Production
https://prod.org.br/article/doi/10.1590/0103-6513.20190140
Production
Systematic Review

An overview of big data analytics application in supply chain management published in 2010-2019

Iman Ghalehkhondabi; Ehsan Ahmadi; Reza Maihami

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Abstract

Abstract: Paper aims: This study reviews the available literature regarding big data analytics applications in supply chain management and provides insight on topics that received a good deal of attention and topics that still require investigation. This review considers the expansion of big data analytics in supply chain management from 2010 to 2019.

Originality: Beyond displaying the increasing frequency of using big data analytics in supply chain management, the authors also aim to develop a useful categorization of applying business analytics in supply chain management and define opportunities for future research in the field.

Research method: This paper briefly discusses big data applications in supply chain management. Four common steps in review papers are performed: collecting articles (Thomson Reuters Web of Science), descriptive analysis, defining categories, and evaluating the material.

Main findings: According to both information technology development trends and the availability of data, more companies are using big data analytics in their supply chains. About 60% of the research on big data applications in supply chain management were published after 2017. These publications have increasingly focused on big data applications in predictive analysis, rather than in the other three types of data analysis: descriptive analysis, diagnostic analysis, and prescriptive analysis.

Implications for theory and practice: This review shows that the collected data by many companies can be analyzed using big data analytics methods to develop the business growth plan, market direction forecast, manufacturing process simulation, delivery optimization, inventory management, and marketing and sales processes, among many other activities in a supply chain. The number of articles using case studies in the literature is greater than the number of theoretical publications. This shows that big data analytics has now been properly developed for practical applications, rather than just being a theoretical concept.

Keywords

Big data analytics, Business processes, Manufacturing systems, Logistics systems, Supply chain management

References

Agrahri, H., Ahmed, F., Verma, V. K., & Purohit, J. K. (2017). Benefits of implement big data driven supply chain management: an ISM based model. International Journal of Engineering Science, 7(5), 11426-11431.

Akter, S., & Wamba, S. F. (2019). Big data and disaster management: a systematic review and agenda for future research. Annals of Operations Research, 283(1-2), 939-959. http://dx.doi.org/10.1007/s10479-017-2584-2.

Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2018). Big data initiatives in retail environments: linking service process perceptions to shopping outcomes. Annals of Operations Research, 270(1-2), 25-51. http://dx.doi.org/10.1007/s10479-016-2276-3.

Andersson, J., & Jonsson, P. (2018). Big data in spare parts supply chains. International Journal of Physical Distribution & Logistics Management, 48(5), 524-544. http://dx.doi.org/10.1108/IJPDLM-01-2018-0025.

Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Transportation Research Part E, Logistics and Transportation Review, 114, 416-436. http://dx.doi.org/10.1016/j.tre.2017.04.001.

Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97-114.

Ayed, A. B., Halima, M. B., & Alimi, A. M. (2015). Big data analytics for logistics and transportation. In Proceedings of the 4th International Conference on Advanced Logistics and Transport (ICALT) (pp. 311-316). Piscataway: IEEE.

Badiezadeh, T., Saen, R. F., & Samavati, T. (2018). Assessing sustainability of supply chains by double frontier network DEA: a big data approach. Computers & Operations Research, 98, 284-290. http://dx.doi.org/10.1016/j.cor.2017.06.003.

Barbosa, M. W., Vicente, A. C., Ladeira, M. B., & Oliveira, M. P. V. (2018). Managing supply chain resources with big data analytics: a systematic review. International Journal of Logistics Research and Applications, 21(3), 177-200. http://dx.doi.org/10.1080/13675567.2017.1369501.

Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains: a resource-based theory perspective. Journal of Operations Management, 25(6), 1217-1233. http://dx.doi.org/10.1016/j.jom.2007.01.003.

Belhadi, A., Zkik, K., Cherrafi, A., & Yusof, M. (2019). Understanding the capabilities of big data analytics for manufacturing process: insights from literature review and multiple case study. Computers & Industrial Engineering, 137, 106099. http://dx.doi.org/10.1016/j.cie.2019.106099.

Benhenni, A. L. (2017). Pragmatic big data and smart manufacturing. In Proceedings of the 18th International Congress of Metrology. France: EDP Sciences. http://dx.doi.org/10.1051/metrology/201709002.

