Descriptive and Predictive Analytics for Supply Chain Data with Machine Learning Technique
Keywords:
Supply chain analytics, demand forecasting, machine learning, supervised methods.Abstract
This study investigates how supply chain analytics, or SCA, can be used to examine past supply chain performance in the Asia Pacific region. The study assesses different supply chain elements, such as client demographics, product categories, payment options, and transportation, using historical sales data. The dataset is observed through descriptive analysis with scatter plots, box-plots, skewness analysis and bar chart. Regression-based machine learning models, such as linear regression, random forest and boosting were utilised to forecast demand (order item total), allowing businesses to analyse their logistical and production processes. The demand was well forecasted by Random Forest, and the danger of late delivery was well-predicted by ensemble learning models, according to the results. The study comes to the conclusion that, in order for businesses to increase productivity and customer happiness, both descriptive and predictive approaches are essential. While predictive analytics enables proactive decision-making and risk avoidance, descriptive analytics offers a thorough understanding of supply chain operations. The study contributes to the supply chain analytics field in the Asia Pacific region by setting a theoretical framework that allows organisations to adopt proactive strategies to optimize their performance based on different supply chain components. However, the paper has several limitations related to data collection, as it does not involve many companies and manufacturers. We need further research to enhance prediction performance and optimize the learning process.
Downloads
References
“World Bank Open Data,” World Bank Open Data. https://data.worldbank.org/indicator/NE.EXP.GNFS.CD?locations=Z4
C. Bode and J. R. Macdonald, “Stages of Supply Chain Disruption Response: Direct, Constraining, and Mediating Factors for Impact Mitigation,” Decision Sciences, vol. 48, no. 5, pp. 836–874, Sep. 2016, doi: 10.1111/deci.12245.
B. Avishai, “The Pandemic Isn’t a Black Swan but a Portent of a More Fragile Global System,” The New Yorker, Apr. 21, 2020. [Online]. Available: https://www.newyorker.com/news/daily-comment/the-pandemic-isnt-a-black-swan-but-a-portent-of-a-more-fragile-global-system
M. Mubarik and R. Z. B. R. M. Rasi, “Triad of Big Data Supply Chain Analytics, Supply Chain Integration and Supply Chain Performance: Evidences from Oil and Gas Sector,” Humanities and Social Sciences Letters, vol. 7, no. 4, pp. 209–224, Jan. 2019, doi: 10.18488/journal.73.2019.74.209.224.
“Supply Chain 4.0: A luxury or a necessity ?,” Social Innovation, Jan. 19, 2024. [Online]. Available: https://social-innovation.hitachi/en-us/think-ahead/manufacturing/supply-chain-4-0/
F. Steinberg, P. Burggräf, J. Wagner, B. Heinbach, T. Saßmannshausen, and A. Brintrup, “A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry,” Supply Chain Analytics, vol. 1, p. 100003, Mar. 2023, doi: 10.1016/j.sca.2023.100003.
M. A. Waller and S. E. Fawcett, “Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77–84, Jun. 2013, doi: 10.1111/jbl.12010.
R. D. Raut, S. K. Mangla, V. S. Narwane, M. Dora, and M. Liu, “Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains,” Transportation Research Part E Logistics and Transportation Review, vol. 145, p. 102170, Jan. 2021, doi: 10.1016/j.tre.2020.102170.
N. A. T. Oyewole, N. C. C. Okoye, N. O. C. Ofodile, and N. E. Ejairu, “Reviewing predictive analytics in supply chain management: Applications and benefits,” World Journal of Advanced Research and Reviews, vol. 21, no. 3, pp. 568–574, Mar. 2024, doi: 10.30574/wjarr.2024.21.3.0673.
T. Schoenherr and C. Speier‐Pero, “Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential,” Journal of Business Logistics, vol. 36, no. 1, pp. 120–132, Feb. 2015, doi: 10.1111/jbl.12082.
M. W. Barbosa, A. De La Calle Vicente, M. B. Ladeira, and M. P. V. De Oliveira, “Managing supply chain resources with Big Data Analytics: a systematic review,” International Journal of Logistics Research and Applications, vol. 21, no. 3, pp. 177–200, Aug. 2017, doi: 10.1080/13675567.2017.1369501.
“Transforming Data With Intelligence,” TDWI. https://tdwi.org/research/2011/09/best-practices-report-q4-big-data-analytics.aspx
C. L. P. Chen and C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, vol. 275, pp. 314–347, Aug. 2014, doi: 10.1016/j.ins.2014.01.015.
N. Chen, N. Chiang, and N. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, vol. 36, no. 4, p. 1165, Jan. 2012, doi: 10.2307/41703503.
