Modeling drivers to big data analytics in supply chains

Authors

  • Md. Nura Alam Siddique Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Kazi Wahadul Hasan Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Syed Mithun Ali Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Md. Abdul Moktadir Department of Leather Products Engineering, Institute of Leather Engineering & Technology, University of Dhaka, Bangladesh
  • Sanjoy Kumar Paul UTS Business School, University of Technology Sydney, Sydney, Australia
  • Golam Kabir Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada

Keywords:

Big data analytics, Multi criteria decision making, Best-worst method, Drivers, Supply chain management

Abstract

The recent emergence of data-driven business markets and the ineligibility of traditional data management systems to trace them have fostered the application of Big Data Analytics (BDA) in supply chains of the present decade. Literature reviews reveal that the successful implication of BDA in a supply chain mainly depends on some key drivers considering the size and operations of an organization. However, collective analysis of all these drivers is still neglected in the existing research field. Therefore, the purpose of this research is to identify and prioritize the most significant drivers of BDA in the supply chains. To this aim, a novel Best-worst method (BWM) based framework has been proposed, which has successfully identified and sequenced the twelve most significant drivers with the help of previous literature and experts’ opinions. Theoretically, this study contributes to the BDA literature by offering some unique drivers to BDA in supply chains. The findings show that ‘sophisticated structure of information technology’ and ‘group collaboration among business partners’ are the top most significant drivers. ‘Digitization of society’ is identified as the least significant driver of BDA in this study. The outcome of this study is expected to assist the industry managers to find out the most and least preferable drivers in their supply chains and then take initiatives to improve the overall efficiency of their organizations accordingly.