Biswas, S., & Sen, J. (2016). A proposed framework of next generation supply chain management using big data analytics. In Proceedings of the National Conference on Emerging Trends in Business and Management: Issues and Challenges. Rourkela: National Institute of Technology Rourkela.

Boone, C. A., Skipper, J. B., & Hazen, B. T. (2017). A framework for investigating the role of big data in service parts management. Journal of Cleaner Production, 153, 687-691. http://dx.doi.org/10.1016/j.jclepro.2016.09.201.

Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. http://dx.doi.org/10.1016/j.ijforecast.2018.09.003.

Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. The Journal of Supply Chain Management, 50(3), 55-73. http://dx.doi.org/10.1111/jscm.12050.

Briggs, E., Landry, T. D., & Daugherty, P. J. (2010). Investigating the influence of velocity performance on satisfaction with third party logistics service. Industrial Marketing Management, 39(4), 640-649. http://dx.doi.org/10.1016/j.indmarman.2009.06.004.

Brinch, M. (2018). Understanding the value of big data in supply chain management and its business processes. International Journal of Operations & Production Management, 38(7), 1589-1614. http://dx.doi.org/10.1108/IJOPM-05-2017-0268.

Brinch, M., Stentoft, J., Jensen, J. K., & Rajkumar, C. (2018). Practitioners understanding of big data and its applications in supply chain management. International Journal of Logistics Management, 29(2), 555-574. http://dx.doi.org/10.1108/IJLM-05-2017-0115.

Bumblauskas, D., Gemmill, D., Igou, A., & Anzengruber, J. (2017a). Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics. Expert Systems with Applications, 90, 303-317. http://dx.doi.org/10.1016/j.eswa.2017.08.025.

Bumblauskas, D., Nold, H., Bumblauskas, P., & Igou, A. (2017b). Big data analytics: transforming data to action. Business Process Management Journal, 23(3), 703-720. http://dx.doi.org/10.1108/BPMJ-03-2016-0056.

Carillo, K. D. A. (2017). Let’s stop trying to be “sexy”: preparing managers for the (big) data-driven business era. Business Process Management Journal, 23(3), 598-622. http://dx.doi.org/10.1108/BPMJ-09-2016-0188.

Chaudhuri, A., Dukovska-Popovska, I., Chan, H. K., Subramanian, N., Bai, R., & Pawar, K. S. (2016). Development of a framework for big data analytics in cold chain logistics. In Proceedings of 21st International Symposium on Logistics (ISL 2016): Sustainable Transport and Supply Chain Innovation. Nottingham: Centre for Concurrent Enterprise, Nottingham University Business School.

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. http://dx.doi.org/10.1080/07421222.2015.1138364.

Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big data: related technologies, challenges and future prospects. Cham: Springer International Publishing.

Cheng, Y., Kuang, Y., Shi, X., & Dong, C. (2018). Sustainable investment in a supply chain in the big data era: an information updating approach. Sustainability, 10(2), 403. http://dx.doi.org/10.3390/su10020403.

Choi, T.-M. (2018). Incorporating social media observations and bounded rationality into fashion quick response supply chains in the big data era. Transportation Research Part E, Logistics and Transportation Review, 114, 386-397. http://dx.doi.org/10.1016/j.tre.2016.11.006.

Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883. http://dx.doi.org/10.1111/poms.12838.

Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In C. Boersch & R. Elschen (Eds.), Das summa summarum des management (pp. 265-275). Wiesbaden: Springer. http://dx.doi.org/10.1007/978-3-8349-9320-5_22.

Clarivate. (2020). CPCI: Clarivate analytics. Retrieved in 2020, March 25, from http://wokinfo.com/products_tools/multidisciplinary/webofscience/cpci/?parentKey=555184,539593

Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: a challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79-96. http://dx.doi.org/10.1093/aepp/ppx056.

Cochran, D. S., Kinard, D., & Bi, Z. (2016). Manufacturing system design meets big data analytics for continuous improvement. Procedia CIRP, 50, 647-652. http://dx.doi.org/10.1016/j.procir.2016.05.004.

Costello, T., & Prohaska, B. (2013). Trends and strategies. IT Professional, 15(1), 64. http://dx.doi.org/10.1109/MITP.2013.5.

Dai, Q., Zhong, R., Huang, G. Q., Qu, T., Zhang, T., & Luo, T. Y. (2012). Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer. International Journal of Computer Integrated Manufacturing, 25(1), 51-65. http://dx.doi.org/10.1080/0951192X.2011.562546.

