The amount of data produced by different sectors in past few years has seen an exponentially increase. As a result, professionals are struggling to process vital data in this new competitive environment. The main purpose of this research study is to understand, explore and provide important insights in the big data analytics (BDA) implementation in different functions of logistics and supply chain management (L&SCM) by conducting a bibliometric analysis and future review of relevant articles to write a literature review on trends and implementation of BDA. The study performs a literature review based on the understanding of bibliometric analysis to further state the review on application and opportunity BDA in logistics management and supply chain management (SCM). The motive of this research study aims to find the advantages of adopting BDA, study the inadequate research findings which was not mentioned in previous research papers in this field. Further the literature review categorizes the trends and implementation of research topic in nine main functions of logistics as well as SCM. This research study will help the practitioners and academia with insightful observation on research finding, applying and incorporating BDA for managers and supply chain (SC) professional to take decision whether use of analytics will help to improve SC and is in vesting in BDA a good decision. Also, the article indicates the scope of future study by highlighting the trends in use of analytics and how this implementation in various functions of SC can be further studied based upon the research gap and future direction. This article is unique in terms on conducting bibliometric study since the evolution of big data concept, analyze the trends, interpret and further with the process of literature review showcase a theory regarding the use of the analytics technology in logistics and SC domain.
AmrM. A., EljazzarM. M., KassemS. S., & EzzatM. (2019). Merging supply chain and blockchain technologies [Paper presentation]. Managing Technology for Inclusive and Sustainable Growth—28th International Conference for the International Association of Management of Technology, 224–228.
2.
AriasM. B., & BaeS. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied Energy, 183, 327–339. 10.1016/j.apenergy.2016.08.080
3.
AshrafiA., Zare RavasanA., TrkmanP., & AfshariS. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47(January), 1–15. 10.1016/j.ijinfomgt.2018.12.005
4.
BalarA., MalviyaN., PrasadS., & GangurdeA. (2013). Forecasting consumer behavior with innovative value proposition for organizations using big data analytics [Paper presentation]. 2013 IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2013. 10.1109/ICCIC.2013.6724280
5.
Ben AyedA., Ben HalimaM., & AlimiA. M. (2015, May). Big data analytics for logistics and transportation [Paper presentation]. 4th IEEE International Conference on Advanced Logistics and Transport, IEEE ICALT 2015, 311–316. 10.1109/ICAdLT.2015.7136630
6.
Ben-DayaM., HassiniE., & BahrounZ. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742. 10.1080/00207543.2017.1402140
7.
BoehmkeB., HazenB., BooneC. A., & RobinsonJ. L. (2020). A data science and open source software approach to analytics for strategic sourcing. International Journal of Information Management, 54(July), 102167. 10.1016/j.ijinfomgt.2020.102167
8.
BrouerB. D., KarstenC. V., & PisingerD. (2016). Big data optimization in maritime logistics. In EmrouznejadA. (Ed.), Big data optimization: Recent developments and challenges (pp. 319–344). Springer. 10.1007/978-3-319-30265-2_14
9.
ChenH., ChiangR. H. L., & StoreyV. C. (2018). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. http://www.jstor.org/stable/41703503
10.
CuzzocreaA. (2014). Privacy and security of big data [Paper presentation]. 1st International Workshop on Privacy and Secuirty of Big Data, 45–47. 10.1145/2663715.2669614
11.
DonthuN., KumarS., MukherjeeD., PandeyN., & LimW. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(March), 285–296. 10.1016/j.jbusres.2021.04.070
12.
DouglasK. M., & SuttonR. M. (2010). Kent Academic Repository. European Journal of Social Psychology, 40(2), 366–374. 10.1080/00369220601100075
13.
DuttaP., ChoiT. M., SomaniS., & ButalaR. (2020). Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation Research Part E: Logistics and Transportation Review, 142(August), 102067. 10.1016/j.tre.2020.102067
14.
FanY., HeiligL., & VoßS. (2015). Supply chain risk management in the era of big data. In MarcusA. (Ed.), Design, user experience, and usability: Design discourse (Vol. 9186, pp. 283–294). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-319-20886-2_27
15.
GalvagnoM. (2017). Bibliometric literature review: An opportunity for marketing scholars. Mercati & Competitività, 4, 7–15. 10.3280/mc2017-004001
16.
