Strategies to Accelerate Manufacturing Growth in India: A System Dynamics–Interpretive Structural Modelling Analysis
Ravindra Ojha and
Prem Vrat
Vision, 2016, vol. 20, issue 2, 85-100
Abstract:
The stagnant manufacturing sectoral share in India’s gross domestic product (GDP) needs an in-depth understanding, analysis and recommendation for growth. Taking manufacturing to a higher growth trajectory has now become a necessity for India’s prosperity. The 2011 National Planning Commission’s (NMP) objective of 25 per cent contribution by manufacturing to India’s GDP by 2025, though challenging, needs an urgent focus. This article utilizes system dynamics (SD)-based simulation for three futuristic economic scenarios to assess the achievability of the NMP targets. The article also provides an understanding of a few critical issues of the manufacturing sector using the causal loop diagram approach of SD. The article also applies the interpretive structural modelling approach to 19 factors identified from the literature, which impact the manufacturing sector. Nine driving factors have been identified for accelerating the manufacturing sector: Technology, labour Reforms, Infrastructural and energy reforms, Good Governance, quality Education and Resource management (TRIGGER). The four dependent factors identified are Domestic value addition, Macroeconomic health, manufacturing Investment and Green-field projects (DMIG).
Keywords: Service Sector; Manufacturing Sector; Agriculture and Allied Sector; GDP; CLD; SD and ISM (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0972262916637255 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:vision:v:20:y:2016:i:2:p:85-100
DOI: 10.1177/0972262916637255
Access Statistics for this article
More articles in Vision
Bibliographic data for series maintained by SAGE Publications ().