Performance Evaluation of Kerala State Road Transport Corporation

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Performance Evaluation of Kerala State Road Transport Corporation

Leejiya Jose

PG Scholar: Dept. of Civil Engineering Jyothi Engineering College, Thrissur Kerala, India

Jisha Akkara

Assistant Professor: Dept. of Civil Engineering Jyothi Engineering College, Thrissur

Kerala, India

AbstractKerala State Road Transport Corporation (KSRTC) is the oldest state run public bus transport services in India. It plays a crucial role in strengthening the public transport system in Kerala. But today the corporation is facing a big crisis. The main objectives of this study are to evaluate the operational and financial performance of KSRTC and to compare the performance of various depots in KSRTC. This study uses both primary and secondary data. It includes interviews with the employees and visiting major depots and KSRTC offices to collect the required data. Different parameters for data analysis are operational parameters and financial parameters which includes, fleet, collection and passengers etc. The analysis of 28 main bus depots of Kerala State Road Transport Corporations are done by using Data Envelopment Analysis (DEA) in the software DEAP 2.1. The analysis shows that Trivandrum City and Trivandrum Central depots have maximum degree of efficiency in every year with an average efficiency score of 1. Ernakulam is the most inefficient depot with average efficiency score of 0.741. It is found out from the Technical Efficiency analysis that on an average 11.5 percent of the technical potential of the depot is not in use. This study has discussed how DEA can be applied to evaluate the degree of efficiency of the depots. Thus, these results give an indication on the degree of efficiency of depots in the process of transforming inputs into output. Target values are also discussed in this project. Target values are the values of input and output which would result in an inefficient organization to become efficient.

KeywordsKSRTC; Technical efficiency; Data Envelopment Analysis; Decision Making Units;

  1. INTRODUCTION

    Transportation is the most important part of human life. It allows people to travel from one place to another. To make people feel convenient and comfortable with their position, different modes of the transportation system are found and it is evolved from the earliest stage to the present stage of the transportation system. At present, with the upgraded technology different modes of transportation systems are developed. The primary mode of transport for most of the Indian citizens are public transport. The availability of a safe and comfortable passenger transport facility is an important index of the economic development of any Country. Public transport provides vital connectivity to different areas of society.

    Kerala State Road Transport Corporation (KSRTC) was developed in 1961. KSRTC is a state-owned public transport corporation in the Indian State of Kerala. This organization divided into three different zones for its proper working namely North Zone, Central Zone, and South Zone, with headquarters at Thiruvananthapuram. The corporation has

    6241 buses which include Scania, Volvo, Ashok Leyland, Tata Motors, Eicher Motors, and minibuses.

    Kerala State Road Transport Corporation(KSRTC) It is one of the oldest state-run public bus transport services in India. It has an important role in the public transport system strengthening in Kerala. But today the corporation is facing a huge crisis in its operation. Management problems, increase in fuel price, etc. can be called as reasons for this crisis. This project aims to study the performance of Kerala Road Transport Corporation by collecting operational and financial parameters from all the depots in Kerala. So the main objectives of this project are

    1. To evaluate the operational and financial performance of KSRTC

    2. To compare the performance of various depots in KSRTC In India, it is not a good experience to travel through public transport, and for good reason. Most of the vehicles run by its state road transport undertakings (SRTUs) is old, and there is a shortage of funds to replace them. The government report shows that most of the SRTUs are not profitable. So it is important to understand various problems associated with working of KSRTC which makes the corporation inefficient, to improve the efficiency of depots.

  2. LITERATURE REVIEW

    This section provides an overview of previous research on Road Transport Corporation with a specific focus on efficiency improvement, management strategies and related issues of the corporation. It shows the details and methods used for the case study that contains the main focus of the research explained in this thesis.

