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Transcript of 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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