G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: Certain investigations for research and applications,” International Journal of Production Economics, vol. 176, pp. 98–110, Jun. 2016, doi: 10.1016/j.ijpe.2016.03.014.
S. Tiwari, H. M. Wee, and Y. Daryanto, “Big data analytics in supply chain management between 2010 and 2016: Insights to industries,” Computers & Industrial Engineering, vol. 115, pp. 319–330, Jan. 2018, doi: 10.1016/j.cie.2017.11.017.
K. Spanaki, Z. Gürgüç, R. Adams, and C. Mulligan, “Data supply chain (DSC): research synthesis and future directions,” International Journal of Production Research, vol. 56, no. 13, pp. 4447–4466, Nov. 2017, doi: 10.1080/00207543.2017.1399222.
R. Y. Zhong, S. T. Newman, G. Q. Huang, and S. Lan, “Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives,” Computers & Industrial Engineering, vol. 101, pp. 572–591, Nov. 2016, doi: 10.1016/j.cie.2016.07.013.
K. Govindan, T. C. E. Cheng, N. Mishra, and N. Shukla, “Big data analytics and application for logistics and supply chain management,” Transportation Research Part E Logistics and Transportation Review, vol. 114, pp. 343–349, Jun. 2018, doi: 10.1016/j.tre.2018.03.011.
Z. Inamdar, R. Raut, V. S. Narwane, B. Gardas, B. Narkhede, and M. Sagnak, “A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018,” Journal of Enterprise Information Management, vol. 34, no. 1, pp. 101–139, Mar. 2020, doi: 10.1108/jeim-09-2019-0267.
R. Y. Zhong, S. Lan, C. Xu, Q. Dai, and G. Q. Huang, “Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 84, no. 1–4, pp. 5–16, Aug. 2015, doi: 10.1007/s00170-015-7702-1.
A. Brintrup, Y. Wang, and A. Tiwari, “Supply Networks as Complex Systems: A Network-Science-Based Characterization,” IEEE Systems Journal, vol. 11, no. 4, pp. 2170–2181, Dec. 2017, doi: 10.1109/jsyst.2015.2425137.
G. C. Souza, “Supply chain analytics,” Business Horizons, vol. 57, no. 5, pp. 595–605, Sep. 2014, doi: 10.1016/j.bushor.2014.06.004.
S. F. Wamba and S. Akter, “Understanding supply chain analytics capabilities and agility for data-rich environments,” International Journal of Operations & Production Management, vol. 39, no. 6/7/8, pp. 887–912, Dec. 2019, doi: 10.1108/ijopm-01-2019-0025.
B. Pochiraju and S. Seshadri, Essentials of Business Analytics. 2019. doi: 10.1007/978-3-319-68837-4.
M. A. Cohen, “Inventory Management in the Age of Big Data,” Harvard Business Review, Aug. 08, 2019. https://hbr.org/2015/06/inventory-management-in-the-age-of-big-data
M. Sharma and N. Garg, “Inventory Control and Big Data,” in Advances in logistics, operations, and management science book series, 2016, pp. 222–235. doi: 10.4018/978-1-4666-9888-8.ch011.
K. Katchasuwanmanee, R. Bateman, and K. Cheng, “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, vol. 230, no. 5, pp. 972–978, Jun. 2015, doi: 10.1177/0954405415586711.
D. Wardman, “Bringing Big Data to the Enterprise -Gaining new insight with Big Data capabilities,” 2013. [Online]. Available: https://public.dhe.ibm.com/software/os/systemz/pdf/09_-_Dan_Wardman_-_Bring_Big_Data_to_the_Enterprise_.pdf
A. Brintrup et al., “Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing,” International Journal of Production Research, vol. 58, no. 11, pp. 3330–3341, Nov. 2019, doi: 10.1080/00207543.2019.1685705.
N. M. He, N. H. Ji, N. Q. Wang, N. C. Ren, and R. Lougee, “Big data fueled process management of supply risks: Sensing, prediction, evaluation and mitigation,” Proceedings of the Winter Simulation Conference 2014, Dec. 2014, doi: 10.1109/wsc.2014.7019960.
Y. Fan, L. Heilig, and S. Voß, “Supply Chain Risk Management in the Era of Big Data,” in Lecture notes in computer science, 2015, pp. 283–294. doi: 10.1007/978-3-319-20886-2_27.
S. Dani, “Predicting and Managing Supply Chain Risks,” in International series in management science/operations research/International series in operations research & management science, 2009, pp. 53–66. doi: 10.1007/978-0-387-79934-6_4.
F. Constante, F. Silva, and A. Pereira, “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS,” Mar. 12, 2019. https://data.mendeley.com/datasets/8gx2fvg2k6/5
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
