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CHAPTER ONE

1.0 Introduction

As the world turns to a global village characterized by intense and ever increasing competition,

operation bank managers continue to experience wrenching changes, which they must keep up

with for survival. Bank customers have also become increasingly demanding. Today, they

require high quality, low price and immediate service delivery and tomorrow, they want

additional components of value from their chosen banker. Since service delivery in banks is

personal, customers are either served immediately or join a queue (waiting line) if the system is

busy.

Waiting line is what we encounter everywhere we go, while shopping, checking into hotels, at

hospitals and clinics e.t.c in additional non-queuing environment, customers left confused as

what line to stand in, what counter to go to when called by noisy crowded environment (Yechiali

etal, 1995).

They obvious but unfortunately at large either because nobody speaks for this most marginalized

sector of our society or because many pretend that such issues are not significant.

The Government found it difficult to handle the situation or they are not aware of it because is

considered as the institutions problem that many to those in different areas. Many societies know

the true situation they perceive.

All though this is not an excuse for failure to intervene, it is necessary to armed with relevant

data and information that will validate the problem that people are facing, convince policy

makers, service providers, community leaders, government and other stakeholders, and inform

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the planning of future interventions. Bank customers accumulate numerous experiences that we

cannot maintain to sufficiently understand; they are seen but are not heard and thus poorly

understood. This research seeks to create a mechanism for these peoples voices in Tanzania that

has in Dodoma, it seeks to understand the true situation in the region and thus provide initial

baseline information from which necessary benchmark to be provide.

1.1 Statement of the Problem

The problem of queuing in the Financial Institutions in Dodoma.

Consistent to now days of development, the process has become one of the big issues that are

barely observing. Problem is not reducing. This study questions the level of effectiveness of the

services and liability that hold on approaches considered inadequate in influencing the changes.

It therefore wishes to examine these approaches, the messages derive from them, and the motives

underlying resolve to early approaches despite of the changing of some systems of services in

those Banks.

1.2 Objective of the study

The obvious cost implications of customers waiting range from idle time spent when queue

builds up, which results in person-hour loss, to loss of goodwill, which may occur when

customers are dissatisfied with a system. However, in a bid to increase service rate, extra hands

are required, which implies cost to management.

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The responsibility is then on the management to strike a balance between the provision of

satisfactory and reasonable quick service and minimizing the cost of such service. Thus, the

management should evaluate performance of different queuing structures and strike a middle

ground between costs on one hand, which is the main thrust of this study.

The primary objective of this study in the line with the identified problems is to determine

whether the present capacity level in the banking industry, using National Microfinance bank

(NMB) as a case study. Strike a balance between the cost of providing service and the time of

waiting. This was carry out by measuring:

i. The number of customers waiting service

ii. The processing time and

iii. The probability that the facility will be idle

The study specifically aims to determine;

i. The amount of customer is likely to experience in a system;

ii. How the waiting time will be affected if there are alteration in the facility to the system,

and,

iii. Make policy recommendation base on the findings from the study.

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1.3 Justification of the study

The previous works on the subject matters of this study only identify the need for the application

of queuing models to customer waiting problem in different banks and associated costs but fails

to determining the maximum number of services that can be used in order to minimize total

expected costs and achieve optimal customer satisfaction.

The specific objectives of the research shall be:

i. Find out the problems that facing customers who have an account to those Banks

in Dodoma.

ii. Provide data of the people who are facing the problem in the region.

iii. Obtain the reasons for these people are in the situation.

iv. To assess, in a participatory way, the needs of the customers

v. Make policy recommendations on the findings from the study on effective actions

that can reduce the problem of which they can be solved to those people.

The research will attempt to answer the following questions:

1.4 Research questions

i. Which categories of customers are in a particular problem?

ii. What are their attitudes to the problem?

iii. How many are getting good services from the Banks?

iv. What are their unique experiences (opportunities, knowledge, and challenges)?

v. The time they spending in the line (queue)

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CHAPTER TWO

2.0 LITERATURE REVIEW

Queues are integral parts of any service system, which refers to the whole situation from arrival

of inputs or times to their departure.