    Bangalore is the largest city in the state of Karnataka and is considered to be the Information Technology capital of India. In order to fulfill the different needs of the growing city population BMTC had introduced different services to serve the different segments of public transportation users. The performance evaluation of Bangalore Metropolitan Transport Corporation specifically aiming at premium bus services of BMTC operating in Bangalore city is conducted by Devaraj Hanumappa et al. (2016). The performance measurement of premium bus services is done using two different approaches. Ratios were computed considering different operational and financial indicators and these ratios are benchmarked by considering the best performing units as the target to compare the bus depots performance. Further, they studied the performance of bus depots using data envelopment analysis (DEA). The main conclusion in our study is that even though the cost of operation in terms of fuel, type maintenance, etc., has increased in these days the efficiency of these depots and

    Premium services are showing very good efficiency scores. This can be attributed to the commuters and it can be seen that the number of people is using public transport over their private mode which is a prime reason for their efficiency.[4] Similarly, an attempt has been made to analyze the Karnataka State Road Transport Corporation in Karnataka by Kavitha

    B.D et al. (2017). The study is based on secondary data collected from secondary sources and the Study gives a brief Picture about the Karnataka State Road Transport Corporation in Karnataka. From this study they found that Karnataka State Road Transport Corporation doing Inter State Services, Long Distance and Night Services, Express & Non-Stop Services City /Suburban Services, Advance Booking & Reservation facilities, Luggage & parcel Transport, Special Services of Jathras/Fairs, Quality Services and It stands 5th amongst State Transport Unions in the Nation by size. Karnataka has greater potential to increase Public Transport to make the Karnataka economy and Service sector more prosperous and sustainable.[7]

    A similar study for the evaluation of the performance of Road transport corporations is done by Vishnu C R et al. (2014). The performances of Road Transport Corporations are compared and a generalized methodology is formulated using the technique of Data Envelopment Analysis. Benchmarking is used as an improvement technique essential in the case where similar organizations are functioning for satisfying the customers in a profitable way but delivering it with different efficiency. For the purpose of analysis three Road Transport Corporation in South India were selected. In which Kerala State Road Transport Corporation is regularly hitting the headlines with reports of huge financial loss every year. The nearby Tamil Nadu State Transport Corpration and Karnataka State Road Transport Corporation are performing extremely opposite by making huge profits. From this study they found that The burden of Interest over various loans is huge, the staff per schedule ratio of the less efficient depot is more when compared to efficient depots, inefficient in fuel consumption, break downs of buses are more and average revenue per kilometer is less for inefficient depots. This case study pinpoints the areas where Kerala SRTC needs to concentrate to improve its standard. Various suggestions to improve the standards of the less efficient depot is also framed and listed on the basis of the result obtained.[11]

    An evaluation of urban public transport of twenty-one Brazilian largest cities through Data Envelopment Analysis Method (DEA) is conducted by Marcius Carvalho et al(2015) from 2005 to 2010 for three scenarios: infrastructure efficiency, service effectiveness, and efficiency versus effectiveness. In this study, they used the DEA models. The purpose is to benchmark Brazilian cities using secondary data on three performance measures: infrastructure efficiency, service effectiveness vs. efficiency score. Efficiency is an indicator of operational excellence, thus of service provider interest. Service effectiveness is a measure of the user satisfaction with the service delivered, thus of passenger interest. The last one allows identifying the city strategy balance regarding both criteria. The cities (DMUs) were selected from the ICC document taking into account the availability of data. As the topic under analysis is public road transport operating in the urban environment, the DMUs are

    in similar environments and have similar objectives. The selected variables for the study were: Municipality inhabitants, Number of buses, Average daily passengers, Average gratuity. The results have shown that the contribution of the DEA is quite significant to the urban transport area that has three main stakeholders: the service provider, the public sector and the passenger. They suggested that a policy aimed at urban public transport planning should establish regulations to protect the rights of users and ensure that the quality of public transport is maintained.[3]

    Kerala SRTC is one of the premiers of public transport systems in India. The survival of this concern is more relevant in these present conditions. The public is facing a steep hike in transportation costs. The performance of public transport is becoming worse. An overview of the performance of Kerala State Road Transport Corporation is given by Vini MS et al. (2017). The study is purely theoretical work which depends on secondary data and it is confined to five financial years from 2012 to 2016. The key variables taken for the study are schedules operated, number of buses, average daily collection, average earnings per kilometer, and average earnings per bus. This study found that The schedules of the KSRTC show a positive slope when compared to the number of buses for the study period. Even the number of buses is increasing; the rate of increase is less when compared to a number of schedules operated. This proves that the public prefers this service more than that of other private services. But the average daily collection, average earnings per kilometer and average earnings per bus show a declining trend. The slight variation in the earnings can be the result of increased operating costs. The findings of the study are also pointing towards that the functioning of this concern is not so satisfactory. The number of schedules and buses is not correlated with its earnings capacity. And they concluded that the operational efficiency should be enhanced so as to robust the overall performance of the Kerala SRTC.[10]

