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Top1. Introduction
Big data analytics (BDA) has explored as a technique to achieve healthy advantage for manufacturing industries in current scenario(Tan et al., 2015; Davenport, 2006).It has reflected more importance to research scholars and academicians(Dubey et al., 2016).According to Strawn (2012), big data has great impact to industry 4.0 paradigm. Gobble (2013) considered BDA techniques as big innovation for remodelling in manufacturing industries. Currently, reverse supply chain management has received more attention by BDA (Jin et al., 2015; Hazen et al., 2016; Zhong et al., 2016; Fosso Wamba et al., 2017; Gunasekaran et al., 2017; Pauleen and Wang, 2017; Rothberg and Erickson, 2017;).
Data, process, and management challenges are three classifications of big data as per (Sivarajah et al. 2017). With the help of resource-based view (RBV) approach (Gunasekaran et al. 2017), it showed the importance and impact of resources and capabilities in supply chain costs and efficiency. RBV approach and model proposal were studied by Fosso Wamba et al. (2017) to have the impact of big data analytics capability (BDAC) on a industry performance. Industry performance and resources by BDAC are studied by Akter et al. (2016) and Gupta and George (2016) respectively.
Though a lot of achievements are reflected in recent research papers, various gaps are remained open and particularly in empirical research (Comuzzi and Patel, 2016; Strawn, 2012, Fosso Wamba et al., 2015; Kache and Seuring, 2017)..BDAC on industry performance and sustainable manufacturing were supported by(Fosso Wamba et al., 2017; Gupta and George, 2016, Dubey et al., 2016).However, a lot of gaps exist in reverse supply chain regarding BDA projects on development of frameworks and empirical research particularly in developing countries.
Manufacturing industries don't know about the development level of BDA or whether the industries’ present abilities are adequate for directing an execution of a BDA venture in RSCM. The research on BDA isn't sufficiently wide and does not offer models as well as structures to examine the attainability of actualizing a big data venture. In this unique situation, Indian writings about BDA in reverse supply chain management (RSCM) can be comprehended to be moderately restricted.
To add to the progression of information and decrease perception flaws related with BDA in RSCM, this examination means to answer the accompanying inquiries:
Question 1: What are the troubles with boundaries for the reception of BDA in Indian reverse supply chains?
Question 2: What are the fundamental contrasts and effects of BDA on various manufacturing industries and reverse supply chain levels?
The essential commitment of present paper is the distinguishing proof of the fundamental troubles and boundaries for execution of BDA techniques in RSCM conditions in Indian manufacturing industries. The second commitment is the proposition of a reference framework (BDA-RSCM triangle) to help researchers in BDA projects with regards to RSCM. Besides, this paper adds to the BDAC reverse supply chain literature (Akter et al., 2016; Fosso Wamba et al., 2017; Gupta and George, 2016) by researching segments of reverse supply chain partnerships (RSCP), human knowledge (HK), and innovation culture (IC) (BDA-RSCM triangle).