Role of Big Data in Reverse Supply Chain
Keywords:reverse supply chain levels (RSCL), big data analytics (BDA), manufacturing industries, reverse supply chain competences, Reverse supply chain(RSC)
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 emphasises on understanding of BDA concept in Indian industries and proposes a structure to examine industriesâ€™ 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 response rate of 25 percent, which was slightly greater than previous studies (Queiroz and Telles,2018).The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with RSCL 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 predictable direction through the BDA-RSCM triangle structure as 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 BDAon reverse supply chains(RSC) in developed countries.
 Addo-Tenkorang, R. and Helo, P.T. (2016), â€œBig data applications in operations/supply-chain management: A literature reviewâ€, Computers and Industrial Engineering, Vol. 101, pp. 528-543.
 Aggestam, V., FleiÃŸ, E. and Posch, A. (2017), â€œScaling-up short food supply chains? A survey study on the drivers behind the intention of food producersâ€, Journal of RuralStudies, Vol. 51, pp. 64-72.
 Akhtar, P., Khan, Z., Rao-Nicholson, R. and Zhang, M. (2016), â€œBuilding relationship innovation in global collaborative partnerships: Big data analytics and traditional organizational powersâ€, R & D Management.
 Akter, S., FossoWamba, S., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016), â€œHow to improve firm performance using big data analytics capability and business strategy alignment?â€,International Journal of Production Economics, Vol. 182, pp. 113-131.
 Barney, J., Wright, M., and Ketchen, D.J. (2001), â€œThe resource-based view of the firm:
 Ten years after 1991â€, Journal of Management, Vol. 27, pp. 625â€“641.
 Chae, B. (2015), â€œInsights from hashtag #supplychain and Twitter analytics: Considering Twitter and Twitter data for supply chain practice and researchâ€, International Journal of Production Economics, Vol. 165, pp. 247-259.
 Chauhan, S., Agarwal, N. and Kar, A.K. (2016), â€œAddressing big data challenges in smart cities: a systematic literature reviewâ€, Info, Vol. 18 No. 4, pp. 73-90.
 Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., and Zhou, X. (2013), â€œBig data challenge: a data management perspectiveâ€. Frontiers of Computer Science, Vol. 7 No. 2, pp. 157-164.
 Chen, I.J., and Paulraj, A. (2004), â€œTowards a theory of supply chain management: The constructs and measurementsâ€, Journal of Operations Management, Vol. 22 No. 2, pp. 119â€“150.
 Comuzzi, M. and Patel, A. (2016), â€œHow organisations leverage: Big Data: A maturity modelâ€, Industrial Management and Data Systems,Vol. 116, No. 8, pp. 1468-1492.
 Curran, P.J., West, S.G., and Finch, J.F. (1996), â€œThe robustness of test statistics to nonnormality and specification error in confirmatory factor analysisâ€, PsychologicalMethods, Vol. 1 No. 1, pp. 16â€“29.
 Davenport, T.H. (2006), â€œCompeting on analyticsâ€, Harvard business review, Vol. 84 No. 1, pp. 84-93.
 Demirkan, H. and Delen, D. (2013), â€œLeveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloudâ€, Decision SupportSystems, Vol. 55, pp. 412-421.
 DeVellis, R.F. (2012), â€œScale Development: Theory and Applicationsâ€, Vol. 26. Sage Publications, US.
 Domo. (2017), â€œData never sleeps 4.0â€, available at:https://www.domo.com/blog/data-never-sleeps-4-0 (accessed 10 March 2017).
 Dubey, R., Gunasekaran, A., Childe, S.J. FossoWamba, S. and Papadopoulos, T. (2016), â€œThe impact of big data on world-class sustainable manufacturingâ€, TheInternational Journal of Advanced Manufacturing Technology, Vol. 84, pp. 631-645.
 Eckstein, D., Goellner, M., Blome, C. and Henke, M. (2015), â€œThe performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexityâ€, International Journal of Production Research, Vol. 53 No. 10, pp. 3028-3046.
 Ellram, L.M., Tate, W.L., and Petersen, K.J. (2013), â€œOffshoring and reshoring: an update on the manufacturing location decisionâ€, Journal of Supply Chain Management, Vol. 49 No. 2, pp. 14â€“22.
 Erevelles, S., Fukawa, N. and Swayne, L. (2016), â€œBig Data consumer analytics and the transformation of marketingâ€, Journal of Business Research, Vol. 69 No. 2, pp. 897-904.
 Excelacom. (2016), â€œ2016 update: what happens in one internet minute?â€, available at: http://www.excelacom.com/resources/blog/2016-update-what-happens-in-one-internet-minute (accessed 10 March 2017).