    The State of Kerala in 2012 through a notification stopped issuing new permits to inter-district buses in Kerala while exempting state-run Kerala State Transport Corporation. This was the beginning of the government's action against private operators as it eventually started taking up routes were private buses used to operate. Madhu Sivaraman(2016) studied the effect of private buses in the operation of public transport and he found that the public-private modal share has tilted in favor of private transport, due to the inefficiency and absence of public transport systems for supporting people's transportation needs and now the cities in India do not have an efficient public transportation system, as more people use their cars or depend on private taxis. Also, they concluded that Kerala has a favorable market for the growth of public transport, which is being distorted due to Government action. Public transportation can only be built with the support of private players, who should take the lead to offer efficient and effective transport services. Therefore, the rule related to banning private operators from inter- district operations needs to be lifted. This will give a strong positive signal to further private participation in Kerala and other states.

    Bus operation service depends on varied factors like population, culture, atmosphere and social science. Types and options of bus operation services area unit designed in keeping with native wants. With special purpose designed technology applicable to the native climate or traveler wants, like air con in Asia, or cycle carrier mounts in UK buses, varied sorts of bus operation service target and become appealing to specific user teams. The important role played by public transport to meet the demand of business and social life is studied by Munirah Md. Rohani et al. (2013). The paper reviewed the kind of bus services, quality of service in the bus operation that influences the passenger decision and also the role of bus provider and bus driver. And they say that an improved understanding of the bus operation is important for well-managed bus services. Maintaining a high normal of quality in commission and performance is of predominant importance to encourage folks to create transport their most well-liked selection. They concluded that the performance of public bus service will be affected largely by the quality of service. In areas in which public transport especially buses are highly accessible, operation performance improvements may be required by improving factors that influence public bus ridership such as bus service reliability, safety, comfort, and cleanliness. Improving such a factor will help to encourage people to shift from private to public transport.[9] A different study is conducted by Riyaz et al (2015). The purpose of their paper is to obtain a better understanding of the extent to which service quality is delivered by KSRTC to its passengers. The core importance of this study is to provide a base for understanding the problems in the transportation system and to know issues, or problems facing by the transportation community by collecting both primary and secondary data. Primary data was obtained from the distribution of questionnaires and secondary data through published sources. From the study, it is found that the majority of people don't feel secure or safe to travel in KSRTC local bus. In this context, the KSRTC department should try to make its service as best. There is a strong culture in KSRTC organization that "passenger's satisfaction is most important" but a lot of things are there to do when it comes to the matter of the passenger's satisfaction. First of all, the bus crews are supposed to behave in a co-operative, pleasant and in a helpful manner with passengers but in the practical only financial goal of the corporation assessed, but behavioral aspects of the crew are not assessed. In order to achieve success in maintaining standard service, the KSRTC should try to add more potential as well as qualified workers.[3]

    Data Envelopment Analysis (DEA) is a decision making tool based on linear programming for measuring the relative effi- ciency of a set of comparable units. A survey of the basic DEA models and a comparison of DEA models is given by Milan M. Martic et al. (2009). DEA is a technique of mathematical programming that enables the determination of a unit's efficiency based on its inputs and outputs and compares it to other units involved in the analysis. This paper shows possibilities for using the DEA for the evaluation of the performance of bank branches, schools, university departments, farming estates, hospitals, and social institutions, military services, entire economic systems, and

    other things. It supplies important information for managing the operations of efficient and inefficient units. Since DEA is used to evaluate performances by directly considering input and output data, the results will depend on the input/output choice for the analysis and the number and homogeneity of the DMUs to be evaluated. The effect of model orientation (input or output) on the efficiency frontier and the effect of the convexity requirements on returns to scale are examined. The paper also explains how DEA models can be used to assess efficiency.