Deleris, L. A., Elkins, D., & Paté-Cornell, M. E. (2004). Analyzing losses from hazard exposure: a conservative probabilistic estimate using supply chain risk simulation. In Proceedings of the 2004 Winter Simulation Conference (pp. 1384-1391). Piscataway: IEEE. http://dx.doi.org/10.1109/WSC.2004.1371476.

Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agility. Management Decision, 57(8), 2092-2112. http://dx.doi.org/10.1108/MD-01-2018-0119.

Dubey, R., Gunasekaran, A., Childe, S. J., Luo, Z., Wamba, S. F., Roubaud, D., & Foropon, C. (2018a). Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. Journal of Cleaner Production, 196, 1508-1521. http://dx.doi.org/10.1016/j.jclepro.2018.06.097.

Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. (2018b). Big data and predictive analytics in humanitarian supply chains. International Journal of Logistics Management, 29(2), 485-512. http://dx.doi.org/10.1108/IJLM-02-2017-0039.

Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019a). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change, 144, 534-545. http://dx.doi.org/10.1016/j.techfore.2017.06.020.

Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019b). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136. http://dx.doi.org/10.1016/j.ijpe.2019.01.023.

Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. International Journal of Advanced Manufacturing Technology, 84(1-4), 631-645. http://dx.doi.org/10.1007/s00170-015-7674-1.

Dutta, D., & Bose, I. (2015). Managing a big data project: the case of ramco cements limited. International Journal of Production Economics, 165, 293-306. http://dx.doi.org/10.1016/j.ijpe.2014.12.032.

El-Kassar, A.-N., & Singh, S. K. (2019). Green innovation and organizational performance: the influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change, 144, 483-498. http://dx.doi.org/10.1016/j.techfore.2017.12.016.

Engelseth, P., & Wang, H. (2018). Big data and connectivity in long-linked supply chains. Journal of Business and Industrial Marketing, 33(8), 1201-1208. http://dx.doi.org/10.1108/JBIM-07-2017-0168.

Feng, Q., & Shanthikumar, J. G. (2018). How research in production and operations management may evolve in the era of big data. Production and Operations Management, 27(9), 1670-1684. http://dx.doi.org/10.1111/poms.12836.

Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. Interaction, 19(3), 50-59. http://dx.doi.org/10.1145/2168931.2168943.

Gawankar, S. A., Gunasekaran, A., & Kamble, S. (2020). A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context. International Journal of Production Research, 58(5), 1574-1593. http://dx.doi.org/10.1080/00207543.2019.1668070.

Giagnocavo, C., Bienvenido, F., Ming, L., Yurong, Z., Antonio Sanchez-Molina, J., & Xinting, Y. (2017). Agricultural cooperatives and the role of organisational models in new intelligent traceability systems and big data analysis. International Journal of Agricultural and Biological Engineering, 10(5), 115-125. http://dx.doi.org/10.25165/j.ijabe.20171005.3089.

Giannakis, M., & Louis, M. (2016). A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), 706-727. http://dx.doi.org/10.1108/JEIM-06-2015-0050.

Gobble, M. M. (2013). Big data: the next big thing in innovation. Research Technology Management, 56(1), 64-67. http://dx.doi.org/10.5437/08956308X5601005.

Guha, S., & Kumar, S. (2018). Emergence of big data research in operations management, information systems, and healthcare: Past contributions and future roadmap. Production and Operations Management, 27(9), 1724-1735. http://dx.doi.org/10.1111/poms.12833.

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. http://dx.doi.org/10.1016/j.jbusres.2016.08.004.

Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile manufacturing practices: the role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1-2), 385-397. http://dx.doi.org/10.1080/00207543.2017.1395488.

Guo, L., Sharma, R., Yin, L., Lu, R., & Rong, K. (2017). Automated competitor analysis using big data analytics. Business Process Management Journal, 23(3), 735-762. http://dx.doi.org/10.1108/BPMJ-05-2015-0065.

Gupta, S., Altay, N., & Luo, Z. (2019a). Big data in humanitarian supply chain management: a review and further research directions. Annals of Operations Research, 283(1), 1153-1173. http://dx.doi.org/10.1007/s10479-017-2671-4.

Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Santibañez Gonzalez, E. D. R. (2019b). Circular economy and big data analytics: a stakeholder perspective. Technological Forecasting and Social Change, 144, 466-474. http://dx.doi.org/10.1016/j.techfore.2018.06.030.

Gupta, S., Qian, X., Bhushan, B., & Luo, Z. (2019c). Role of cloud ERP and big data on firm performance: a dynamic capability view theory perspective. Management Decision, 57(8), 1857-1882. http://dx.doi.org/10.1108/MD-06-2018-0633.

Gupta, S., Modgil, S., & Gunasekaran, A. (2020). Big data in lean six sigma: a review and further research directions. International Journal of Production Research, 58(3), 947-969. http://dx.doi.org/10.1080/00207543.2019.1598599.

Hanumanthappa, M., & Sarakutty, T. (2011). Predicting the future of car manufacturing industry using data mining techniques. International Journal of Information Technology, 1(1), 27-29.

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. http://dx.doi.org/10.1016/j.ijpe.2014.04.018.

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. http://dx.doi.org/10.1080/00207543.2015.1061222.

Hopkins, J., & Hawking, P. (2018). Big data analytics and IoT in logistics: a case study. International Journal of Logistics Management, 29(2), 575-591. http://dx.doi.org/10.1108/IJLM-05-2017-0109.

Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward scalable systems for big data analytics: a technology tutorial. IEEE Access: Practical Innovations, Open Solutions, 2, 652-687. http://dx.doi.org/10.1109/ACCESS.2014.2332453.

Huang, L., Wu, C., & Wang, B. (2019). Challenges, opportunities and paradigm of applying big data to production safety management: From a theoretical perspective. Journal of Cleaner Production, 231, 592-599. http://dx.doi.org/10.1016/j.jclepro.2019.05.245.

Iannone, F. (2012). The private and social cost efficiency of port hinterland container distribution through a regional logistics system. Transportation Research Part A, Policy and Practice, 46(9), 1424-1448. http://dx.doi.org/10.1016/j.tra.2012.05.019.

Idc-Vesset, D., Woo, B., Morris, H., Villars, R., Little, G., Bozman, J. S., Borovick, L., Olofson, C. W., Feldman, S., & Conway, S. (2012). Market analysis-worldwide big data technology and services 2012-2015 forecast. IDC Analyze the Future, 1, 1-34.

Irani, Z., Sharif, A. M., Lee, H., Aktas, E., Topaloğlu, Z., van’t Wout, T., & Huda, S. (2018). Managing food security through food waste and loss: Small data to big data. Computers & Operations Research, 98, 367-383. http://dx.doi.org/10.1016/j.cor.2017.10.007.

Ittmann, H. W. (2015). The impact of big data and business analytics on supply chain management. Journal of Transport and Supply Chain Management, 9(1), 1-9. http://dx.doi.org/10.4102/jtscm.v9i1.165.

Jagtap, S., & Duong, L. N. K. (2019). Improving the new product development using big data: a case study of a food company. British Food Journal, 121(11), 2835-2848. http://dx.doi.org/10.1108/BFJ-02-2019-0097.

Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. International Journal of Logistics Management, 29(2), 513-538. http://dx.doi.org/10.1108/IJLM-05-2017-0134.

Jha, M., Jha, S., & O’Brien, L. (2016). Combining big data analytics with business process using reengineering. In Proceedings of the 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS) (pp. 1-6). Piscataway: IEEE. http://dx.doi.org/10.1109/RCIS.2016.7549307.

Ji, G., & Tan, K. (2017). A big data decision-making mechanism for food supply chain. In Proceedings of the 13th Global Congress on Manufacturing and Management (GCMM 2016) (Vol. 100). France: EDP Sciences.

Ji, S., & Sun, Q. (2017). Low-carbon planning and design in B&R logistics service: a case study of an e-commerce big data platform in China. Sustainability, 9(11), 2052. http://dx.doi.org/10.3390/su9112052.

Jin, D.-H., & Kim, H.-J. (2018). Integrated understanding of big data, big data analysis, and business intelligence: a case study of logistics. Sustainability, 10(10), 3778. http://dx.doi.org/10.3390/su10103778.

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 & Production Management, 37(1), 10-36. http://dx.doi.org/10.1108/IJOPM-02-2015-0078.

Kaur, H., & Singh, S. P. (2018). Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Computers & Operations Research, 98, 301-321. http://dx.doi.org/10.1016/j.cor.2017.05.008.

