time Distribution

Another important feature of the waiting structure is the time the

customer or unit spends with the server once the service has started.


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Waiting line formulas generally specify service rate as the capacity of

the server in number of units per time period (such as 12 completions

per hour) and not as service time, which might average five minutes

each. A constant service time rule states that each service takes exactly

the same time. As in constant arrivals, this characteristic is generally

limited to machine-controlled operations.

When service times are random, they can be approximated by the

exponential distribution. When using the exponential distribution as an

approximation of the service times, we will refer to as the average

number of units or customers that can be served per time period. Line

Structures As Exhibit shows, the flow of items to be serviced may go

through a single line, multiple lines, or some mixtures of the two. The

choice of format depends partly on the volume of customers served and

partly on the restrictions imposed by sequential requirements governingthe order in which service must be performed.

1. Single channel, single phase This is the simplest type of waiting line

structure, and straightforward formulas are available to solve the

problem for standard distribution patterns of arrival and service. When

the distributions are nonstandard, the problem is easily solved by

computer simulation. A typical example of a single-channel, single-

phase situation is the one-person barbershop.


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2. Single channel, multiphase A car wash is an illustration because a

series of ser-vices (vacuuming, wetting, washing, rinsing, drying,

window cleaning, and parking) is per-formed in a fairly uniform

sequence.


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3.1.Research Methodology

The term waiting line system is used to indicate a collection of one or

more waiting lines along with a server or collection of servers thatprovide service to these waiting lines. The example of ICA supermarket

is taken for waiting line system discussed in this chapter include: 1) a

single waiting line and multiple servers (fig.1), 2) multiple waiting lines

(arranged by priority) and multiple servers (fig.2) , and 3) a single

waiting line and a single server (fig.3). All results are presented in next

chapter assuming that FIFO is the waiting line discipline in all waiting

lines and the behavior of queues is jockey The supermarkets may consist

of multiple units to perform same checkout operation of sales, which are

usually set all together besides the entrance of the supermarket. Each

unit contains one employee. This kind of a system is called a multiple-

server system with single service facility, in other words multiplecheckouts counters (service units) with sales checkout as a service

available in a system. There are two possible models for multiple-server

system: Single-Queue Multiple-Server model, and Multiple-Queue

Multiple-Server model.

Using the same concept of model, the sales checkout operating units are

all together taken as a series of servers that forms either single queue or

multiple queues for sales checkout (single service facility) where the

arrival rate of customers in a waiting line system and service rate per


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busy server are constants regardless of the state of the system (busy or

idle).


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4. Analysis of Data

Customers are served on a first-come, first-served (FIFO) basis as a

salesman of checkout operation unit becomes free. The data has been

collected for only two out of five servers on Wednesday (weekday) by

using questionnaires (Appendix A). It was assumed that the customers

crowd is more, on average, on weekday. Although the sales checkout

unit has 5 parallel counters out of which 2 were observed (each of them

has an individual salesman to deal with the customers in a queue), it is

possible that some of the checkout units are idle. The data collected from

questionnaires were tabulated in a spreadsheet in order to calculate the

required parameters of queuing theory analysis (Appendix B). Firstly,

the confidence intervals are computed to estimate service rate and arrival

rate for the customers. Then the later first part of the analysis is done for

the model involving one queue and 2 parallel servers (fig.1), whereas the

second part is done by queuing simulation for second model involving 2

queues for each corresponding parallel server (fig.2).

We can estimate confidence intervals for average service rate and

average arrival rate. Assuming service time and arrival time are id with

N (0,1), then the

95% confidence interval for arrival rate can be:

Confidence Intervals


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Similarly, 95% confidence interval for service rate can be:

Confidence Intervals for weekday:

We have,

Mean (service time) = 01:06 minutes per customer (read clock as

min:sec)

SD (service time) = 00:06 min

Mean (arrival time) = 00:37 min per customer

SD (arrival time) = 00:06 min

And n = 41 customers

95% Confidence Intervals for Service Time:


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Mean (service time) - 1.96 (SE(service time)) = 54 sec/customer

Mean (service time) + 1.96 (SE(service time)) = 78 sec/customer

SE = SD/sqrt(n)

95% Confidence Intervals for Service Rate:

1/[Mean(service time) + 1.96 (SE(service time))] = 0.01282 = 46

customers/sec

1/[Mean(service time) - 1.96 (SE(service time))] = 0.01852 = 67

customers/sec** **(0.01852 sec *60 *60)

95% Confidence Intervals for Arrival Time:

Mean (arrival time)1.96 (SE(arrival time)) = 24 sec /customer

Mean (arrival time) + 1.96 (SE(arrival time)) = 49 sec /customer

95% Confidence Intervals for Arrival Rate:

1/[Mean(arrival time) + 1.96 (SE(arrival time))] = 0.02041

= 73 customers/sec**

1/[Mean(arrival time) - 1.96 (SE(arrival time))] = 0.04167 = 150

customers/sec **(0.02041 sec *60 *60)

Interpretation of confidence intervals


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The confidence intervals show that 73 to 150 customers arrive in 2-

server system within an hour whereas 46 to 67 customers are served.

That means there are still some customers not being served and are

waiting for their turn in a queue to be served. This is due to a service

time provided by a server to the customers. The service time can vary

between 54 sec to 78 sec per customer.