  3. METHODOLOGY

    Systematic and theoretical analysis of the methods adopted to a field of study is known as the methodology of the study. It mainly consists of the theoretical analysis of the methods and principles associated with a branch of knowledge. The methods describe actions applied to execute a research problem and for the application of specific procedures used to identify, select, process, and analyze the information applied to understand the problem, thereby, allowing the reader to evaluate a study's overall reliability. To achieve the goal of this study, all the data are collected from Kerala State Road Transport Corporation office.

    1. Selection of variables for data collection

      Anything used in a study that has a quantity or quality that varies can be defined as a variable. Most of the research projects are based on some variables. Here in this project, the variables are the characteristics or attributes of the road transport corporation selected for this project. For the data collection, the variables selected in this project are mainly classified into two. Operational variables and financial variables. Examples for operational variables are Number of schedules in operating from the depot, Number of vehicles, vehicle utilization, etc., And examples for financial variables are Total earnings per passenger kilometer(EPKM), Total cost per passenger kilometer, etc.,

    2. Selection of depots

    Kerala State Road Transport Corporation is a public transport corporation owned by the government of Kerala. South zone, Central zone, and North zone are the three zones in the KSRTC with the main office working at Thiruvananthapuram. As July 2018 KSRTC has three independent zones with each zone headed by zonal officers provided with self-administrative powers. This project deals with the collection and analysis of data for all the main depots under different zones of KSRTC.

    Data collection is done by approaching KSRTC transport Bhavan. For the purpose of data collection, details about all the depots in KSRTC are studied. The data collected for this study are the month wise operational and financial details for a period of three financial years from 2015 April to 2018 March.

  4. DATA ANALYSIS

    After the information has been collected, it has to be bestowed during an approach that communicates the knowledge and permits conclusions to be drawn. Clear, correct and applicable ways that of presenting information were chosen out of the many ways that of information presentation. The many ways that of presenting information

    embrace tables, pie charts, bar graphs, and line graphs, only tables, pie charts, and bar graphs were used in this research. This section deals with the analysis of 28 main depots in KSRTC by considering the data for three financial years from 2015 to 2018.

    The concept of productivity of any firm relates to the efficient and effective use of resources in terms of the quantified output obtained from the system and input resources used for the proper running of the system. It can be determined by dividing the output by the inputs. When we compare the productivity of two firms, the more productive firm will produce more output with the same inputs or which will produce the same output with fewer inputs. In this study, an attempt has been made to develop a model to assess the efficiency of KSRTC depots in Kerala using Data Envelopment Analysis (DEA).

    Input variable considered in the Data Envelopment analysis is the fleet size. Which is one of the most important variable in producing the output. Fleet size comprises the number of buses in the depots. It is a representative of capital input. In this study two output variables are considered namely passengers and earnings. The extent of relationship between input and output variables has been analyzed using correlation analysis. It is found that the output variables have good correlation with the input variable.

    1. Application of Model

      The Data Envelopment Analysis is applied for the data from three financial years 2015-2016, 2016-2017 and 2017- 2018.Input and output variables used in the analysis is given in the table I.