References

Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers and Industrial Engineering, 101, 528-543. https://doi.org/10.1016/j.cie.2016.09.023
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459-471. https://doi.org/10.1016/j.comnet.2017.06.013
Amanullah, M. A., Habeeb, R. A. A., Nasaruddin, F. H., Gani, A., Ahmed, E., Nainar, A. S. M., . . . Imran, M. (2020). Deep learning and big data technologies for IoT security. Computer Communications, 151, 495-517. https://doi.org/10.1016/j.comcom.2020.01.016
Amankwah-Amoah, J., & Adomako, S. (2019). Big data analytics and business failures in data-Rich environments: An organizing framework. Computers in Industry, 105, 204-212.
Asrawi, I., Saleh, Y., & Othman, M. (2017). Integrating drivers’ differences in optimizing green supply chain management at tactical and operational levels. Computers and Industrial Engineering, 112, 122-134. https://doi.org/10.1016/j.cie.2017.08.018
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153. https://doi.org/10.1016/j.resconrec.2019.104559
Beemsterboer, D. J. C., Hendrix, E. M. T., & Claassen, G. D. H. (2018). On solving the Best-Worst Method in multi-criteria decision-making?. IFAC-PapersOnLine, 51(11), 1660-1665. https://doi.org/10.1016/j.ifacol.2018.08.218
Belaud, J. P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50.
Berk, J., & van Binsbergen, J. H. (2015). Active Managers Are Skilled. SSRN Electronic Journal, 15-37. https://doi.org/10.2139/ssrn.2616505
Braunscheidel, M. J., & Suresh, N. C. (2009). The organizational antecedents of a firm's supply chain agility for risk mitigation and response. Journal of Operations Management, 27(2), 119-140. https://doi.org/10.1016/j.jom.2008.09.006
Braunscheidel, M. J., Suresh, N. C., & Boisnier, A. D. (2010). Investigating the impact of organizational culture on supply chain integration. Human Resource Management, 49(5), 883-911. https://doi.org/10.1002/hrm.20381
Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002). Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. Quarterly Journal of Economics, 117(1), 339-376. https://doi.org/10.1162/003355302753399526
Bronson, K. (2018). Digitization and big data in food security and sustainability. In Encyclopedia of Food Security and Sustainability (pp. 582-587).https://doi.org/10.1016/B978-0-08-100596-5.22462-1
Buchmann, M. (2016). Integrating stakeholders into the governance of data exchange from smart metering. Competition and Regulation in Network Industries, 17(2), 102-122. https://doi.org/10.1177/178359171601700201
Busse, J. A., Green, T. C., & Baks, K. (2011). Fund Managers Who Take Big Bets: Skilled or Overconfident. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.891727
Cao, M., Vonderembse, M. A., Zhang, Q., & Ragu-Nathan, T. S. (2010). Supply chain collaboration: Conceptualisation and instrument development. International Journal of Production Research, 48(22), 6613-6635. https://doi.org/10.1080/00207540903349039
Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163-180. https://doi.org/10.1016/j.jom.2010.12.008
Chowdhury, P., & Paul, S. K. (2020). Applications of MCDM methods in research on corporate sustainability: a systematic literature review. Management of Environmental Quality: An International Journal, 31(2), 385–405. https://doi.org/10.1108/MEQ-12-2019-0284
Chehbi-Gamoura, S., Derrouiche, R., Damand, D., & Barth, M. (2020). Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Production Planning and Control, 31(5), 355-382. https://doi.org/10.1080/09537287.2019.1639839
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4-39. https://doi.org/10.1080/07421222.2015.1138364
Choi, T. M., & Luo, S. (2019). Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes. Transportation Research Part E: Logistics and Transportation Review, 131, 139-152. https://doi.org/10.1016/j.tre.2019.09.019
Christofferson, F. (2018). Building a cognitive data management strategy (and Why Doing so Is Suddenly so Important). SMPTE Motion Imaging Journal, 127(6), 28-33. https://doi.org/10.5594/JMI.2018.2832578
Colin, M., Galindo, R., & Hernández, O. (2015). Information and communication technology as a key strategy for efficient supply chain management in manufacturing SMEs. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2015.07.152
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. https://doi.org/10.1108/LR-06-2015-0061
Debortoli, S., Müller, O., & Brocke, J. v. (2014). Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-SpezialistenComparing Business Intelligence and Big Data Skills. WIRTSCHAFTSINFORMATIK, 56(5), 315-328. https://doi.org/10.1007/s11576-014-0432-4
Deus, H. F. (2019). Big Semantic Data Processing in the Life Sciences Domain. In S. Sakr & A. Y. Zomaya (Eds.), Encyclopedia of Big Data Technologies (pp. 351-358): Springer,Cham.https://doi.org/10.1007/978-3-319-77525-8_315