According to Ashley (2002), the variants of queuing models that can be applied to waiting

problems include; a simple system, multi-channel system, constant service and limited

population model. A simple system (MMI) is a single line and single server system, which

consists of items forming a single queue, which is served by a single facility while a multi-

channel system is a system where two or more servers available to handle arriving customers

needs. Here a common line is formed and the customers at the head of the line proceed to the

first free server.

In Nigeria, a study conducted by Oladabo (1988) revealed a positive correlation between arrival

rates of customers and banks service rates. He concluded that the potential utilization of the

banks service facility was 3.18% efficient and 68.2% of the time. However, Ashley (2000)

asserted that even if service system can provide service at a faster rate then customers arrival

rate, waiting lines could still form if the arrival and service processes are random.

One week conducted by Elegelam (1978) revealed that 59.2% of the 390 persons making

withdrawals from their accounts spent between 30 to 60 minutes while 7% spent between 90 and

20 minutes. Baale (1996) while paraphrasing Alumatu and Ariyo (1983) observed that the mean

time spent was 53 minutes but customers prefer to spend a maximum of 20 minutes. Their study

revealed worse service delays in urban centers (average of 64.32 minutes) compared to (average

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of 22.2 minutes) in rural areas. To buttress these observations, Juwah (1986) found out that

customers spend between 55.27 to 64.56 minutes making withdrawal from their accounts.

In situations where facilities are limited and cannot satisfy the demand made upon them,

bottlenecks occur which manifest as queue but customers are not interested in waiting in the

queues (Kelly, 2001). When customers wait in the queue, where the danger is that, waiting time

will become excessive leading to the loss of some customers to competitors. (kotler, 1999). But

allowing them to serve themselves so easily is a key factor in both keeping and attracting

customers (Michael, 2001).

A queue for the purpose of this study is the aggregation of customers waiting a service function.

It is an everyday occurrences and results when the numbers of calling units exceed the number of

available service centers.

Classification of Queuing System

A Simple System (MMI) is a single line and server system that consist of items forming a single

queue, which served by a single facility while multi-channel system is a system where two or

more server or channel available to handle arriving customers needs. Here, a common line

formed and the customer at the head of the first line proceeds to the first server.

There is also a constant service Time Model where the customers service time is constant instead

of exponential distributed times and lastly, the limited population model where there is a

dependent relationship between the length of the queue and the arrive rate. Among all these

models, those can be applied to solving customer waiting problems in the banks are the simple

and multi-channel models.

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2.1 Characteristics of a Waiting line system

The three main characteristics that determine the appropriateness of a queuing model is; the

arrival to the system, queue discipline and the service facility (Safe Associate, 2002).

Arrival characteristics of waiting line are related to size, pattern to arrival and service time

distribution. The size of the source population may be unlimited when the number of arrival on

hand at any given moment is just small portion of the potential arrival or limited when the

population size relatively known.

The pattern of arrival at the system considered random when arrivals are independent of one

another and their occurrence cannot be predicted exactly. In addition, the number of arrivals per

unit of time in queuing problems can be estimated by a Poisson probability distribution. The

service time distribution may be constant when it takes the same amount of time to take of each

customer or random when the reverse holds. If arrival rare is Poisson distributed, a negative

potential probability distribution is assumed for random services times.

2.3 Queue Discipline

This refers to the priority rule by which customers are served, that is, the order in which items

received service. According to Jay and Barry (1993), two main categories include;

1)Preemptive Priority; This is common in emergencies, which allows customers that

arrive at any time to replace customer that are being served e.g patient treatment in

hospitals.

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2) Non-Preemptive Priority; here, items in the queue are arranged so that the item which

the highest priority in the system is served first and there is no displacement of items in

service. The method here include:

(i) FIFO (First-in-First-out); allows the first item to enter the system (items at the

head of the queue) to be served first. It is the most frequently applied discipline

because it is believed to be fairer than the other types of rule.

(ii) LIFO (Last-in-First-out); here the last item on the queue or that which enters the

system last is served first.

2.3.1 Queue structure

This is the nature of the queuing in terms of input, queue and service mechanism. This is

illustrating below.

Figure 1

HYPOTHETICAL STRUCTURE OF A QUEUE

SOURCE: Jay & Barry (1993)

2.3.2 Features of a Simple Queue

Safe (2002) limited the characteristics of a single server to include;

8

INPUT OR

SOURCE

QUEUE SYSTEM

QUEUESERVICE

MECHANISM

SERVED UNIT OR

EXIT


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a) A single service channel and is variables arrival following a Poisson distribution while

service time is exponentially distributed.

b) FIFO queue discipline is adopted because of infinite population of potential customers

that has no simultaneous arrival and there is neither balking nor reneging.