      Depots

      Passengers

      Collection

      Fleet

      Alappuzha

      1507573

      25855740

      2406

      Aluwa

      1310901

      17853058

      1871

      Attingal

      1759507

      26975655

      2474

      Changanassery

      917556

      16450766

      1649

      Chengannoor

      1186966

      21070799

      2077

      Cherthala

      1634935

      24498281

      2329

      Eranakulam

      1384921

      35411003

      3213

      Kannur

      1756753

      32595922

      3049

      Kasergode

      1643524

      32527862

      2405

      Kayamkulam

      1297038

      19964684

      1805

      Kollam

      1940712

      31159770

      2803

      Kottarakkara

      2183842

      34716459

      2991

      Kottayam

      1318154

      35632387

      2808

      Kozhikkode

      438017

      21494542

      1393

      Moovattupuzha

      1076895

      19414024

      1831

      Nedumangadu

      1142460

      16695121

      1633

      Neyyattinkara

      1628857

      22674167

      2389

      Pala

      1126338

      27014750

      2319

      Palakkad

      1079337

      35878723

      2300

      Pappanamcode

      1648424

      16456555

      2105

      Pathanamthitta

      1045092

      22786875

      1982

      Perumbavoor

      973576

      13542506

      1260

      Sulthan bathery

      1343566

      30324703

      2519

      Thiruvalla

      1059826

      19759303

      1807

      Thrissur

      791999

      26547502

      1905

      Tvm. City

      2316812

      24882880

      2637

      Tvm.central

      1007923

      63747737

      3432

      Vizhinjam

      1296339

      17531135

      1633

      Depots

      Passengers

      Collection

      Fleet

      Alappuzha

      1507573

      25855740

      2406

      Aluwa

      1310901

      17853058

      1871

      Attingal

      1759507

      26975655

      2474

      Changanassery

      917556

      16450766

      1649

      Chengannoor

      1186966

      21070799

      2077

      Cherthala

      1634935

      24498281

      2329

      Eranakulam

      1384921

      35411003

      3213

      Kannur

      1756753

      32595922

      3049

      Kasergode

      1643524

      32527862

      2405

      Kayamkulam

      1297038

      19964684

      1805

      Kollam

      1940712

      31159770

      2803

      Kottarakkara

      2183842

      34716459

      2991

      Kottayam

      1318154

      35632387

      2808

      Kozhikkode

      438017

      21494542

      1393

      Moovattupuzha

      1076895

      19414024

      1831

      Nedumangadu

      1142460

      16695121

      1633

      Neyyattinkara

      1628857

      22674167

      2389

      Pala

      1126338

      27014750

      2319

      Palakkad

      1079337

      35878723

      2300

      Pappanamcode

      1648424

      16456555

      2105

      Pathanamthitta

      1045092

      22786875

      1982

      Perumbavoor

      973576

      13542506

      1260

      Sulthan bathery

      1343566

      30324703

      2519

      Thiruvalla

      1059826

      19759303

      1807

      Thrissur

      791999

      26547502

      1905

      Tvm. City

      2316812

      24882880

      2637

      Tvm.central

      1007923

      63747737

      3432

      Vizhinjam

      1296339

      17531135

      1633

      TABLE I. INPUT AND OUTPUT VARIABLES

      The model has been applied to assess the performance of bus depots of Kerala State Road Transport Corporation, India for the data collected for the year 2015 2018. The efficiency score (TE, PTE and SE) of the 28 depots of Kerala State Road Transport Corporation for the year 2015-18 obtained from CRS and VRS input oriented models along with reference set, peer weights and peer counts are presented in Table II.

            1. Technical Efficiency (TE)

              TE scores are calculated through CRS Model. Table 5.1 shows that out of 28 depots, three depots [D9, D26, D27] are relatively technically efficient (efficiency score =1) and thus form the efficient frontier. The average of TE score works out to be 0.885, which implies that on an average a depot can reduce its resources by 11.5% to obtain the existing level of output. Out of 28 depots, 15 depots have an efficiency score lower than the average efficiency score and 13 depots have higher than the average efficiency.

            2. Pure Technical Efficiency (PTE)

              Table II also provides details about DEA results from VRS model. It is evident from the table that out of 28 depots, seven are efficient (VRS Score = 1), i.e, none of these have scope to further reduce inputs for maintaining the same output level. The remaining 21 depots are relatively inefficient. The average PTE works out to be 0.907. This means that given the scale of operation, on average, a depot can reduce its inputs by 9.3%. Out of the 28 depots, 13 depots have an efficiency score lower than the average efficiency score and 15 depots have higher than the average efficiency.

              It is observed that D12, D14, D22 and D28 are poor in CRS Technical efficiency but efficient in pure technical efficiency. This indicates that these depots are able to convert their inputs into outputs with 100% efficiency but their overall efficiency (TE) is low due to their scale size disadvantage (low scale efficiency). D9 has the highest peer count of 24 and D22 has the peer count of 14, D26 has peer count of 17, D27 has a peer count of 11, and D28 has a peer count of 7(Table II). Therefore, these depots can be considered as the best practice depots.

              TABLE II. CRS AND VRS EFFICIENCY OF DEPOTS

              Depot No.

