Impact of big data analytics in reverse supply chain of Indian manufacturing industries: An empirical research

Behera, A.

Abstract: The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasizes on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 500 industries. Of the 500 surveys that were mailed, 125 completed surveys were returned, corresponding to a re-sponse rate of 25 percent, which was slightly greater than previous studies. The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized. A structure was presented for the selection of BDA ventures in RSCM. This paper gives bits of knowledge to professionals to create activities including big data and RSCM, and presents utilitarian and predict-able direction through the BDA-RSCM triangle structure as an extra device in the execution of BDA ventures in the RSCM factors. This paper does not provide outside legitimacy owing to limitations for the speculation of the outcomes even in the Indian surroundings, which originates from the present test. Future research ought to enhance the understanding in this area and spotlight on the effect of big data on reverse supply chains in developed countries.

How to cite this paper

        APA: Behera, A. (2019). Impact of big data analytics in reverse supply chain of Indian manufacturing industries: An empirical research. International Journal of Data and Network Science, 3(1), 37-46.
    

        Chicago/Turabian: Behera, A. 2019. "Impact of big data analytics in reverse supply chain of Indian manufacturing industries: An empirical research." International Journal of Data and Network Science 3, no. 1 (2019): 37-46.
    

        AMA: Behera, A. Impact of big data analytics in reverse supply chain of Indian manufacturing industries: An empirical research. International Journal of Data and Network Science. 2019;3(1):37-46.
    

References

Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain manage-ment: A literature review. Computers & Industrial Engineering, 101, 528-543.
Aggestam, V., Fleiß, E., & Posch, A. (2017). Scaling-up short food supply chains? A survey study on the drivers behind the intention of food producers. Journal of rural studies, 51, 64-72.
Akhtar, P., Khan, Z., Rao‐Nicholson, R., & Zhang, M. (2016). Building relationship innovation in glob-al collaborative partnerships: big data analytics and traditional organizational powers. R&D Man-agement.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm per-formance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, 113-131.
Chae, B. K. (2015). Insights from hashtag# supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Eco-nomics, 165, 247-259.
Chauhan, S., Agarwal, N., & Kar, A. K. (2016). Addressing big data challenges in smart cities: a sys-tematic literature review. info, 18(4), 73-90.
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: a data man-agement perspective. Frontiers of Computer Science, 7(2), 157-164.
Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of operations management, 22(2), 119-150.
Comuzzi, M., & Patel, A. (2016). How organisations leverage big data: A maturity model. Industrial Management & Data Systems, 116(8), 1468-1492.
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological methods, 1(1), 16.
Davenport, T. H. (2006). Competing on analytics. harvard business review, 84 (1), 98. Надійшла до редколегії, 3, 14.
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufactur-ing Technology, 84(1-4), 631-645.
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capabil-ity. Information & Management, 53(8), 1049-1064.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone, C. A. (2016). Big Data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineer-ing, 101, 592-598.
He, W., Wang, F. K., & Akula, V. (2017). Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management, 21(2), 275-294.
Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data re-search. Big Data Research, 2(2), 59-64.
Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Produc-tion Management, 37(1), 10-36.
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience, 46(1), 79-105.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical da-ta. biometrics, 159-174.
Larson, P. D. (2005). A note on mail surveys and response rates in logistics research. Journal of Busi-ness Logistics, 26(2), 211-222.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the man-agement revolution. Harvard business review, 90(10), 60-68.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big da-ta: The next frontier for innovation, competition, and productivity.
Marshall, A., Mueck, S., & Shockley, R. (2015). How leading organizations use big data and analytics to innovate. Strategy & Leadership, 43(5), 32-39.
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108-1118.
Pauleen, D. J., & Wang, W. Y. (2017). Does big data mean big knowledge? KM perspectives on big da-ta and analytics. Journal of Knowledge Management, 21(1), 1-6.
Rothberg, H. N., & Erickson, G. S. (2017). Big data systems: knowledge transfer or intelligence in-sights?. Journal of Knowledge Management, 21(1), 92-112.
Schoenherr, T., & Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120-132.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data chal-lenges and analytical methods. Journal of Business Research, 70, 263-286.
Strawn, G. O. (2012). Scientific Research: How Many Paradigms?. Educause Review, 47(3), 26.
Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.
Var, I. (1998). Multivariate data analysis. vectors, 8(2), 125-136.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data an-alytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of industrie 4.0: an out-look. International Journal of Distributed Sensor Networks, 12(1), 3159805.
Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. CAIS, 34, 65.
Zelbst, P. J., Green, K. W., Sower, V. E., & Reyes, P. M. (2012). Impact of RFID on manufacturing ef-fectiveness and efficiency. International Journal of Operations & Production Management, 32(3), 329-350.

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