2.3.3 Characteristics of Multi-server System

According to Safe Associates (2002), a multi server system has all the features of simple queue

and in addition assumes no limit to the permissible length of the queue. Also, all servers are

assumes to perform at the same time.

Efforts in this study are directed towards application of reducing the queuing of customers are

waiting service and total operating cost.

Chapter Three

3.0 Methodology of the Study

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Tanzanian Banking Industry most bedeviled with customer waiting problems is studied here for

a period of four weeks in the National Microfinance Bank in Dodoma, through observation,

interview and questionnaires administration. The variables measured include arrival rate (I) and

service rate (m). They variables were analyzed for simultaneous efficiency in customer

satisfaction and cost minimization through the use of a single channel and multi-channel queuing

models, which are compared for a number of queue performances such as the average time each

customer spends in the queue and in the system, average number of customers in the queue in the

system and the probability of system being idle.

In the realization of these objectives, primary data in respect of customer arrival rate and cashier

with also ATMs service rate were used and were obtained through observation while customer

attitude survey was carried out through a total of twenty questionnaire in the period of three

weeks were administered. These activities carried out for four weeks (May 2010).

The variables analyzed using the queuing Models presented in the table below;

Note that I= arrival rate and M= service rate.

Since there are (5 x 52 weeks) i.e 260 working days in a year , with the exclusion of public

holidays, a 27 working days of May 2010 used in this study while the working hours per day is 8

hours.

3.1 Performance Measures

Single Queue and Multi Queues

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This is a classical problem in queuing theory, Should there be multiple queues, one for each

ATM in the bank, or should we have one single queue for all the ATMs? I have this version of

the problem from an example of the two situations are shown in figs. 2.0 and 2.1.

Figure 2.0. Multiple Queues (4 Teller Machines)

Q1

Qn

Figure 2.1. Single Queue (2 Teller Machines)

Queue

Source: Authors Perception (2010)

First look at fig. 2.0. Customers arrive to exponential distribution (which produces inter-arrival

times) and they immediately go to any teller that is free (not busy). If all tellers are busy with the

line then the customer chooses the shortest queue to wait for service. Once in a queue the

customer waits there, and does not jump to other queues, in a first-come-first-served basis, until

he/she has completed their business. Customers require die rent types of transactions in table 2.0.

This table gives the probabilities and approximate mean service times for each of these types of

transactions.

11

Arrive

T

1

T

n

Leave

Arrive

T

1

T

n

Leave


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Formulas

i. Probability that there are no customer in the system i.e the ATMs are idle (PO)

1-I

M.

1

Sm-1(I/m0) m mu

mu- I

1

Sm-1(I/m) + (I/m0) m mu

m! mu-l

1

Sm-1(I/m)n + (I/m)m m n=0 n!

n= 0 n! m! mu-l

ii. Average of number of customer in the system (LS)

l

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I-m.

I/m(I/m)2 (PO)+ l

(m-I)! (mm-l2) m

I/m (I/m) m (PO) + l

mm-I m

I/m(I/m)m (PO)+ 1

(m-I) ! mm-l m

iii. Average time a customer spends in the ATM

I/l m.

LS/I

LS/I

iv. Average of number of customers in the queue (LQ)

I2

(m I)

LS- I/m

v. Average time a customer spends in the queue (WQ)

I

m (m-I)

LO/I

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Lq/I

P, Ls, Ws, LQ and WQ are conventional symbols for measuring the performance they

describe.

3.2 Study area

This research based in Dodoma institution, which are including of the following;

National Microfinance Bank (NMB) which are the Main branch and Mazengo branch.

The major language spoken is Swahili, because most of the locals understand and speak Swahili

fluently.

3.2.1 Study Population

This research was focus on the customers population who have opening account or are served

by this bank in Dodoma. It was attempt to draw out information from all of them who are

accessible to the research, thus no form of sampling were be required. It is expected that the

research provided detailed information on all the customers defined in this category. It is

however predict that the follow-up to this research were involved attempting to reach the ATMs,

branch managers and customer service of the particular institutions. Since this research was

trying to provide detailed information on all the customers having this situation in Dodoma.