GarfieldE. (1964). ‘Science citation index’—A new dimension in indexing. Science, 144(3619), 649–654. 10.1126/science.144.3619.649
GhoshalA., LarsonE. C., SubramanyamR., & ShawM. J. (2014). The impact of business analytics strategy on social, mobile, and cloud computing adoption [Paper presentation]. 35th International Conference on Information Systems ‘Building a Better World Through Information Systems’, 1–11.
19.
GiannakisM., & LouisM. (2016). A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), 706–727. 10.1108/JEIM-06-2015-0050
20.
GunasekaraH. G., SridarranP., & RajaratnamD. (2021). Effective use of blockchain technology for facilities management procurement process. Journal of Facilities Management. 10.1108/JFM-10-2020-0077
21.
HaddudA., DeSouzaA., KhareA., & LeeH. (2017). Examining potential benefits and challenges associated with the Internet of Things integration in supply chains. Journal of Manufacturing Technology Management, 28(8), 1055–1085. 10.1108/JMTM-05-2017-0094
22.
HassaniH., & SilvaE. S. (2015). Forecasting with big data: A review. Annals of Data Science, 2(1), 5–19. 10.1007/s40745-015-0029-9
23.
HazenB. T., SkipperJ. B., EzellJ. D., & BooneC. A. (2016). Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers and Industrial Engineering, 101, 592–598. 10.1016/j.cie.2016.06.030
24.
IcsG. Y. N.MA.. (2014). The theory of horizontal and vertical ratios on the applicability: Comparison of earthquakes and micro-movements. Hiroshi Kawase 1), Shinichi Matsushima 2), Nagashima Shiming 3) 1), 11(7), 1056–1063.
25.
InamdarZ., RautR., NarwaneV. S., GardasB., NarkhedeB., & SagnakM. (2020). A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. Journal of Enterprise Information Management, 34(1), 101–139. 10.1108/JEIM-09-2019-0267
26.
IssaouiY., KhiatA., BahnasseA., & OuajjiH. (2020). SMART logistics: Blockchain trends and applications. Journal of Ubiquitous Systems & Pervasive Networks, 12(2), 09–15. 10.5383/juspn.12.02.002
27.
JainC. L. (2007). Six rules for effective forecasting. Harvard Business Review, 85(10), 158–159.
28.
JinJ., LiuY., JiP., & LiuH. (2016). Understanding big consumer opinion data for market-driven product design. International Journal of Production Research, 54(10), 3019–3041. 10.1080/00207543.2016.1154208
29.
JohansonM., BelenkiS., JalmingerJ., & FantM. (2014). Leveraging large volumes of data for knowledge-driven product development [Paper presentation]. IEEE Big Data, 736–741.
30.
KatchasuwanmaneeK., BatemanR., & ChengK. (2016). Development of the Energy-smart Production Management system (e-ProMan): A Big Data driven approach, analysis and optimisation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(5), 972–978. 10.1177/0954405415586711
31.
KeowA. K. S., & NeeA. Y. H. (2019). Emerging technologies for supply chain management. Global Supply Chain Institute, University of Tennessee. https://supplychainmanagement.utk.edu/blog/emerging-technology-in-supply-chain-management/
32.
KhanO., ChristopherM., & CreazzaA. (2012). Aligning product design with the supply chain: A case study. Supply Chain Management, 17(3), 323–336. 10.1108/13598541211227144
33.
KouhizadehM., & SarkisJ. (2018). Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability (Switzerland), 10(10), 3652. 10.3390/su10103652
34.
LabbiO., OuziziL., & DouimiM. (2016). Simultaneous design of a product and its supply chain integrating reverse logistic operations: An optimization model. https://hal.archives-ouvertes.fr/hal-01260795
35.
MehmoodR., & GrahamG. (2015). Big data logistics: A health-care transport capacity sharing model. Procedia Computer Science, 64, 1107–1114. 10.1016/j.procs.2015.08.566
36.
MongeonP., & Paul-husA. (2014, November). The journal coverage of bibliometric databases: A comparison of Scopus and Web of Science [Conference session]. METRICS 2014 Workshop at ASIS&T, Seattle, WA, USA.
37.
NguyenT., ZHOUL., SpieglerV., IeromonachouP., & LinY. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers and Operations Research, 98, 254–264. 10.1016/j.cor.2017.07.004
OsmonbekovT., & JohnstonW. J. (2018). Adoption of the Internet of Things technologies in business procurement: Impact on organizational buying behavior. Journal of Business and Industrial Marketing, 33(6), 781–791. 10.1108/JBIM-10-2015-0190
40.