 Forza, C. (2002), â€œSurvey research in operations management: a process-based perspectiveâ€, International Journal of Operations & Production Management, Vol. 22 No. 2, pp. 152-194.
 FossoWamba, S., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015), â€œHow â€˜big dataâ€™ can make big impact: Findings from a systematic review and a longitudinal case studyâ€, International Journal of Production Economics, Vol. 165, pp. 234-246.
 FossoWamba, S., Gunasekaran, A., Akter, S., Ren, S.J.-., Dubey, R. and Childe, S.J. (2017), â€œBig data analytics and firm performance: Effects of dynamic capabilitiesâ€, Journal of Business Research, Vol. 70, pp. 356-365.
 Giannakis, M. and Louis, M. (2016), â€œA multi-agent based system with big data processing for enhanced supply chain agilityâ€, Journal of Enterprise InformationManagement, Vol. 29 No. 5, pp. 706-727.
 Gobble, M.M. (2013), â€œBig data: The next big thing in innovationâ€, ResearchTechnology Management, Vol. 56 No. 1, pp. 64-66.
 Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S. (2017), â€œBig data and predictive analytics for supply chain and organizational performanceâ€, Journal of Business Research, Vol. 70, pp. 308-317.
 Gupta, M. and George, J.F. (2016), â€œToward the development of a big data analytics capabilityâ€, Information & Management, Vol. 53, pp. 1049-1064.
 Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), â€œMultivariate data analysisâ€. Vol. 6, Upper Saddle River, NJ: Pearson Prentice Hall.
 Hazen, B.T., Boone, C.A., Ezell, J.D. and Jones-Farmer, L.A. (2014), â€œData quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applicationsâ€, International Journal of Production Economics, Vol. 154, pp. 72-80.
 Hazen, B.T., Skipper, J.B., Ezell, J.D. and Boone, C.A. (2016), â€œBig data and predictive analytics for supply chain sustainability: A theory-driven research agendaâ€, Computersand Industrial Engineering, Vol. 101, pp. 592-598.
 He, W., Wang, F.-. andAkula, V. (2017), â€œManaging extracted knowledge from big social media data for business decision makingâ€, Journal of KnowledgeManagement, Vol. 21 No. 2, pp. 275-294.
 Jin, X., Wah, B.W., Cheng, X., and Wang, Y. (2015), â€œSignificance and challenges of big data researchâ€, Big Data Research, Vol. 2 No. 2, pp. 59-64.
 Kache, F. and Seuring, S. (2017), â€œChallenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain managementâ€, InternationalJournal of Operations and Production Management, Vol. 37 No. 1, pp. 10-36.
 Kune, R., Konugurthi, P.K., Agarwal, A., Chillarige, R.R. and Buyya, R. (2016), â€œThe anatomy of big data computingâ€, Software - Practice and Experience, Vol. 46 No. 1, pp. 79-105.
 Lambert, D.M., and Harrington, T.C. (1990), â€œMeasuring nonresponse bias in customer service mail surveysâ€, Journal of Business Logistics, Vol. 11, No. 2, pp. 5â€“25.
 Landis, J.R. and Koch, G.G. (1977), â€œThe measurement of observer agreement for categorical dataâ€, Biometrics, Vol. 33, pp. 159-174.
 Larson, P.D. (2005), â€œA note on mail surveys and response rates in logistics researchâ€, Journal of Business Logistics, Vol. 26 No. 2, pp. 211-222.
 Lee, J. (2015), â€œSmart factory systemsâ€, Informatik-Spektrum, Vol. 38 No. 3, pp. 230-235.
 Lugmayr, A., Stockleben, B., Scheib, C. and Mailaparampil, M.A. (2017), â€œCognitive big data: survey and review on big data research and its implications. What is really â€œnewâ€ in big data?â€,Journal of Knowledge Management, Vol. 21 No. 1, pp. 197-212.
 Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), â€œBig Data: the Next Frontier for Innovation, Competition and Productivityâ€, McKinsey Global Institute.
 Marshall, A., Mueck, S. and Shockley, R. (2015), â€œHow leading organizations use big data and analytics to innovateâ€, Strategy and Leadership, Vol. 43 No. 5, pp. 32-39.
 Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J. and FossoWamba, S.( 2017), â€œThe role of Big Data in explaining disaster resilience in supply chains for sustainabilityâ€, Journal of Cleaner Production, Vol. 142, pp. 1108-1118.
 Pauleen, D.J. and Wang, W.Y.C. (2017), â€œDoes big data mean big knowledge? KM perspectives on big data and analyticsâ€, Journal of Knowledge Management, Vol. 21 No. 1, pp. 1-6.
 Podsakoff, P.M., & Organ, D.W. (1986), â€œSelf-reports in organizational research:
 Problems and prospectsâ€, Journal of Management, Vol. 12, pp. 69â€“82.
