Skip to main content
Review | Green Intelligent Systems and Applications | Volume 2 - Issue 2 - 2022 | 108-117 | https://doi.org/10.53623/gisa.v2i2.115
© 2022 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Big Data in Supply Chain Management: A Systematic Literature Review

Author(s): Johan Krisnanto Runtuk 1 , Filson Sidjabat 2 , Jsslynn 1 , Felicia Jordan 1
Author(s) information:
1 Industrial Engineering Study Program, Faculty of Engineering, President University, Indonesia
2 Environmental Engineering Study Program, Faculty of Engineering, President University, Indonesia

Corresponding author

Green Intelligent Systems and Applications

Volume 2 - Issue 2 - 2022

 About the journal
Submit your article
Big data analytics (BDA) have the potential to improve upon and change conventional supply chain management (SCM) techniques. Using BDA, organisations need to build the necessary skills to use big data effectively. Since BDA is relatively new and has few practical applications in SCM and logistics, a systematic review is needed to emphasise the most significant advancements in current research. The objectives are to evaluate and categorise the literature that addresses the big data potential in SCM and the current practises of big data in SCM. The Systematic Literature Review (SLR) was conducted to analyse several published papers between 2017 and 2022. It follows four steps: the literature collection, descriptive analysis, category selection, and material evaluation in a systematic review. The finding reveals that BDA has been applied in many supply chain functions. Furthermore, integrating BDA in SCM has several advantages, including improved data analytics capabilities, logistical operation efficiency, supply chain and logistics sustainability, and agility. Finally, the study emphasises the importance of using BDA to support the success of SCM in businesses.
Read Full-Text
Previous article
Next article

Zhang, G.; Yang, Y.; Yang, G. (2022). Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America. Annals of Operations Research, https://doi.org/10.1007/s10479-022-04689-1.

Chen I.J.; Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 22, 119–150. https://doi.org/10.1016/j.jom.2003.12.007.

Raut, R.D.; Yadav, V.S.; Cheikhrouhou, N.; Narwane, V.S.; Narkhede, B.E. (2021). Big data analytics: Implementation challenges in Indian manufacturing supply chains. Computers in Industry, 125, 103368. https://doi.org/10.1016/j.compind.2020.103368.

Meredith, R.; Remington, S.; O’Donnell, P.; Sharma, N. (2012). Organisational transformation through Business Intelligence: theory, the vendor perspective and a research agenda. Journal of Decision Systems, 21, 187–201. https://doi.org/10.1080/12460125.2012.731218.

Hernandez-de-Menendez, M.; Morales-Menendez, R.; Escobar, C.A.; McGovern, M. (2020). Competencies for Industry 4.0. International Journal on Interactive Design and Manufacturing, 14, 1511–1524. https://doi.org/10.1007/s12008-020-00716-2.

Narwane, V.S.; Raut, R.D.; Yadav, V.S.; Cheikhrouhou, N.; Narkhede, B.E.; Priyadarshinee, P. (2021). The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. Journal of Enterprise Information Management, 34, 1452–1480. https://doi.org/10.1108/JEIM-11-2020-0463.

Hallikas, J.; Immonen, M.; Brax, S. (2021). Digitalizing procurement: the impact of data analytics on supply chain performance. Supply Chain Management, 26, 629–646. https://doi.org/10.1108/SCM-05-2020-0201.

Elgendy, N.; Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. In Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science; Perner, P., Ed.; Springer: Cham, Switzerland; Volume 8557, pp. 214–227. https://doi.org/10.1007/978-3-319-08976-8_16,

Maroufkhani, P.; Wagner, R.; Wan Ismail, W.K.; Baroto, M.B.; Nourani, M. (2019). Big data analytics and firm performance: A systematic review. Information, 10, https://doi.org/10.3390/INFO10070226.

Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.; 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.

Choi, T.M.; Wallace, S.W.; Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operation Management, 27, 1868-1883. https://doi.org/10.1111/poms.12838.

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.

Saucedo-Martínez, J.A.; Pérez-Lara, M.; Marmolejo-Saucedo, J.A.; Salais-Fierro, T.E.; Vasant, P. (2018). Industry 4.0 framework for management and operations: a review. Journal of Ambient Intelligence and Humanized Computing, 9, 789–801. https://doi.org/10.1007/s12652-017-0533-1.