Elia, G., Polimeno, G., Solazzo, G., & Passiante, G. (2020). A multi-dimension framework for value creation through Big Data. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2020.03.015
Fawcett, S. E., Ogden, J. A., Magnan, G. M., & Cooper, M. B. (2006). Organizational commitment and governance for supply chain success. International Journal of Physical Distribution and Logistics Management, 36(1), 22-35. https://doi.org/10.1108/09600030610642913
Gandhi, S., Mangla, S. K., Kumar, P., & Kumar, D. (2015). Evaluating factors in implementation of successful green supply chain management using DEMATEL: A case study. International Strategic Management Review, 3(1-2), 96-109. https://doi.org/10.1016/j.ism.2015.05.001
Ghosh, J. (2016). Big data analytics: A field of opportunities for information systems and technology researchers. Journal of Global Information Technology Management, 19(4), 217-222. https://doi.org/10.1080/1097198X.2016.1249667
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. https://doi.org/10.1016/j.jbusres.2016.08.004
Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. https://doi.org/10.1016/j.knosys.2017.01.010
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 and Industrial Engineering, 101, 592-598. https://doi.org/10.1016/j.cie.2016.06.030
Henao, C. A., Muñoz, J. C., & Ferrer, J. C. (2019). Multiskilled workforce management by utilizing closed chains under uncertain demand: A retail industry case. Computers and Industrial Engineering, 127, 74-88. https://doi.org/10.1016/j.cie.2018.11.061
Hofmann, E. (2017). Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108-5126. https://doi.org/10.1080/00207543.2015.1061222
Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: a conceptual analysis. International Journal of Logistics Management, 29(2), 739-766. https://doi.org/10.1108/IJLM-04-2017-0088
Huang, G. Q., Zhong, R. Y., & Tsui, K. L. (2015). Special issue on 'big data for service and manufacturing supply chain management'. International Journal of Production Economics, 165, 172-173. https://doi.org/10.1016/j.ijpe.2015.05.009
Huang, T., & Van Mieghem, J. A. (2014). Clickstream data and inventory management: Model and empirical analysis. Production and Operations Management, 23(3), 333-347. https://doi.org/10.1111/poms.12046
Hult, G. T. M., Ketchen, D. J., & Arrfelt, M. (2007). Strategic supply chain management: Improving performance through a culture of competitiveness and knowledge development. Strategic Management Journal, 28(10), 1035-1052. https://doi.org/10.1002/smj.627
Hung, J. L., He, W., & Shen, J. (2020). Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86, 144-153. https://doi.org/10.1016/j.indmarman.2019.11.001
Hussain, A., & Roy, A. (2016). The emerging era of Big Data Analytics. Big Data Analytics, 1(1). https://doi.org/10.1186/s41044-016-0004-2
Inmon, W. H., & Linstedt, D. (2015). What is Big Data? In Data Architecture: a Primer for the Data Scientist (pp. 49-55): Academic Press.https://doi.org/10.1016/b978-0-12-802044-9.00009-x
Ishwarappa, & Anuradha, J. (2015). A brief introduction on big data 5Vs characteristics and hadoop technology. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2015.04.188
Isik, Ö. (2018). Big Data Capabilities: An Organizational Information Processing Perspective. In A. V. Deokar, A. Gupta, L. S. Iyer, & M. C. Jones (Eds.), Analytics and Data Science (pp. 29-40): Springer,Cham.https://doi.org/10.1007/978-3-319-58097-5_4
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007
Kacfah Emani, C., Cullot, N., & Nicolle, C. (2015). Understandable Big Data: A survey. Computer Science Review, 17, 70-81. https://doi.org/10.1016/j.cosrev.2015.05.002
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 and Production Management, 37(1), 10-36. https://doi.org/10.1108/IJOPM-02-2015-0078
Kaur, P., Sharma, M., & Mittal, M. (2018). Big Data and Machine Learning Based Secure Healthcare Framework. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2018.05.020
Kchaou, H., Kechaou, Z., & Alimi, A. M. (2015). Towards an offloading framework based on big data analytics in mobile cloud computing environments. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2015.07.306
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. International Journal of Logistics Management, 29(2), 676-703. https://doi.org/10.1108/IJLM-06-2017-0153
Lamba, K., & Singh, S. P. (2017). Big data in operations and supply chain management: current trends and future perspectives. Production Planning and Control, 28(11-12), 877-890. https://doi.org/10.1080/09537287.2017.1336787
Lee, J., Bagheri, B., & Kao, H. A. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lezoche, M., Hernandez, J., Diaz, M. D. M. A., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Computers in Industry, 116.
Limaj, E., & Bilali, E. (2018). Big data systems: A renewed definition of the concept. Paper presented at the Proceedings of the 11th IADIS International Conference Information Systems 2018, IS 2018.
Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149-157. https://doi.org/10.1016/j.jsis.2015.08.002
Majiwala, H., Parmar, D., & Gandhi, P. (2019). Leeway of Lean Concept to Optimize Big Data in Manufacturing Industry: An Exploratory Review. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 16, pp. 189-199). Singapore: Springer,Singapore.https://doi.org/10.1007/978-981-10-7641-1_16
Moktadir, M. A., Ali, S. M., Paul, S. K., & Shukla, N. (2019). Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh. Computers and Industrial Engineering, 128, 1063-1075. https://doi.org/10.1016/j.cie.2018.04.013
Mukhametzyanov, I., & Pamu?ar, D. (2018). A Sensitivity analysis in MCDM problems: A statistical approach. Decision Making: Applications in Management and Engineering, 1(2), 1-20. https://doi.org/10.31181/dmame1802050m
Ngai, E. W. T., Chau, D. C. K., & Chan, T. L. A. (2011). Information technology, operational, and management competencies for supply chain agility: Findings from case studies. Journal of Strategic Information Systems, 20(3), 232-249. https://doi.org/10.1016/j.jsis.2010.11.002
Philip Chen, C. L., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. https://doi.org/10.1016/j.ins.2014.01.015
Picciano, A. G. (2012). The evolution of big data and learning analytics in american higher education. Journal of Asynchronous Learning Network, 16(3), 9-20. https://doi.org/10.24059/olj.v16i3.267
Pinto, L., Kaynak, E., Chow, C. S. F., & Zhang, L. L. (2019). Ranking of choice cues for smartphones using the Best–Worst scaling method. Asia Pacific Journal of Marketing and Logistics, 31(1), 223-245. https://doi.org/10.1108/APJML-01-2018-0004
Popovi?, A., Hackney, R., Coelho, P. S., & Jakli?, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729-739. https://doi.org/10.1016/j.dss.2012.08.017
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51-59. https://doi.org/10.1089/big.2013.1508
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1). https://doi.org/10.1186/2047-2501-2-3
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega (United Kingdom), 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rodríguez Fernández, M., Cortés García, A., González Alonso, I., & Zalama Casanova, E. (2016). Using the Big Data generated by the Smart Home to improve energy efficiency management. Energy Efficiency, 9(1), 249-260. https://doi.org/10.1007/s12053-015-9361-3
Rodriguez, L., & Cunha, C. D. (2015). Impacts of big data analytics and absorptive capacity on sustainable supply chain innovation : a conceptual framework. Logforum, 14(2). https://doi.org/10.17270/J.LOG.267
Salamai, A., Hussain, O. K., Saberi, M., Chang, E., & Hussain, F. K. (2019). Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management. IEEE Access, 7, 49297-49315. https://doi.org/10.1109/ACCESS.2019.2902191
Santoro, G., Vrontis, D., Thrassou, A., & Dezi, L. (2018). The Internet of Things: Building a knowledge management system for open innovation and knowledge management capacity. Technological Forecasting and Social Change, 136, 347-354. https://doi.org/10.1016/j.techfore.2017.02.034
Serdarasan, S. (2013). A review of supply chain complexity drivers. Computers and Industrial Engineering, 66(3), 533-540. https://doi.org/10.1016/j.cie.2012.12.008
Sevkli, M., Koh, S. C. L., Zaim, S., Demirbag, M., & Tatoglu, E. (2007). An application of data envelopment analytic hierarchy process for supplier selection: A case study of BEKO in Turkey. International Journal of Production Research, 45(9), 1973-2003. https://doi.org/10.1080/00207540600957399
Shah, N., Irani, Z., & Sharif, A. M. (2017). Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors. Journal of Business Research, 70, 366-378. https://doi.org/10.1016/j.jbusres.2016.08.010
Shamout, M. D. (2019). Does supply chain analytics enhance supply chain innovation and robustness capability? Organizacija, 52(2), 95-106. https://doi.org/10.2478/orga-2019-0007
Simanaviciene, R., & Ustinovichius, L. (2010). Sensitivity analysis for multiple criteria decision making methods: TOPSIS and SAW. Paper presented at the Procedia - Social and Behavioral Sciences.https://doi.org/10.1016/j.sbspro.2010.05.207
Singh, A., & Teng, J. T. C. (2016). Enhancing supply chain outcomes through Information Technology and Trust. Computers in Human Behavior, 54, 290-300. https://doi.org/10.1016/j.chb.2015.07.051
Singh, N. P., & Singh, S. (2019). Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation. Benchmarking: An International Journal, 26(7), 2318-2342. https://doi.org/10.1108/BIJ-10-2018-0346
Somsuk, N. (2014). Prioritizing Drivers of Sustainable Competitive Advantages in Green Supply Chain Management Based on Fuzzy AHP. Journal of Medical and Bioengineering, 259-266. https://doi.org/10.12720/jomb.3.4.259-266