              Depots name

              CRS Technical Efficiency

              VRS Pure Technical Efficiency

              Efficie ncy Score

              Peer

              Peer Weight

              Peer Coun t

              Efficienc y Score

              Peer

              Peer Weight

              Peer Count

              D1

              Alappuzha

              0.857

              9,26

              0.650,0.190

              0

              0.862

              28,9,26

              0.434,0.544,0.022

              0

              D2

              Aluwa

              0.87

              9,26

              0.254,0.386

              0

              0.884

              28,9,26

              0.974,0.017,0.008

              0

              D3

              Attingal

              0.927

              9,26

              0.543,0.374

              0

              0.929

              28,9,26

              0.224,0.489,0.288

              0

              D4

              Changanassery

              0.777

              9,26

              0.443,0.081

              0

              0.827

              22,27,14

              0.852,0.041,0.107

              0

              D5

              Chengannoor

              0.794

              9,26

              0.559,0.115

              0

              0.813

              9,27,22

              0.317,0.030,0.653

              0

              D6

              Cherthala

              0.906

              9,26

              0.466,0.375

              0

              0.911

              27,9,26

              0.429,0.362,0.209

              0

              D7

              Eranakulam

              0.742

              9,27

              0.731,0.183

              0

              0.747

              9,27,22

              0.603,0.207,0.189

              0

              D8

              Kannur

              0.818

              9,26

              0.923,0.104

              0

              0.823

              12,26,9

              0.163,0.038,0.800

              0

              D9

              Kasergode

              1

              9

              1

              24

              1

              9

              1

              18

              D10

              Kayamkulam

              0.938

              9,26

              0.406,0.272

              0

              0.952

              9,28,22

              0.226,0.532,0.241

              0

              D11

              Kollam

              0.92

              9,26

              0.693,0.346

              0

              0.929

              12,26,9

              0.241,0.248,0.511

              0

              D12

              Kottarakkara

              0.966

              9,26

              0.757,0.406

              0

              1

              12

              1

              2

              D13

              Kottayam

              0.839

              27,9

              0.218,0.668

              0

              0.847

              9,27,22

              0.502,0.250,0.248

              0

              D14

              Kozhikkode

              0.872

              27,9

              0.293,0.087

              0

              1

              14

              1

              2

              D15

              Moovattupuzha

              0.823

              9,26

              0.528,0.091

              0

              0.854

              9,27,22

              0.151,0.060,0.789

              0

              D16

              Nedumangadu

              0.894

              9,26

              0.297,0.282

              0

              0.914

              28,9,22

              0.317,0.100,0.584

              0

              D17

              Neyyattinkara

              0.854

              9,26

              0.348,0.456

              0

              0.86

              9,26,28

              0.220,0.251,0.529

              0

              D18

              Pala

              0.802

              27,9

              0.108,0.619

              0

              0.825

              9,27,22

              0.218,0.186,0.596

              0

              D19

              Palakkad

              0.969

              9,27

              0.453,0.331

              0

              0.987

              9,27,22

              0.138,0.393,0.469

              0

              D20

              Pappanamcode

              0.891

              26

              0.712

              0

              0.927

              26,22

              0.502,0.498

              0

              D21

              Pathanamthitta

              0.819

              27,9

              0.048,0.606

              0

              0.854

              22,9,27

              0.754,0.099,0.147

              0

              D22

              Perumbavoor

              0.967

              9,26

              0.207,0.273

              0

              1

              22

              1

              14

              D23

              Sulthan bathery

              0.847

              27,9

              0.085,0.765

              0

              0.859

              9,27,22

              0.546,0.128,0.326

              0

              D24

              Thiruvalla

              0.834

              9,26

              0.563,0.058

              0

              0.868

              22,9,27

              0.799,0.125,0.077

              0

              D25

              Thrissur

              0.863

              9,27

              0.330,0.248

              0

              0.918

              27,14,22

              0.203,0.352,0.445

              0

              D26

              Tvm. City

              1

              26

              1

              17

              1

              26

              1

              8

              D27

              Tvm.central

              1

              27

              1

              8

              1

              27

              1

              11

              D28

              Vizhinjam

              0.983

              9,26

              0.243,0.387

              0

              1

              28

              1

              7

            3. Scale Efficiency (SE)

      Scale efficiency is the ratio of TE to PTE score. If the value of SE score is one, then the depot apparently operates at an optimal scale. If the value is less than one, then the depot operates at either small or big relative to its optimum scale size. The fourth column of Table III shows the SE score of the depots.