Kshetri, N. (2014). Big data׳ s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145. http://dx.doi.org/10.1016/j.telpol.2014.10.002.

Kumar, A., Shankar, R., Choudhary, A., & Thakur, L. S. (2016). A big data MapReduce framework for fault diagnosis in cloud-based manufacturing. International Journal of Production Research, 54(23), 7060-7073. http://dx.doi.org/10.1080/00207543.2016.1153166.

Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23-25. http://dx.doi.org/10.1038/544023a. PMid:28383012.

Kynast, M., & Marjanovic, O. (2016). Big Data in supply chain management-applications, challenges and benefits. In Proceedings of the 22nd Americas Conference on Information Systems. San Diego: AMCIS.

Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management. International Journal of Logistics Management, 29(2), 676-703. http://dx.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. http://dx.doi.org/10.1080/09537287.2017.1336787.

Lamba, K., & Singh, S. P. (2019). Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment. Technological Forecasting and Social Change, 144, 573-584. http://dx.doi.org/10.1016/j.techfore.2018.03.020.

Lamba, K., Singh, S. P., & Mishra, N. (2019). Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in big data environment. Computers & Industrial Engineering, 128, 1052-1062. http://dx.doi.org/10.1016/j.cie.2018.04.028.

Laney, D. (2001a). Big 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(71), 1.

Laney, D. (2001b). Application delivery strategies. Stamford: META Group.

Lau, R. Y. K., Zhang, W., & Xu, W. (2018). Parallel aspect‐oriented sentiment analysis for sales forecasting with big data. Production and Operations Management, 27(10), 1775-1794. http://dx.doi.org/10.1111/poms.12737.

Lee, C. K. H. (2017). A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0. International Journal of Production Research, 55(2), 593-605. http://dx.doi.org/10.1080/00207543.2016.1221162.

Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41. http://dx.doi.org/10.1016/j.mfglet.2013.09.005.

Li, B., Ch’ng, E., Chong, A. Y.-L., & Bao, H. (2016). Predicting online e-marketplace sales performances: a big data approach. Computers & Industrial Engineering, 101, 565-571. http://dx.doi.org/10.1016/j.cie.2016.08.009.

Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using Big Data. Annals of Operations Research, 268(1-2), 113-128. http://dx.doi.org/10.1007/s10479-016-2342-x.

Li, S., Peng, G. C., & Xing, F. (2019). Barriers of embedding big data solutions in smart factories: insights from SAP consultants. Industrial Management & Data Systems, 119(5), 1147-1164. http://dx.doi.org/10.1108/IMDS-11-2018-0532.

Lin, C. (2016). Exploring big data capability: drivers and impact on supply chain performance. Toledo, OH: University of Toledo.

Liu, C., Li, H., Tang, Y., Lin, D., & Liu, J. (2019). Next generation integrated smart manufacturing based on big data analytics, reinforced learning, and optimal routes planning methods. International Journal of Computer Integrated Manufacturing, 32(9), 820-831. http://dx.doi.org/10.1080/0951192X.2019.1636412.

Liu, P. (2017). Pricing strategies of a three-stage supply chain: a new research in the big data era. Discrete Dynamics in Nature and Society, 2017, 2017. http://dx.doi.org/10.1155/2017/9024712.

Liu, P. (2019). Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. Journal of Cleaner Production, 210, 343-357. http://dx.doi.org/10.1016/j.jclepro.2018.10.328.

Liu, P., & Yi, S. (2016). Investment decision-making and coordination of supply chain: a new research in the big data era. Discrete Dynamics in Nature and Society, 2016, 2016. http://dx.doi.org/10.1155/2016/2026715.

Liu, P., & Yi, S. (2017). Pricing policies of green supply chain considering targeted advertising and product green degree in the big data environment. Journal of Cleaner Production, 164, 1614-1622. http://dx.doi.org/10.1016/j.jclepro.2017.07.049.

Liu, Y.-P., Guo, J.-F., & Fan, Y. (2017). A big data study on emitting companies’ performance in the first two phases of the European Union Emission Trading Scheme. Journal of Cleaner Production, 142, 1028-1043. http://dx.doi.org/10.1016/j.jclepro.2016.05.121.

Mandal, S. (2019). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility. Information Technology & People, 32(2), 297-318. http://dx.doi.org/10.1108/ITP-11-2017-0386.