Somsuk, N., & Laosirihongthong, T. (2017). Prioritization of applicable drivers for green supply chain management implementation toward sustainability in Thailand. International Journal of Sustainable Development and World Ecology, 24(2), 175-191. https://doi.org/10.1080/13504509.2016.1187210
Spanaki, K., Gürgüç, Z., Adams, R., & Mulligan, C. (2017). International Journal of Production Research Data supply chain (DSC): research synthesis and future directions Data supply chain (DSC): research synthesis and future directions. International Journal of Production Research, 4447-4466. https://doi.org/10.1080/00207543.2017.1399222
Srivastava, U., & Gopalkrishnan, S. (2015). Impact of big data analytics on banking sector: Learning for Indian Banks. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2015.04.098
Srivathsan, M., & Yogesh, A. K. (2015). Health monitoring system by prognotive computing using big data analytics. Paper presented at the Procedia Computer Science.https://doi.org/10.1016/j.procs.2015.04.092
Tan, K. H., Zhan, Y. Z., 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(12), 223-233. https://doi.org/10.1016/j.ijpe.2014.12.034
Tanino, T. (1999). Sensitivity Analysis in MCDM. In T. Gal, T. J. Stewart, & T. Hanne (Eds.), Multicriteria Decision Making (Vol. 21, pp. 173-201). Boston: Springer,Boston,MA.https://doi.org/10.1007/978-1-4615-5025-9_7
Terrada, L., Bakkoury, J., El Khaili, M., & Khiat, A. (2019). Collaborative and Communicative Logistics Flows Management Using the Internet of Things. Paper presented at the Advances in Intelligent Systems and Computing.https://doi.org/10.1007/978-3-319-91337-7_21
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers and Industrial Engineering, 115, 319-330. https://doi.org/10.1016/j.cie.2017.11.017
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big Data, 2(1). https://doi.org/10.1186/s40537-015-0030-3
Turkulainen, V., Roh, J., Whipple, J. M., & Swink, M. (2017). Managing Internal Supply Chain Integration: Integration Mechanisms and Requirements. Journal of Business Logistics, 38(4). https://doi.org/10.1111/jbl.12165
van den Broek, T., & van Veenstra, A. F. (2018). Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation. Technological Forecasting and Social Change, 129, 330-338. https://doi.org/10.1016/j.techfore.2017.09.040
Veeramani, T., Srinuvasarao, P., Rama Krishna, B., & Thilagavathy, R. (2019). Impact of social media networks big data analysis for high-level business. International Journal of Recent Technology and Engineering, 7(5), 87-92.
Vemula, R., & Zsifkovits, H. (2016). Cloud Computing im Supply Chain ManagementCloud Computing for Supply Chain Management. BHM Berg- und Hüttenmännische Monatshefte, 161(5), 229-232. https://doi.org/10.1007/s00501-016-0485-3
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. https://doi.org/10.1111/jbl.12010
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, G., Gunasekaran, A., Ngai, E. W. T., & 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. https://doi.org/10.1016/j.ijpe.2016.03.014
Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34(1), 1247-1268. https://doi.org/10.17705/1cais.03465
Witkowski, K. (2017). Internet of Things, Big Data, Industry 4.0 - Innovative Solutions in Logistics and Supply Chains Management. Paper presented at the Procedia Engineering.https://doi.org/10.1016/j.proeng.2017.03.197
Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., . . . Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. Communications of the Association for Information Systems, 34(1), 1-13. https://doi.org/10.17705/1CAIS.03401
Wu, K. J., Liao, C. J., Tseng, M. L., Lim, M. K., Hu, J., & Tan, K. (2017). Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142(2), 663-676. https://doi.org/10.1016/j.jclepro.2016.04.040
Yu, V. F., & Hu, K.-J. (2010). An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants. Computers & Industrial Engineering, 58(2), 269-277. https://doi.org/10.1016/j.cie.2009.10.005
Zhan, Y., & Tan, K. H. (2020). An analytic infrastructure for harvesting big data to enhance supply chain performance. European Journal of Operational Research, 281(3), 559-574. https://doi.org/10.1016/j.ejor.2018.09.018
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers and Industrial Engineering, 101, 572-591. https://doi.org/10.1016/j.cie.2016.07.013
Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225. https://doi.org/10.1016/j.rser.2015.11.050

Downloads

Published

2020-09-24 — Updated on 2020-10-27

Versions

How to Cite

Siddique, M. N. A. ., Hasan, K. W. ., Ali, S. M. ., Moktadir, M. A. ., Paul, S. K. ., & Kabir, G. (2020). Modeling drivers to big data analytics in supply chains. Journal of Production Systems and Manufacturing Science, 2(1), 4–25. Retrieved from https://www.imperialopen.com/index.php/JPSMS/article/view/26 (Original work published September 24, 2020)

Issue

Section

Original Research Articles