3.2.2 Data Types and Sources

Both Primary and Secondary sources were employed in data collection. Primary source helped

so much the researcher to obtain first hand data, this included questionnaires and focus group

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discussions using checklists. Secondary source of data such as journals, reports, books and

written documents were use.

3.2.3 Sampling frame

The sampling frame included those customers was twenty (20) by whom the numbers of

respondents were eighteen (18) which is the average of 80%.

3.2.4 Sampling unit

The samples were included individual workers, students and the business people.

3.2.5 Sample size

This is a number of substance/elements that selected from the population (sampling figure) to

constitute a sample. The sample size strained from a particular number of customers in two

branches, which are Main branch and Mazengo branch.

Table 1: The affected group

Categories Mazengo branch Main branch Total

Workers 3 4 7

Students 5 3 8

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Business people and entrepreneurs 2 1 3

Total 10 8 18

Source; Authors calculation (2010)

due to time and financial resource constraints, and given the size of the population sampling

based on selected number of customers themselves in the two branches, who were

representatives of the total population of the two branches.

3.2.6 Sampling procedure

Sampling is an activity in which a portion, rather that the whole population, was selected for

surveying. The respondents selected in the sample used to represent the whole population. The

research conducted by using two sampling techniques that were random sampling and purposive

sampling that means that both probability and non-probability sampling were used.

3.2.7 Probability Sampling

Stratified random sampling method, used to select the customer by considering their positions

that stratified to students, workers and those business people as the study respondents from the

two branches.

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3.2.8 Purposive sampling

Five key respondents were selected due to the position they hold since their day to day activities

are linked with the need of money from the bank. This constituted the total number of

respondents selected through the purposive sampling method which a researcher has chosen.

3.2.9 Data Collection Methods

The study used different methods of collecting both primary and secondary data. The methods

used presented here below.

3.3.1 Primary Data

3.3.2Interview method

This method used whereby that all the designated groups of respondents were interviewed using

open ended question to ask for the required data and information.

3.3.3 Questionnaire method

Two sets of questionnaires were prepared whereas one were given to those customers found at

main branch and Mazengo branch while the other two set was given to the group but was

processed through interviews. Such questionnaires provided information to cover the objectives

of the study.

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3.3.4 Secondary Data

Secondary data collected from different books, reports, national and international publications.

3.3.5 Data Processing, Analysis and Presentation

After collecting, data they were processed using computer, the processing involved, editing,

coding, classification and tabulation of the data collected. Thereafter the data analyzed.

3.3.6 Data processing

Data obtained from the field was processed and analyzed for reliability reasons; the analysis

followed personally, the questions formulated.

In some instances, data presentation was in a form of tables, graphs and statistically summarized

data processed by using both manual operation and Computer programmer.

3.3.7 Data analysis

A univariaty analysis used. Univariaty was used to calculate frequencies, to determine

relationship between variables.

4.0 Data presentation

Data presented done in the form of tables, graphs and figures (statistical summaries) as

mentioned above.

5.0 Limitations of the Study

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The study carried out as a means of increasing stakeholders understanding of the issues related to

the customers who wants to draw their cash from the bank. The focus group discussion did not

take place in some cases as scheduled. Some of the key respondents such as students were

providing good information to a researcher as due to the touch of the problem. Some of the

respondents provided wrong answers because they believed their details might take to the other

way around which would affect their relationship with their bank. The shortcoming of the data

addressed during data entry and analysis in order to produce the highest quality data set possible.

This explains in part, the delays experienced during data processing.

CHAPTER FOUR

6.0 Results and Discussion

6.1 Overview of the discussion

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The bank through its policies and directives made some mechanism, which is still used up to date

on instruct those new users or customer who got their ATM Card lately. Many branches which

used to be depended on this service has been taken as not to solve the problem completely but to

some extent it has been at least a good way of saving those new customer. They are doing this by

providing some brochures that shows the instruction to the user of ATM card on the machines.

The banks responsibility is to take care of the customer who needs service from them. As an

example of CRDB bank limited in Dar es Salaam at Posta (city centre) at the new building of

Benjamin Mkapa also known as Mafuta house, they installed many ATMs machines which helps

to reduce the queue of customer. Customers of a particular bank are getting fast and reliable

service and for a short time, which means the system is fast and is a friendly user.

