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The purpose of this paper is to benchmark the operational and financial performances of the major Indian seaports to help derive useful insights to improve their performance.

A two-stage data envelopment analysis (DEA) methodology has been used with the help of data collected on the 13 major seaports of India. The first stage of the DEA captured the operational efficiencies, while the second stage the financial performance.

A window analysis over a period of three years revealed that no port was able to score an overall average efficiency of 100 per cent. The study identified the better performing units among their peers in both the stages. The contrasting results of the study with the traditional operational and financial performance measures used by the ports helped to derive useful insights.

The data used in the study were majorly limited to the available sources in the public domain. Also, the study was limited to the major seaports which are under the Government of India and no comparisons were carried out with other local or international ports.

There is a need to prioritize investments and improvement efforts where they are most needed, instead of following a generalized approach. Once the benchmark ports are identified, the port authorities and other relevant stakeholders should work in detail on the factors causing inefficiencies, for possible improvements in performance.

This paper carried out a two-stage DEA that helped to derive useful insights on operational efficiency and financial performance of the India seaports. A combination of the financial and operational parameters, along with a comparison of the DEA results with the traditional measures, provided a different perspective on the Indian seaport performance. Considering the scarcity of research papers reported in the literature on DEA-based benchmarking studies of seaports in the Indian context, it has the potential to attract future research in this field.

The purpose of this paper is to study the supply chain resilience of Indian truckload transportation industry, in the event of potential disasters that affect the normalcy of their services. This study helped to identify factors affecting the two important dimensions of resilience, namely, resistive capacity and restorative capacity.

With the help of a comprehensive literature review, the different variables that capture both resistive and restorative capacities were identified. A framework for measurement of resilience was developed and an analytical model using Bayesian belief networks methodology was used to understand the linkages between the variables.

The results also suggest that at present resilience of the supply chains in Indian trucking firms is very low and there is a need for companies to invest in resources to build both in resistive and restorative capacities to enhance resilience.

The results of the model and the sensitivity analysis performed further helped to understand the major drivers that can enhance resilience of truckload firms.

The major contribution of this paper is to develop a quantitative model for resilience modeling in truckload transportation. This model can be updated when a new data arrives.

The purpose of this paper is to evaluate performance of Indian telecom service providers through a benchmarking study of their operational efficiency and service delivery effectiveness. The paper also carries out a peer-to-peer comparison and identifies-specific areas of improvement for different service providers to attain sustainable growth and profitability.

A two stage data envelopment analysis (DEA) model was used to compare the performances of the service providers. The first stage represents how efficiently a unit is able to use its infrastructure and resources to generate better quality services. The second stage captures how well the company is able to communicate and deliver these services to the customer.

The results of the study support the applicability of the two stage DEA for comparing the performances of the telecom service providers as they are in line with the financial performance indicators and brand ranking. It is observed that those companies which score high on both operational efficiency and service delivery effectiveness have achieved superior profitability.

This study has been carried out at a pan-India level and hence does not take into account circle level or local performance which varies significantly for most service providers. Besides, this the analysis was constrained by limited data in the public domain, which necessitated estimations and extrapolations for some variables of few service providers.

The study has helped to provide inputs for the Indian telecom companies for potential performance improvements by providing a comparative analysis of their operational efficiency and service delivery effectiveness. It has enabled to derive deeper insights on potential target areas for managerial attention that could be translated into implementable actions. The benchmarking analysis has also helped to understand whether the current performance of the service provider is sustainable, unprofitable or ephemeral.

This paper goes beyond the traditional benchmarking studies of Indian telecom service providers introducing a two stage DEA model to understand the operational efficiency as well as the service delivery effectiveness. The study has helped to derive valuable academic and practical insights on the issue of performance measurement of the Indian telecom service providers.

The paper aims to carry out a performance benchmarking study of the zones of Indian Railways (IR) to develop an alternate approach for measurement of aggregate operational performance of the railway zones and to envisage its operations in a supply chain perspective, so as to gain academic and practical insights.

A case study research employing data envelopment analysis (DEA) methodology has been used, with the help of data obtained from the IR annual statistical statements published by the Ministry of Railways, Government of India.

Within the set of inputs and outputs considered, the exercise identified the best performing railway zones over the years and the efficiency trends. Some weaknesses of the conventional DEA were addressed by including the concept of cross‐efficiencies along with self‐efficiencies, by analyzing longitudinal data spread over four years and also by comparing the efficiencies with the operating ratios. To an extent, this study has also helped to understand the impact of the recent restructuring of the zones on their performance.

The study enables the reader to gain some valuable insights from a managerial perspective for IR so as to formulate strategies of its zones to foster better performance.