Abstract
The present scenario of India is highly urban-centric regarding business development. The growing knowledge and consciousness of brands in rural India attract attention in spite of tedious supply chain which has levels of hierarchies for suppliers, distributors and finally the consumers. The business requires a mechanism to track the goods at every level of the supply chain system. The report released by the Internet and Mobile Association of India (IAMAI) in 2017 points that out of the total 481 million Internet users, urban India has 295 million Internet users and the rest 186 million is rural. This is a huge opportunity to tap the rural market. The proposed model presents a framework of crowdsourcing applications for the optimization of the supply chain. The focus is on developing effective communication and mapping value of the goods produced and making available goods and services at low cost to the consumers. There is a potential for parcel delivery to villagers who are registered and willing to deliver for a nominal amount. Crowdsourcing approach can fasten the delivery time and lessen the cost too by the implementation of the algorithm. The trend analysis was done to understand the current demand. The gap between urban and rural markets can be reduced with this endeavor which involves Crowdshipping as a tool to optimize resources.
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Acknowledgements
Apart from few academicians we contacted in Indian Institute of Management, Lucknow (IIM-L), for academic and research guidance, this theoretical work was possible only because of the extensive and timely support of the CSCs and the staff present there. The knowledge and the real-life ground realities explained to us by them helped us to understand the problems and opportunities at the grassroot level. The coordination with them provided us with valuable insights and has made the project possible.
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Upadhyay, C.K., Tiwari, V., Tiwari, V., Pandiya, B. (2020). Crowdsourcing Advent to Optimize Supply Chain Network in Rural India. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_8
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