In Tanzania, many Banks are providing ATM service such as Azania Bancop, Tanzania Postal

Bank, Exim Bank limited, Barclays Bank limited, Dar es Salaam Community bank, BOA bank,

National Bank of Commerce (NBC), Diamond Trust Bank (DTB), Twiga Bank, just to mention a

few.

This chapter presents the major findings of the study whereby the problem of the customer

observed.

The findings reflect the general objective of the study as well as specific objectives. Thought

prepared in the course of deliberate variables in order to bring comprehensive and valuable

report. The use of clear presentation in data interpretation was the main tool used for reporting

the findings of the study.

The average time a customer spends in the in the queue are 25 minutes and 1.05 minutes

respectively for in the ATMs machine and this is for two machines with a single line. However,

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with those with single line and single machine they use 35 and 40 minutes, this is further worse

than that of single line with two ATMs that records 15 and 20 minutes in the queue and in the

ATMs. In terms of the number of the customers in the 2 ATMs system has almost 20 and 25

customers and a 1 ATMs system with a single line have 15 and 20 customers in the queue. The

probabilities of idleness of the machine are 2%, and 5% for a 2 and 4 ATM machines. The

average time of a customer spends in the queue and in the system for a 2 ATMs is 12.5 minutes

and for a 4 ATMs system is 17.5, and the average of a system to be idle is 0.02 minutes and 0.05

minutes.

6.2 Presentation of Results

Performance Measures

Single line with a 2 Tellers

Single line with 1 Teller

i. Probability of the system being idle

-0.05

2%

5%

ii. Average number of customers in the system

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-2.14

14.5

3

3.2

iii. Average number of customer in the queue

0.25

0.15

iv. Average time a customer spends in the queue

14 min

20 min

Source: Authors calculation (2010)

The appropriate of a single line with a 2 Tellers for solving customer-waiting problems become

an important as it shows the minimum time for a customer to reach the machine in all

performance. However multi-channel as single line per machine were compared and it was seen

that;

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1. Using a single line for 2 Tellers machine is better than a single Teller system for a single

line, as due to the little time spends in a single line for 2 Tellers.

2. The bank adopted a both lines system for drawing money from the Tellers and the rate is

quiet reduced to some extent. While a customer has to wait for a service more than 40

minutes now the customer has to wait for 10 minutes to get service. The introduction of

new ATMs has reducing the loss of customer by 60% (obtained from the staff). It was

also noticed that friends of those who came earlier in the line helped by them to get quick

service which lead to the complain because of their waiting time

6.3 Reasons for queue in the ATMs machines

The study has shown that several things among the customers caused the increase of customer in

the ATMs. Most of them were able to tell their reasons.

Salary is a major thing of which brings many people to ATMs for the drawing of their cash,

70.55% of the customer who was in the line to draw the money from their salaries as per

interview between the researcher and the customers. Most of these groups were the workers who

employed by Government, Public and Private Sectors.

Paying of bills and buying the electricity unit known as LUKU through ATMs is among of the

issues that cause of the queue in the particular places, 5% of customers are there to pay the bills.

Such bills are for electricity to Tanesco.

The study further discovered that also the other group of which affected by the queue is the

student who goes to the ATMs for the money from the Loan Board for the accommodations,

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stationeries and meal when they are in the study. 15% shows that they are in the problem

according to the research conducted.

Another group is the business people and entrepreneurs who come to draw the money for their

daily spend on their business and investment for the payment to their wages. This group took 5%

of the sample taken, and they are a large group that is facing this problem in a specific time.

6.4 Identifying the affected groups

The target group of customer is workers, students and the business people or entrepreneurs who

spent most of the time in the line to get service from the ATMs and gets their money as indicated

in the table 1. These group customers in the daytime are coming to the bank seeking service

particularly in the ATMs by then them find a long line of people for that service. As they stay on

the line almost for an hour to reach the system and draw the money for the end.

