A novel Index-based quantification approach for port performance measurement: a case from Indian major ports

ABSTRACT

This paper develops a unified port performance index (PPI) considering different cargo categories and the multi-dimensional nature of port performance indicators/dimensions. This study has used the quintile method to construct PPI. Further, the PPI obtained from the quintile method is compared with weighted unified index (PPI_PCA) using principal component analysis (PCA), extensively used for index development in the literature. A pilot index development is demonstrated using secondary panel data for 12 major Indian ports on five significant dimensions, namely, operations, physical infrastructure, technical infrastructure, finance, and socio-economic. Results show that the JNPT port outperforms all other ports under the container cargo category. Likewise, Kandla port in the liquid port category and Paradip port in the other (dry & break bulk) cargo category are on top. Also, qualitatively similar results and insights are obtained with the PPI_PCA. Subsequently, the panel data regression and efficiency analysis are performed to demonstrate the utility of the proposed index. The results affirm that the operations, physical infrastructure, and socio-economic dimensions have a positive and significant impact on port financial performance. The present study operationalises some key unexplored port performance indicators/dimensions that can enable effective decision-making.

KEYWORDS:

• Port performance index
• performance indicators
• port performance measurement
• port competitiveness
• port selection
• benchmarking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03088839.2022.2116656

Notes

1. CCR model is named after the researchers: Charnes, Cooper, and Rhodes (1978), who introduced the technical efficiency of decision making units (DMUs) based on the constant return to scale assumption.

2. BCC model is named after the researchers: Banker, Charnes, and Cooper (1984), who introduced the pure technical efficiency (PTE) and scale efficiency (SE) of DMUs based on variable return to scale assumption.

3. Scale efficiency = Overall technical efficiency / Pure technical efficiency, or (SE = OTE / PTE).

Additional information

Funding

This work was supported by the Ministry of Shipping, Government of India, Sagarmala [IIT/SRIC/NA/IWL_CYC/2019-2020/224].