Mani, V., Delgado, C., Hazen, B. T., & Patel, P. (2017). Mitigating supply chain risk via sustainability using big data analytics: evidence from the manufacturing supply chain. Sustainability, 9(4), 608. http://dx.doi.org/10.3390/su9040608.

Mashey, J. R. (1997). Big data... and the next wave of infrastress. In Proceedings of the Computer Science Division Seminar. Berkeley: University of California.

Mehmood, R., & Graham, G. (2015). Big data logistics: a health-care transport capacity sharing model. Procedia Computer Science, 64, 1107-1114. http://dx.doi.org/10.1016/j.procs.2015.08.566.

Mikavicaa, B., Kostić-Ljubisavljevića, A., & Radonjić, V. (2015). Big data: challenges and opportunities in logistics systems. In Proceedings of the 2nd Logistics International Conference (pp. 185-190). Belgrade, Serbia: LOGIC.

Militaru, G., Pollifroni, M., & Ioanid, A. (2015). Big data in supply chain management: an exploratory study. Network Intelligence Studies, (6), 103-108.

Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018). Big data and supply chain management: a review and bibliometric analysis. Annals of Operations Research, 270(1-2), 313-336. http://dx.doi.org/10.1007/s10479-016-2236-y.

Mishra, N., Singh, A., Rana, N. P., & Dwivedi, Y. K. (2017). Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique. Production Planning and Control, 28(11-12), 945-963. http://dx.doi.org/10.1080/09537287.2017.1336789.

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 & Industrial Engineering, 128, 1063-1075. http://dx.doi.org/10.1016/j.cie.2018.04.013.

Mourtzis, D., Vlachou, E., & Milas, N. (2016). industrial big data as a result of IoT adoption in manufacturing. Procedia Cirp, 55, 290-295. http://dx.doi.org/10.1016/j.procir.2016.07.038.

Nedelcu, B. (2013). About big data and its challenges and benefits in manufacturing. Database Systems Journal, 4(3), 10-19.

Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: a state-of-the-art literature review. Computers & Operations Research, 98, 254-264. http://dx.doi.org/10.1016/j.cor.2017.07.004.

Niebel, T., Rasel, F., & Viete, S. (2019). BIG data-BIG gains? Understanding the link between big data analytics and innovation. Economics of Innovation and New Technology, 28(3), 296-316. http://dx.doi.org/10.1080/10438599.2018.1493075.

Niu, B., & Zou, Z. (2017). Better demand signal, better decisions? Evaluation of big data in a licensed remanufacturing supply chain with environmental risk considerations. Risk Analysis, 37(8), 1550-1565. http://dx.doi.org/10.1111/risa.12796. PMid:28370119.

Niu, B., Dai, Z., & Zhuo, X. (2019). Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions. Transportation Research Part E, Logistics and Transportation Review, 123, 29-44. http://dx.doi.org/10.1016/j.tre.2019.01.011.

O’Donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. (2015). An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, 2(1), 25. http://dx.doi.org/10.1186/s40537-015-0034-z.

Oncioiu, I., Bunget, O. C., Türkeș, M. C., Căpușneanu, S., Topor, D. I., Tamaș, A. S., Rakoș, I.-S., & Hint, M. (2019). The impact of big data analytics on company performance in supply chain management. Sustainability, 11(18), 4864. http://dx.doi.org/10.3390/su11184864.

Opresnik, D., & Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165, 174-184. http://dx.doi.org/10.1016/j.ijpe.2014.12.036.

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. http://dx.doi.org/10.1016/j.jclepro.2016.03.059.

Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20(2), 209-222. http://dx.doi.org/10.1007/s10796-016-9720-4.

Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: a resource dependence perspective. Annals of Operations Research, 270(1-2), 383-413. http://dx.doi.org/10.1007/s10479-016-2280-7.

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24. http://dx.doi.org/10.1016/j.jclepro.2019.03.181.

Rehman, M. H., Chang, V., Batool, A., & Wah, T. Y. (2016). Big data reduction framework for value creation in sustainable enterprises. International Journal of Information Management, 36(6), 917-928. http://dx.doi.org/10.1016/j.ijinfomgt.2016.05.013.

Reinsel, D., Gantz, J., & Rydning, J. (2018). The digitization of the world from edge to core (IDC White Paper). Framingham, MA: IDC.

Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. Journal of Cleaner Production, 210, 1343-1365. http://dx.doi.org/10.1016/j.jclepro.2018.11.025.

Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019). Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. Management Decision, 57(8), 2052-2068. http://dx.doi.org/10.1108/MD-07-2018-0821.

Richey Junior, R. G., Morgan, T. R., Lindsey-Hall, K., & Adams, F. G. (2016). A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710-739. http://dx.doi.org/10.1108/IJPDLM-05-2016-0134.

Robak, S., Franczyk, B., & Robak, M. (2014). Research problems associated with big data utilization in logistics and supply chains design and management. Annals of Computer Science and Information Systems, 3, 245-249. http://dx.doi.org/10.15439/2014F472.

Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018). The future and social impact of big data analytics in supply chain management: results from a Delphi study. Technological Forecasting and Social Change, 130, 135-149. http://dx.doi.org/10.1016/j.techfore.2017.10.005.

Sagaert, Y. R., Aghezzaf, E.-H., Kourentzes, N., & Desmet, B. (2018). Temporal big data for tactical sales forecasting in the tire industry. Interfaces, 48(2), 121-129. http://dx.doi.org/10.1287/inte.2017.0901.

Sanders, N. R. (2014). Big data driven supply chain management: a framework for implementing analytics and turning information into intelligence. New Jersey: Pearson Education.

Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48. http://dx.doi.org/10.1525/cmr.2016.58.3.26.

Santos, M. Y., Oliveira e Sá, J., Andrade, C., Vale Lima, F., Costa, E., Costa, C., Martinho, B., & Galvão, J. (2017). A big data system supporting bosch braga industry 4.0 strategy. International Journal of Information Management, 37(6), 750-760. http://dx.doi.org/10.1016/j.ijinfomgt.2017.07.012.

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. http://dx.doi.org/10.1111/jbl.12082.

Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: the real-world use of big data. IBM Global Business Services, 12, 1-20.

Schwab, K., Marcus, A., Oyola, J. O., Hoffman, W., & Luzi, M. (2011). Personal data: the emergence of a new asset class: an initiative of the World Economic Forum. Geneva: World Economic Forum.

Seles, B. M. R. P., Sousa Jabbour, A. B. L., Jabbour, C. J. C., Camargo Fiorini, P., Mohd-Yusoff, Y., & Thomé, A. M. T. (2018). Business opportunities and challenges as the two sides of the climate change: Corporate responses and potential implications for big data management towards a low carbon society. Journal of Cleaner Production, 189, 763-774. http://dx.doi.org/10.1016/j.jclepro.2018.04.113.

Shang, Y., Dunson, D., & Song, J.-S. (2017). Exploiting big data in logistics risk assessment via bayesian nonparametrics. Operations Research, 65(6), 1574-1588. http://dx.doi.org/10.1287/opre.2017.1612.

Shen, B., Choi, T.-M., & Chan, H.-L. (2019). Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the big data era. Technological Forecasting and Social Change, 144, 412-420. http://dx.doi.org/10.1016/j.techfore.2017.09.003.

Shukla, M., & Mattar, L. (2019). Next generation smart sustainable auditing systems using big data analytics: understanding the interaction of critical barriers. Computers & Industrial Engineering, 128, 1015-1026. http://dx.doi.org/10.1016/j.cie.2018.04.055.

Shukla, M., & Tiwari, M. K. (2017). Big-data analytics framework for incorporating smallholders in sustainable palm oil production. Production Planning and Control, 28(16), 1365-1377. http://dx.doi.org/10.1080/09537287.2017.1375145.

Singh, S. K., & El-Kassar, A.-N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273. http://dx.doi.org/10.1016/j.jclepro.2018.12.199.

Sonra. (2015, June 15). Data warehousing in the age of big data: RDBMS scalability, exploding data volumes and license costs. Retrieved in 2020, March 25, from https://sonra.io/2015/06/15/data-warehousing-in-the-age-of-big-data-rdbms-scalability-exploding-data-volumes-and-license-costs/

StatSlice. (2013). Hadoop business case: a cost effective queryable data archive/storage platform. Retrieved in 2020, March 25, from http://www.statslice.com/hadoop-business-case-a-cost-effective-queryable-data-archivestorage-platform

Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2), 288-297. http://dx.doi.org/10.1016/j.ijpe.2008.09.002.

Swaminathan, S. (2012). The effects of big data on the logistics industry. Redwood City: Profit Oracle.

