Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach

Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach

Neeraj Bhanot (Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India) and Harwinder Singh (Guru Nanak Dev Engineering College, India)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-2255-3.ch051

Abstract

The overall development of business operations logistics activities becomes more important for firms with the globalization of economy and therefore performance measurement being equally important. In order to break monopolistic control of Container Corporation of India (CONCOR), Indian Railways entered for competition in the container segment in January 2006 through private-public participation for customer centric competitiveness. The purpose of this book chapter is to benchmark the performance indicators in CONCOR. A case study has been conducted employing super-efficiency models of Data Envelopment Analysis (DEA) on secondary data of CONCOR container terminals from 1994-95 till 2015-16 for performance evaluation within CONCOR. The exercise identified efficiency trends fluctuating between 75.83% to 109.51% (CCR Model) and 93.52% to 1000% (BCC Model) within CONCOR owing to lack of operational planning and lack of efficient staff.
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Introduction

To accomplish the organization’s logistics/distribution activities, measurement systems have been designed to capture information regarding five types of performance. These five categories are: asset management, cost, customer service, productivity and logistics quality (Li, 2005).

Li (2005) discussed several evaluation methods for logistics performance. Qualitative and quantitative data can be dealt by AHP method but the weight of different factors is objective. The result is influenced by the subjectivity greatly. Mathematic statistics method mainly conducts the quantitative data, and the evaluation process is objective. However strong qualitative analysis ability has been shown by Fuzzy comprehensive evaluation and Rough sets method. The above methods are not systematized as a whole. The objects, contents and methods are too much complicated to be integrated. But DEA (Data Envelopment Analysis) especially suitable for multi-input and multi-output complicated systems take the weights of input and output of DMU (Decision Making Units) for variables, evaluates in terms of DMU. In a word, DEA is more suitable evaluation method for the logistics activities as it has very strong objectivity.

Data Envelopment Analysis (DEA)

DEA measurement can be defined as the ratio of total weighted output to total weighted input. It is a linear programming-based technique that converts multiple input and output measures into a single comprehensive measure of productivity efficiency (Epstein, & Henderson, 1989). DEA provides a measure by which one firm or department can compare its performance, in relative terms, with other homogeneous firms or departments. In measuring the relative efficiencies of organizations with DEA each organization can utilize different weights for the set of performance measures. Weights are selected that will maximize the composite efficiency score for each functional unit. Taking into account differences in goals, responsibilities, and type of procurement, this variable weighting allows for the evaluation of performance. The range of possible weights is controlled by requiring all weights to be positive, and specifying that if another unit used the same weight, their total efficiency score could not exceed the value of one. The technique also gives information as to the specific effect each input or output has on overall efficiency as yet, which has demonstrated its use as a potential evaluation tool for logistics performance. In summation, DEA can be used to reduce some of the existing problems with performance evaluation system (Charnes, Cooper, & Rhodes, 1978).

An examination of the literature reveals the application of DEA for performance benchmarking in a variety of settings such as telecommunications (Lupi, Manenti, Scialà, & Varin, 2011), educational institutes (Portela, Camanho, & Borges, 2011), productive efficiency (Farrell, 1957; Chandraprakaikul, & Suebpongsakorn, 2012; Holden, Xu, Greening, Piecyk, & Dadhich, 2016), Irrigation sector (Phadnis, & Kulshrestha, 2012), safety performance (Beriha, Patnaik, & Mahapatra, 2011), etc.

Key Terms in this Chapter

Super-Efficiency: The Super-efficiency analysis allows calculating efficiency improvements for efficient units.

Performance Evaluation: The analysis in terms of initial objectives and estimates, and usually made on site, of accomplishments using an automatic data-processing system, to provide information on operating experience and to identify corrective actions required, if any.

Indian Railways: Indian Railways (IR) is an Indian state-owned enterprise, owned and operated by the government of India through the Ministry of Railways.

Container Corporation of India: Container Corporation of India Ltd. (CONCOR) is a Category I Miniratna Public sector undertaking under the Indian Ministry of Railways. Incorporated in March 1988 under the Companies Act, Concor commenced operations in November 1989 taking over an existing network of seven inland container depots (ICDs) from Indian Railways.

Data Envelopment Analysis: Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (or DMUs).

Benchmarking: It is a management tool through which a plan for evaluation, measurement, and improvement is implemented.

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