A total number of 18 customers interviewed to determine their occupations, which were workers,

students and business people. The figure 1 and table 1 below shows that, a larger percent of

customers fell on the workers group which is 70.55%

6.5 Age and Sex of the customers

When these customers asked to state their ages by chance enough all the customers who asked

responded. Many of them were men and relatively a little number of them women. Some

appeared hesitant especially those found complaining that many related people like in office

researcher interviewed them many times but they did not get the promised solutions.24


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Table 2: Age and Sex Distribution of customers

Age group Male % Female % total %

18-25 2 66.67 1 33.33 3 100

26-33 5 83.33 1 16.67 6 100

32-39 4 80 2 20 5 100

40-49 1 25 3 75 4 100

Total 12 66.67 7 33.37 18 100


Many of the customers interviewed were men, as they were (12) and very few women (7). A

large percentage of customers fell in the age 26-33 age groups, followed by 32-39 age group

those over 40 years relatively few in number that they were few during the interviewed session.

As (1) were males and (3) female.

Table 3: Reasons for being in queue

Reasons Rate Percentage

Paying the bills 4 22.22

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Salaries 7 38.89

Loan from the loan board 5 27.77

Cash for business, investment 2 11.11

Total 18 100


The response of the customer about their reasons for being in the line as shown in the above table

3. There was nearly half of them (38.89%) which is 70.55% of the sample were there to draw the

cash from the salary, while the second group were students who receives their cash from the

loan board which was (27.77%) also in a range of 15% in a sample being interviewed, while the

other group of paying the bills where 22.22% that was range on the 5% of the sample

interviewed by a researcher, lastly which lied on (11.11%) that was considered as a the lowest

group.

Table 4: The times of customers are on the queue by occupations

Time Students % Workers % Business people % Total %

& bills

Weekdays 2 66.67 0 0 1 33.33 3 100

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Weekends 4 57.14 2 28.57 1 14.28 7 100

Month end 1 12.5 7 87.5 0 0 8 100

Total 7 38.89 9 50 2 11.11 18 100

Source: Authors calculation (2010)

As the table 4, depict that most of the workers were in the queue during the end of the month of

which shows there were 53% of workers in that weekends there were 5% of the sample

interviewed. Those who came to draw the money on weekends were mostly students in which

89.92% interviewed.

Increase in number of customers

The study showed that the number of customer kept on growing year after year. This can be

official the number of growth in Dodoma hence the increase of queue in the ATMs is getting

higher than before. With the rate of 40 in 2006, followed by 60 in 2008 and the late rate were 80

in 2009. The trend showed that the increase of customers would keep rising, as the bank will not

take some measures on it.

Graph 1: Increase in number of customers in NMB bank

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Source: NMB bank (2010)

CHAPTER FIVE

CONCLUSION AND RECOMMENDARIONS

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7.0 Conclusion

This study uncovered the applicability and extent of usage of queuing in achieving customer

satisfaction at the lowest cost of time. Customers are unhappy due to delay in getting the service

from the ATMs while the bank is in reluctant in increasing the number of ATMs units because

the theoretical underpinnings of queuing model was not well understood.

A single line with single 1 ATMs is not effective when arrival rate exceeds service rate, while

the current single line to a 2 ATMs is used as to reduce the long queue. In addition, it is also

eliminate the long time waiting in the line.

7.1 Recommendations

Based on the summary and conclusions of this study, the following recommendations are

suggested for efficiency improvement and quality of service to customers in National

Microfinance Bank (NMB) and in the service industry generally.

1) The management should educate their operation managers and other staff on the

application of queuing models to operational problems to take a proper and quick action

when the ATMs are out of service.

2) The queue characteristics should be viewed from the standpoint of customer as whether

the waiting time is reasonable and acceptable by making queuing discipline fair and

varying the number of service channel according to queue circumstances.

3) Reengineering the banking operations through IT solutions e.g. voicemail and online

withdrawal system to admiring comment queuing model.

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4) Making customers comfortable and unaware of waiting time by providing electronic

notice board, cooling and toilet facilities at the time when they are in the line.

5) To introduce the use of credit card for payment in the shopping, supermarkets, and also to

pay in the bus fair, as sometimes other customer may be forced to postponed his/her

journey due to the problem of queue which is found in the bank.

6) The bank must looking toward those areas with high movement or population of its

customers so as to reduce the coming centre point to seek for their cash, there are some

places like at shells, and also in some institutions like St. Johns University main campus.

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Bale, A.F (2002) Managerial Applications of Queuing Models in the Service Industry A case

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Administration Department, University of Ilorin, Ilorin.

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