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Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach

Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach

Neeraj Bhanot, Harwinder Singh
Copyright: © 2019 |Pages: 18
ISBN13: 9781522573623|ISBN10: 1522573623|EISBN13: 9781522573630
DOI: 10.4018/978-1-5225-7362-3.ch013
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MLA

Bhanot, Neeraj, and Harwinder Singh. "Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach." Advanced Methodologies and Technologies in Business Operations and Management, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2019, pp. 173-190. https://doi.org/10.4018/978-1-5225-7362-3.ch013

APA

Bhanot, N. & Singh, H. (2019). Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach. In M. Khosrow-Pour, D.B.A. (Ed.), Advanced Methodologies and Technologies in Business Operations and Management (pp. 173-190). IGI Global. https://doi.org/10.4018/978-1-5225-7362-3.ch013

Chicago

Bhanot, Neeraj, and Harwinder Singh. "Benchmarking Performance Indicators of Indian Rail Freight by DEA Approach." In Advanced Methodologies and Technologies in Business Operations and Management, edited by Mehdi Khosrow-Pour, D.B.A., 173-190. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7362-3.ch013

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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 competition in the container segment in January 2006 through private-public participation for customer-centric competitiveness. The purpose of this 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 to 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 100% (BCC model) within CONCOR, owing to lack of operational planning and lack of efficient staff.

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