Expected Queue Length

We can find the expected length of queue by using empirical data. In

survey, the number of customers waiting in a queue was observed

( Appendix B ) . The average of that number in a system is

(1+1+3++2+0)/41 = 2.07 customers per minute on average waiting in

a queue in asystem within 25 min of data collection time.

Queuing Analysis

On Wednesday (weekday), customers arrive at an average of 98

customers per hour, and an average of 55 customers can be served per

hour by a salesperson.


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Results for Weekday applying Queuing model 1 (fig.1)

The parameters and corresponding characteristics in Queuing Model

M/M/2, assuming system is in steady-state condition, are:

c number of servers = 2

arrival rate = 98 customers per hour

serving rate = 55 customers per server per hour

c (2) (55) = 110 (service rate for 2 servers)


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= /(c) =98 / 110 = 0.8909

= = 1.7818

Overall system utilization = = 89.09 %

The probability that all servers are idle (Po) = 0.5769

Average number of customers in the queue (Lq) = =6.8560

Average time customer spends in the queue (Wq) = Lq/ = 0.0700 hours

Interpretation of results for waiting line management

The performance of the sales checkout service on weekday is

sufficiently good. We can see that the probability for servers to be busy

is 0.8909, i.e. 89.09%. The average number of customers waiting in a

queue is Lq = 6.8560 customers per 2-server. The waiting time in a

queue per server is Wq = 4.2 min which is normal time in a busy server.

This estimate is not realistic as the model shows that the customers make

a single queue and choose an available server. Hence we can consider

each server with a queuing. M/M/1 queue is a useful approximate model

when service times have standard deviation approximately equal to their

means.


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Results for Weekend applying Queuing model 2 (fig.2)

The parameters and corresponding characteristics inQueuing Model

M/M/1, assuming system is in steady-state condition, are:

c number of servers = 1

arrival rate = 98 customers per hour for 2 servers i.e. 49 customers

serving rate = 55 customers per server per hour

= /(c) =(98 2)/ 55 = 0.8909

= = 0.8909 (= in case of c = 1)

Overall system utilization = = 89.09 %

The probability that all servers are idle (Po) = 0.1091


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Average number of customers in the queue (Lq) = 7.2758

Average time customer spends in the queue (Wq) = Lq/ = 0.1485 hours


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5. Conclusion

The retail sector has played a phenomenal role throughout the world in

increasing productivity of consumer goods and services. It is also the

second largest industry in US in terms of numbers of employees and

establishments. There is no denying the fact that most of the developed

economies are very much relying on their retail sector as a locomotive of

growth. The India Retail Industry is the largest among all the industries,

accounting for over 10 per cent of the countrys GDP and around 8 per

cent of the employment. The Retail Industry in India has come forth as

one of the most dynamic and fast paced industries with several players

entering the market. But all of them have not yet tasted success because

of the heavy initial investments that are required to break even with

other companies and compete with them. The India Retail Industry is

gradually inching its way towards becoming the next boom industry.

For launching Big bazaar in India city is companys mission to expand

the business. For this purpose company is carrying out many marketing

strategy. To study this marketing strategy I will be visiting the mall and

also the manager to collect the data from them. Also Ill be collecting

data from the customer as what strategies they liked.

This paper reviews a waiting line management for Big Bazaar. The

average queue length can be estimated simply from raw data from

questionnaires by using the collected number of customers waiting in a


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queue each minute. We can compare this average with that of queuing

model. Three different models are used to estimate a queue length: a

single-queue multi-server model, single-queue single-server and

multiple-queue multi-server model. In case of more than one queue

(multiple queue), customers in any queue switch to shorter queue

(jockey behavior of queue).Therefore, there are no analytical solutions

available for multiple queues and hence queuing simulation is run to find

the estimates for queue length and waiting time.

The empirical analysis of queuing system of Big Bazaar supermarket is

that they may not be very efficient in terms of resources utilization.

Queues form and customers wait even though servers may be idle much

of the time. The fault is not in the model or underlying assumptions. It is

a direct consequence of the variability of the arrival and service

processes. If variability could be eliminated, system could be designedeconomically so that there would be little or no waiting, and hence no

need for queuing models.

With the increasing number of customers coming to Big Bazaar for

shopping, either for usual grocery or for some house wares, there is a

trained employee serving at each service unit. Sales checkout service has

sufficient number of employees (servers) which is helpful during the

peak hours of weekdays. Other than these hours, there is a possibility of

short Queues in a model and hence no need to open all checkouts

counters for each hour. Increasing more than sufficient number of


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servers may not be the solution to increase the efficiency of the service

by each service unit. When servers are analyzed with one queue for two

parallel servers, the results are estimated as per server whereas when

each server is analyzed with its individual queue, the results computed

from simulation are for each server individually.


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Bibliography

Bhatti, S. A., Bhatti, N. A. (1998), Operations Research anIntroduction, Department of Computer Science, Oxford University

Press, pp. 315356

Hillier, F. S., Lieberman, G. J. (2001), Introduction to OperationsResearch, McGraw-Hill higher education, 7

thedition, pp. 8348.

Karmakar,R(2012) Managing Resources in Retailing, StrategicManagement journal pp 21-33.

Agarwal P., Laney,C.(2011) Waiting Line analytics, InternationalJournal of business Management pp 112-134.

Rathore, A.(2013) Resource Recombination in ManagementReview pp 12-24.

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The Study of Waiting Line Management With Reference to Big Bazar