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Multiple objective data envelopment analysis as applied to the Indian Pharmaceutical Industry

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  • H Saranga

    (Indian School of Business)

Abstract

The change of Intellectual Property Protection (IPP) from a softer process patenting to a stronger product patenting in Indian Pharmaceutical Industry (IPI) is attracting many global drug majors to source their production from India, which is the fourth largest producer of pharmaceuticals in the world. In this paper, the interests of different stake holders like the buyers (multinational enterprises), who are searching for efficient partners and the vendors (Indian drug producers) that are competing for the contracts, are analysed for a suitable efficiency evaluation criterion. The primary objective of this paper is to study how various firms in the IPI with different business strategies, competing for the same opportunities can find suitable benchmarking peer groups to meet the challenges of a dynamic business environment using data envelopment analysis (DEA). A multiple objective DEA model that determines suitable peer groups for inefficient companies is discussed along with more traditional DEA models. The proposed model has the flexibility to include inputs like R&D expenditure and outputs like Exports that are not homogeneously distributed across the firms and address the interests of various stake holders like buyers and vendors simultaneously.

Suggested Citation

  • H Saranga, 2007. "Multiple objective data envelopment analysis as applied to the Indian Pharmaceutical Industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1480-1493, November.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:11:d:10.1057_palgrave.jors.2602280
    DOI: 10.1057/palgrave.jors.2602280
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Huang, Z. M. & Sun, D. B., 1990. "Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 73-91.
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    4. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
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    Cited by:

    1. White, Leroy & Smith, Honora & Currie, Christine, 2011. "OR in developing countries: A review," European Journal of Operational Research, Elsevier, vol. 208(1), pages 1-11, January.
    2. H Saranga & R D Banker, 2010. "Productivity and technical changes in the Indian pharmaceutical industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(12), pages 1777-1788, December.
    3. Yung-Hsiang LU & Ku-Hsieh CHEN & Chun-Cheng WU, 2015. "Cross-country analysis of efficiency and productivity in the biotech industry: an application of the generalized metafrontier Malmquist productivity index," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 61(3), pages 116-134.
    4. Yung-Hsiang LU & Shun-Ching WANG & Chih-Hung YUAN, 2017. "Financial crisis and the relative productivity dynamics of the biotechnology industry: Evidence from the Asia-Pacific countries," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(2), pages 65-79.
    5. Varun Mahajan & D. K. Nauriyal & S. P. Singh, 2018. "Efficiency and Its Determinants: Panel Data Evidence from the Indian Pharmaceutical Industry," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 12(1), pages 19-40, February.
    6. Varun Mahajan & D. K. Nauriyal & S. P. Singh, 2020. "Domestic market competitiveness of Indian drug and pharmaceutical industry," Review of Managerial Science, Springer, vol. 14(3), pages 519-559, June.

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