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
ABSTRACT
Pantaloons Retail Limited is India‟s 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 the number 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.
ACKNOWLEDGMENT
It is really a matter of pleasure for me to get an opportunity to thank all the persons who contributed directly or indirectly for the successful completion 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 providing me with this opportunity and for all its cooperation and contribution. I also express my gratitude to my Project mentor and guide Mrs. Shipra Sharma. 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 guidance and suggestions for my entire study. I would also like to thank the Big Bazaar team for extending their valuable time and cooperation. Neeta Kumari Roll no. : 03611809912
Table of Contents Executive summary……………………………………………………. 1. Introduction……………………………………………………........5 1.1.Company‟s 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
List of Tables and Figures
Executive Summary
The consumer buying pattern is influenced by several factors including the length of time the consumer spends waiting in line at the checkout counter. Retailers and businesses that use checkout systems in their businesses spend considerable resources searching for ways to improve their methods of customer checkout . Nevertheless , there is an overwhelming need for businesses to value customers‟ time and install more efficient checkout systems to improve the efficiency of queues and waiting lines . A 1990 survey by the Food Marketing Institute (Alcott, 1991) reported that 89% of shoppers interviewed indicated that a fast checkout process is one of the top priorities in their choice of a store that meets their expectations. The issue of improving customers‟ checkout process is
not a new
phenomenon. Managers in the retail industry have been tackling the problem of improving customers‟ checkout systems. This concern
contributed to one of the earliest inventions for keeping track of commercial transactions and checking out customers on a timely manner. The abacus helped early merchants make arithmetic calculation and checkout customers in the process. A variety of abacus designs followed and led to the present checkout machines used in the retail stores today. The other dimension in the history of the checkout process involved how managers decide on what system to implement to reduce
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. Lewin‟s (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 selfcheckout at retail stores. Customers are demanding better and faster service, 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 and
there 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
service waits can be controlled. They suggested that, to control the time customers‟ wait in line, researchers must determine the factors that caus e
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
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 of bustling 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 into newer 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
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.
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 the problems associated with franchise model, we decided to have our own retail outlets. They launched the own retail store, “Pantaloon‟s”. 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
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 value
retailing 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 India‟s fi rst 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
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.
Organisation Structure
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 dealing with 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
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 line to be checked out may influence the choice of a store in which he or she shops.
(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. This research 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 Line management in the current business environment
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 stores like Big Bazaar and see whether it is effective enough to serve the number of footfalls.
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 $511 billion in 2013. Simultaneously, modern retail is likely to increase its share in the
total retail market to 22 per cent by 2014. Organized retail in India raked in US$ 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, as compared 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.
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. Cash flow
Maturity
Growth
Decline
Introduction
Time
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.
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 µ 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 governing the 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, singlephase situation is the one-person barbershop.
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.
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 that provide 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 multipleserver system with single service facility, in other words multiple checkouts 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
busy server are constants regardless of the state of the system (busy or idle).
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
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:
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))] = customers/sec **(0.02041 sec *60 *60) Interpretation of confidence intervals
0.04167 = 150
The confidence intervals show that 73 to 150 customers arrive in 2server 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.
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
µ serving rate = 55 customers per server per hour cµ (2) (55) = 110 (service rate for 2 servers)
= =µ
/(cµ) =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.
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
µ serving rate = 55 customers per server per hour = γ=µ
/(cµ) =(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
Average number of customers in the queue (Lq) = 7.2758 Average time customer spends in the queue (Wq) = Lq/
= 0.1485hours
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 country‟s 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 company‟s missi on 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 I‟ll 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
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 designed economically 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
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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