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. http://dx.doi.org/10.1016/j.ijpe.2014.12.034.

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576. http://dx.doi.org/10.1007/s00170-017-0233-1.

Terziovski, M. (2010). Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view. Strategic Management Journal, 31(8), 892-902. http://dx.doi.org/10.1002/smj.841.

Tsao, Y.-C. (2017). Managing default risk under trade credit: Who should implement Big-Data analytics in supply chains? Transportation Research Part E, Logistics and Transportation Review, 106, 276-293. http://dx.doi.org/10.1016/j.tre.2017.08.013.

Van Asselt, E. D., van der Fels‐Klerx, H. J., Marvin, H. J. P., Van Bokhorst‐van de Veen, H., & Groot, M. N. (2017). Overview of food safety hazards in the European dairy supply chain. Comprehensive Reviews in Food Science and Food Safety, 16(1), 59-75. http://dx.doi.org/10.1111/1541-4337.12245.

Van der Aalst, W. M. (2012). A decade of business process management conferences: personal reflections on a developing discipline. In Proceedings of the International Conference on Business Process Management (pp. 1-16). Berlin: Springer. http://dx.doi.org/10.1007/978-3-642-32885-5_1.

Vera-Baquero, A., Colomo Palacios, R., Stantchev, V., & Molloy, O. (2015). Leveraging big-data for business process analytics. The Learning Organization, 22(4), 215-228. http://dx.doi.org/10.1108/TLO-05-2014-0023.

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. http://dx.doi.org/10.1111/jbl.12010.

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. http://dx.doi.org/10.1016/j.ijpe.2014.12.031.

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. http://dx.doi.org/10.1016/j.ijpe.2016.03.014.

Weerakkody, V., Kapoor, K., Balta, M. E., Irani, Z., & Dwivedi, Y. K. (2017). Factors influencing user acceptance of public sector big open data. Production Planning and Control, 28(11-12), 891-905. http://dx.doi.org/10.1080/09537287.2017.1336802.

Weng, W.-H., & Weng, W.-T. (2013). Forecast of development trends in big data industry. In Proceedings of the Institute of Industrial Engineers Asian Conference 2013 (pp. 1487-1494). Singapore: Springer. http://dx.doi.org/10.1007/978-981-4451-98-7_174.

Witkowski, K. (2017). Internet of things, big data, industry 4.0-: nnovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763-769. http://dx.doi.org/10.1016/j.proeng.2017.03.197.

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming: a review. Agricultural Systems, 153, 69-80. http://dx.doi.org/10.1016/j.agsy.2017.01.023.

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, 663-676. http://dx.doi.org/10.1016/j.jclepro.2016.04.040.

Wu, P.-J., & Lin, K.-C. (2018). Unstructured big data analytics for retrieving e-commerce logistics knowledge. Telematics and Informatics, 35(1), 237-244. http://dx.doi.org/10.1016/j.tele.2017.11.004.

Xu, F., Li, Y., & Feng, L. (2019). The influence of big data system for used product management on manufacturing: remanufacturing operations. Journal of Cleaner Production, 209, 782-794. http://dx.doi.org/10.1016/j.jclepro.2018.10.240.

Xu, L. (2016). Construction mode of efficient logistics system under the big data environment. Advanced Science and Technology Letters, 138, 150-155. http://dx.doi.org/10.14257/astl.2016.138.31.

Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies’ performance: an integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199-210. http://dx.doi.org/10.1016/j.techfore.2018.07.043.

Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213-223. http://dx.doi.org/10.1016/j.ijforecast.2017.11.005.

Zaki, M., Theodoulidis, B., Shapira, P., Neely, A., & Tepel, M. F. (2019). Redistributed manufacturing and the impact of big data: a consumer goods perspective. Production Planning and Control, 30(7), 568-581. http://dx.doi.org/10.1080/09537287.2018.1540068.

Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2018). Unlocking the power of big data in new product development. Annals of Operations Research, 270(1-2), 577-595. http://dx.doi.org/10.1007/s10479-016-2379-x.

Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626-641. http://dx.doi.org/10.1016/j.jclepro.2016.07.123.

Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097. http://dx.doi.org/10.1016/j.jclepro.2016.03.006.

Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260-272. http://dx.doi.org/10.1016/j.ijpe.2015.02.014.

Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 2610-2621. http://dx.doi.org/10.1080/00207543.2015.1086037.
 

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