Kim, D.; Woo, J.; Shin, J.; Lee, J.; Kim, Y. (2019). Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market. Industrial Management & Data Systems, 119, 1089–1103. https://doi.org/10.1108/IMDS-08-2018-0347.

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

Seyedan, M.; Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7, 53. https://doi.org/10.1186/s40537-020-00329-2.

Bienhaus F.; Haddud, A. (2018). Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24, 965–984. https://doi.org/10.1108/BPMJ-06-2017-0139.

Ji, G.; Hu, L.; Tan, K.H. (2017). A study on decision-making of food supply chain based on big data. Journal of Systems Science and Systems Engineering, 26, 183–198. https://doi.org/10.1007/s11518-016-5320-6.

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

Majeed, A.; Lv, J.; Peng, T. (2019). A framework for big data driven process analysis and optimization for additive manufacturing. Rapid Prototyping Journal, 25, 308–321. https://doi.org/10.1108/RPJ-04-2017-0075.

Li, C.; Chen, Y.; Shang, Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal, 29, 101021. https://doi.org/10.1016/j.jestch.2021.06.001.

Lee, J.Y.; Yoon, J.S.; Kim, B.-H. (2017). A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: An empirical case study of a die casting factory. International Journal of Precision Engineering and Manufacturing, 18, 1353–1361. https://doi.org/10.1007/s12541-017-0161-x.

Sang, H.; Takahashi, S.; Gaku, R. (2019). Big Data-Driven Simulation Analysis for Inventory Management in a Dynamic Retail Environment. Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018, Singapore: Springer Singapore, pp. 687–694. https://doi.org/10.1007/978-981-13-3402-3_72.

Liu, L.; Zhu, G.; Zhao, X. (2022). Application of medical supply inventory model based on deep learning and big data. International Journal of System Assurance Engineering and Management, 469. https://doi.org/10.1007/s13198-022-01669-3.

Mitroshin, P.; Shitova, Y.; Shitov, Y.; Vlasov, D.; Mitroshin, A. (2022). Big Data and Data Mining Technologies Application at Road Transport Logistics. Transportation Research Procedia, 61, 462–466. https://doi.org/10.1016/j.trpro.2022.01.075.

Hurtado, P.A.; Dorneles, C.; Frazzon, E. (2019). Big Data application for E-commerce’s Logistics: A research assessment and conceptual model. IFAC-PapersOnLine, 52, 838–843. https://doi.org/10.1016/j.ifacol.2019.11.234.

Chen, Y. (2020). Intelligent algorithms for cold chain logistics distribution optimization based on big data cloud computing analysis. Journal of Cloud Computing, 9, 37. https://doi.org/10.1186/s13677-020-00174-x.

Hopkins J.; Hawking P. (2018). Big Data Analytics and IoT in logistics: a case study. International Journal of Logistics Management, 29, 575–591. https://10.1108/IJLM-05-2017-0109.

Saleem, H.; Li, Y.; Ali, Z.; Ayyoub, M.; Wang, Y.; Mehreen, A. (2021). Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation. Journal of Enterprise Information Management, 34, 1121–1143. https://10.1108/JEIM-03-2020-0119.

Gopal, P.R.C.; Rana, N.P.; Krishna, T.V.; Ramkumar, M. (2022). Impact of big data analytics on supply chain performance: an analysis of influencing factors. Annals of Operations Research, https://10.1007/s10479-022-04749-6.

Read Full-Text
About this article

SUBMITTED: 02 September 2022
ACCEPTED: 23 November 2022
PUBLISHED: 24 November 2022
SUBMITTED to ACCEPTED: 82 days
DOI: https://doi.org/10.53623/gisa.v2i2.115

Cite this article
Runtuk, J. K., Sidjabat, F., Jsslynn, & Jordan, F. (2022). Big Data in Supply Chain Management: A Systematic Literature Review. Green Intelligent Systems and Applications, 2(2), 108–117. https://doi.org/10.53623/gisa.v2i2.115
Accessed
965
Citations
0
Share this article