 Qin, J., Liu, Y. and Grosvenor, R. (2016). â€œA categorical framework of manufacturing for industry 4.0 and beyondâ€, Procedia CIRP, Vol. 52, pp. 173-178.
 Queiroz, M. M.andTelles, R(2018), â€œBig data analytics in supply chain and logistics: an empirical approachâ€ The International Journal of Logistics Management,Vol-29,No-2,pp.767-783
 Rothberg, H.N. and Erickson, G.S. (2017), â€œBig data systems: knowledge transfer or intelligence insights?â€,Journal of Knowledge Management, Vol. 21 No. 1, pp. 92-112.
 Schoenherr, T. and Speier-Pero, C. (2015), â€œData science, predictive analytics, and big data in supply chain management: Current state and future potentialâ€, Journal ofBusiness Logistics, Vol. 36 No. 1, pp. 120-132.
 Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V. (2017), â€œCritical analysis of Big Data challenges and analytical methodsâ€, Journal of Business Research, Vol. 70, pp. 263-286.
 Stock, T. and Seliger, G. (2016), â€œOpportunities of sustainable manufacturing in industry 4.0â€ Procedia CIRP, Vol. 40, pp. 536-541.
 Strawn, G.O. (2012), â€œScientific Research: How Many Paradigms?â€ Educause Review Vol. 47 No. 3, pp. 26-34.
 Tan, K.H., Zhan, Y., Ji, G., Ye, F. and Chang, C. (2015), â€œHarvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graphâ€, International Journal of Production Economics, Vol. 165, pp. 223-233.
 Tokman, M., Richey, R.G., Deitz, G.D., and Adams, F.G. (2012), â€œThe retailerâ€™s perspective on the link between logistical resources and perceived customer loyalty to manufacturer brandsâ€, Journal of Business Logistics, Vol. 33 No. 3, pp. 181-195.
 Waller, M.A. and Fawcett, S.E. (2013), â€œData science, predictive analytics, and big data: A revolution that will transform supply chain design and managementâ€, Journal ofBusiness Logistics, Vol. 34 No. 2, pp. 77-84.
 Wang, G., Gunasekaran, A., Ngai, E.W.T. and Papadopoulos, T. (2016), â€œBig data analytics in logistics and supply chain management: Certain investigations for research and applicationsâ€, International Journal of Production Economics, Vol. 176, pp. 98-110.
 Wang, S., Wan, J., Li, D. and Zhang, C. (2016), â€œImplementing smart factory of industries 4.0: An outlookâ€, International Journal of Distributed Sensor Networks, Vol. 2016, pp. 1-10.
 Watson, H.J. (2014), â€œTutorial: big data analytics: Concepts, technologies, and applicationsâ€, Communications of the Association for Information Systems, Vol. 34 No. 1, pp. 1247-1268.
 Wernerfelt, B. (1984), â€œA resource-based view of the firmâ€, Strategic Management Journal, Vol. 5 No. 2, pp. 171â€“180.
 Wills, M.J. (2014), â€œDecisions through data: Analytics in healthcareâ€, Journal ofHealthcare Management, Vol. 59, pp. 254-262
 Wu, K.-., Liao, C.-., Tseng, M.-., Lim, M.K., Hu, J. and Tan, K. (2017), â€œToward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertaintiesâ€, Journal of Cleaner Production, Vol. 142, pp. 663-676.
 Zelbst, P.J., Green, K.W., Sower, V.E., and Reyes, P.M. (2012), â€œImpact of RFID on manufacturing effectiveness and efficiencyâ€, International Journal ofOperations & Production Management, Vol. 32 No. 3, pp. 329-350.
 Zhao, R., Liu, Y., Zhang, N. and Huang, T. (2017), â€œAn optimization model for green supply chain management by using a big data analytic approachâ€, Journal of CleanerProduction, Vol. 142, pp. 1085-1097.
 Zhong, R.Y., Huang, G.Q., Lan, S., Dai, Q.Y., Chen, X. and Zhang, T. (2015), â€œA big data approach for logistics trajectory discovery from RFID-enabled production dataâ€, International Journal of Production Economics, Vol. 165, pp. 260-272.
 Zhong, R.Y., Newman, S.T., Huang, G.Q. and Lan, S. (2016), â€œBig Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectivesâ€, Computers and Industrial Engineering, Vol. 101, pp. 572-591.
 Zhou, Z.-., Chawla, N.V., Jin, Y. and Williams, G.J. (2014), â€œBig data opportunities and challenges: Discussions from data analytics perspectivesâ€, IEEE ComputationalIntelligence Magazine, Vol. 9 No. 4, pp. 62-74.
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