      The result presented in Table III show that out of 28 depots, only 3 depots are scale efficient (D9, D26 and D27) while the remaining 25 depots are scale inefficient. The average SE is 0.976. It means that on an average a depot may be able to decrease the input by 2.4% maintaining the same output.

      Depot

      Depot name

      Scale efficiency

      Return to scale

      D1

      Alappuzha

      0.994

      Irs

      D2

      Aluwa

      0.984

      Irs

      D3

      Attingal

      0.997

      Irs

      D4

      Changanassery

      0.94

      Irs

      D5

      Chengannoor

      0.977

      Irs

      D6

      Cherthala

      0.994

      Irs

      D7

      Eranakulam

      0.993

      Irs

      D8

      Kannur

      0.993

      Drs

      D9

      Kasergode

      1

      Crs

      D10

      Kayamkulam

      0.986

      Irs

      D11

      Kollam

      0.991

      Drs

      D12

      Kottarakkara

      0.966

      Drs

      D13

      Kottayam

      0.99

      Irs

      Depot

      Depot name

      Scale efficiency

      Return to scale

      D1

      Alappuzha

      0.994

      Irs

      D2

      Aluwa

      0.984

      Irs

      D3

      Attingal

      0.997

      Irs

      D4

      Changanassery

      0.94

      Irs

      D5

      Chengannoor

      0.977

      Irs

      D6

      Cherthala

      0.994

      Irs

      D7

      Eranakulam

      0.993

      Irs

      D8

      Kannur

      0.993

      Drs

      D9

      Kasergode

      1

      Crs

      D10

      Kayamkulam

      0.986

      Irs

      D11

      Kollam

      0.991

      Drs

      D12

      Kottarakkara

      0.966

      Drs

      D13

      Kottayam

      0.99

      Irs

      TABLE III. SCALE EFFICIENCY OF DEPOTS

      D14

      Kozhikkode

      0.872

      Irs

      D15

      Moovattupuzha

      0.965

      Irs

      D16

      Nedumangadu

      0.978

      Irs

      D17

      Neyyattinkara

      0.993

      Irs

      D18

      Pala

      0.972

      Irs

      D19

      Palakkad

      0.981

      Irs

      D20

      Pappanamcode

      0.961

      Irs

      D21

      Pathanamthitta

      0.959

      Irs

      D22

      Perumbavoor

      0.967

      Irs

      D23

      Sulthan bathery

      0.986

      Irs

      D24

      Thiruvalla

      0.96

      Irs

      D25

      Thrissur

      0.941

      Irs

      D26

      Tvm. City

      1

      Crs

      D27

      Tvm.central

      1

      Crs

      D28

      Vizhinjam

      0.983

      Irs

      Mean

      0.976

      It is observed from Table III that only 3 depots have CRS (operates on optimum scale size) and 3 depots have DRS and remaining 22 depots operate under IRS. Figure 1 shows depot- wise DEA Efficiency Score of KSRTC.

      Fig.1. Combined three-year efficiency of depots

  5. CONCLUSION

In this study, an attempt has been made to measure the technical and scale efficiency of the depots of Kerala State Road Transport Corporation using DEA. One input and two output DEA model has been developed with fleet size as inputs and passengers and collection as output. The model has been applied to evaluate 28 bus depots of Kerala State Road Transport Corporations. The model provides relative efficiencies and bench marks (Peer group). From the analysis Trivandrum City and Trivandrum Central are the depots which are working efficiently in every year and have maximum degree of efficiency in every year with an average efficiency score of 1 and Ernakulam is the most inefficient

depot with average efficiency score of 0.741. The overall mean TE of the depots is found to be 88.5%. This indicates that on an average 11.5% of the technical potential of the depot is not in use. And this implies that these depots have the scope of producing the same output with inputs of 11.5% less than the existing level. In the recent years peer count is higher for Kasaragod depot. i.e.It is the depot to which most of the inefficient organizations are compared.

This study has discussed how DEA can be applied to evaluate the degree of efficiency of the depots. Thus, these results give an indication on the degree of efficiency of depots in the process of transforming inputs into output. The results also depend upon the choice of inputs and output and the way the DEA model measures efficiency.

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