Exploring Key Success Factors of Indian Pharmaceutical Supply Chain Using Interpretive Structural Modelling

Exploring Key Success Factors of Indian Pharmaceutical Supply Chain Using Interpretive Structural Modelling

Anurag Mishra, Pankaj Dutta, Suruj Kakoti
Copyright: © 2020 |Pages: 16
DOI: 10.4018/978-1-7998-2216-5.ch003
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Indian pharmaceutical industry is witnessing enormous challenges due to varying patent laws, increasing demand, and continuous pressure from the government to provide medicines at a lower price. To overcome these challenges, there is a need for a more robust supply chain (SC) which will help in information sharing and reduce overall cost. The chapter determines the key drivers of Indian pharmaceutical SC, and draws the attention of industry, stakeholders, and top management to emphasise on these drivers to enhance the performance and profitability of SC. An interpretive structural modelling-based approach has been employed to model the pharmaceutical SC key drivers. The 16 key parameters have been identified across all major dimensions such as SC, HR, & organizational, market, technology, and reverse logistics. Further fuzzy MICMAC analysis is done to categorize based on their driving and dependence power. The factors like collaborative relationship among SC partners, quality regulations, third party logistics, and end-to-end responsive SC are found to be more important enablers.
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Literature Review

A well synchronized SC focuses on customer satisfaction, sustaining competency and continuous improvement in performance. These coordinating actions are tough to finalize due to different working cultures of organizations (Narayanan and Raman, 2004).

Cooper et al. (1997) said that support from the top management, business objectives that are being set are the major drivers influencing SC practices. Shah (2012) had done a thorough literature review addressing the key challenges, the drivers and major components working in a pharmaceutical supply chain. They also addressed the challenges with the solution strategy and future directions. Papageorgiou et al., (2001) used an optimization-based approach for the selection of strategy for product development, capacity and investment in case of pharmaceutical industries. The problem is formulated a s Mixed integer linear programming (MILP) and further an example is used to validate the model. Kumar et al., (2009) studies the control measures needed in the Supply chain of pharmaceutical industry, analysed the whole system using DMAIC process. The target of the study was to improve the reverse logistics process which in turn will help to reduce the harm to a consumer.

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