ParéG., TrudelM. C., JaanaM., & KitsiouS. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information and Management, 52(2), 183–199. 10.1016/j.im.2014.08.008
41.
PrasadS., ZakariaR., & AltayN. (2018). Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of Operations Research, 270(1–2), 383–413. 10.1007/s10479-016-2280-7
ProvostF., & FawcettT. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. 10.1089/big.2013.1508
44.
RejebA., KeoghJ. G., & TreiblmaierH. (2019). Leveraging the Internet of Things and blockchain in supply chain management. Future Internet, 11(7), 1–22.
45.
RejebA., SimskeS., RejebK., TreiblmaierH., & ZailaniS. (2020). Internet of Things research in supply chain management and logistics: A bibliometric analysis. Internet of Things (Netherlands), 12, 100318. 10.1016/j.iot.2020.100318
46.
RussomP. (2011). Big Data Analytics—TDWI Best Practices Report Introduction to Big Data Analytics. TDWI Best Practices Report, Fourth Quarter, 19(4), 1–34. https://vivomente.com/wp-content/uploads/2016/04/big-data-analytics-white-paper.pdf
47.
SheelA., & NathV. (2019). Effect of blockchain technology adoption on supply chain adaptability, agility, alignment and performance. Management Research Review, 42(12), 1353–1374. 10.1108/MRR-12-2018-0490
48.
SinkovicsN. (2016). Enhancing the foundations for theorisingbibliometric mapping. International Marketing Review, 33(3), 327–350. 10.1108/IMR-10-2014-0341
49.
SrinivasanU., & ArunasalamB. (2013). Leveraging big data analytics to reduce healthcare costs. IT Professional, 15(6), 21–28. 10.1109/MITP.2013.55
TiedekuntaI. (2021). Utilising Internet of Things in demand planning process: Case Kalmar. https://jyx.jyu.fi/handle/123456789/78770?locale-attribute=en
52.
TiwariS., WeeH. M., & DaryantoY. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers and Industrial Engineering, 115(October), 319–330. 10.1016/j.cie.2017.11.017
53.
TreiblmaierH. (2018). The impact of the blockchain on the supply chain: A theory-based research framework and a call for action. Supply Chain Management, 23(6), 545–559. 10.1108/SCM-01-2018-0029
54.
WangG., GunasekaranA., NgaiE. W. T., & PapadopoulosT. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. 10.1016/j.ijpe.2016.03.014
55.
WangQ., & HuoB. (2018). The effect of intellectual capital on supply chain integration and competitive performance. Academy of Management Proceedings, 2018(1), 18643. 10.5465/ambpp.2018.18643abstract
56.
WebsterJ., & WatsonR. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii–xxiii. https://www.jstor.org/stable/4132319
57.
WhiteH. D., & GriffithB. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171. 10.1002/asi.4630320302
58.
WongD. (2012, November). Data is the next frontier, analytics the new tool. Big Innovation Center. http://www.mbsportal.bl.uk/taster/subjareas/techinnov/bic/143509datanextfrontier12.pdf
59.
WuK. J., LiaoC. J., TsengM. L., LimM. K., HuJ., & TanK. (2017). Toward sustainability: Using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142, 663–676. 10.1016/j.jclepro.2016.04.040
60.
YerpudeS., & SinghalT. K. (2017). Impact of Internet of Things (IoT) data on demand forecasting. Indian Journal of Science and Technology, 10(15), 1–5. 10.17485/ijst/2017/v10i15/111794
61.
ZhaoR., LiuY., ZhangN., & HuangT. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085–1097. 10.1016/j.jclepro.2016.03.006
62.
ZhongR. Y., HuangG. Q., & LanS. (2014). Shopfloor logistics management using RFID-enabled big data under physical interne [Paper presentation]. Proceedings of 1st International Physical Internet Conference (IPIC 2014), 2012–2013. https://drive.google.com/file/d/0BwqMeiFdDUgIVHVEVktJeGNUclU/view?usp=sharing
63.
ZhongR. Y., HuangG. Q., LanS., DaiQ. Y., ChenX., & ZhangT. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260–272. 10.1016/j.ijpe.2015.02.014
