The paper analyzed the impact of digital transformation, specifically through artificial intelligence (AI) and big data, on enhancing supply chain efficiency in the Nigerian oil sector. The research aimed to evaluate how these technologies improve operational performance and identify the challenges and opportunities associated with their implementation. Using a systematic content analysis in reviewing recent related studies, the findings revealed that AI and big data significantly enhance decision-making, predictive maintenance, inventory management, and risk mitigation, contributing to overall supply chain efficiency. However, the study also identifies several challenges, including inadequate infrastructure, a shortage of skilled personnel, and organizational resistance to change. Despite these barriers, the opportunities for optimizing operations and improving supply chain resilience are considerable. The paper concluded that, with targeted investments in technology and workforce development, the Nigerian oil sector can fully leverage AI and big data to achieve sustained operational excellence and competitive advantage in a dynamic global market.
| Published in | International Journal of Management and Fuzzy Systems (Volume 10, Issue 2) |
| DOI | 10.11648/j.ijmfs.20241002.11 |
| Page(s) | 35-42 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Artificial Intelligence, Big Data, Supply Chain Management, Digital Transformation, Nigerian Oil Sector, Operational Efficiency, Risk Management
| [1] | Abdulrahman, A. K., Hashim, N., & Saidin, S. F. (2020). Predictive analytics and artificial intelligence for Nigerian oil and gas sector optimization. Journal of Petroleum Exploration and Production Technology, 10(4), 1023-1035. |
| [2] | Adebayo, O., & Nduka, O. (2021). Challenges of big data implementation in supply chain management: Evidence from the Nigerian oil sector. International Journal of Production Research, 59(10), 3034-3048. |
| [3] | Aghadiuno, I. A., Fagbohun, F. F., & Adeyemo, S. A. (2022). AI-driven predictive maintenance in the Nigerian oil industry: Enhancing supply chain efficiency. Energy Reports, 8, 550-564. |
| [4] | Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. |
| [5] | Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. |
| [6] | Brinch, M. (2018). Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework. International Journal of Operations & Production Management, 38(7), 1589–1614. |
| [7] | Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. |
| [8] | Choi, T. M., Wallace, S. W., & Wang, Y. (2021). Big data analytics in operations management. Journal of Production Economics, 233, 107968. |
| [9] | Christopher, M. (2016). Logistics & supply chain management (5th ed.). Pearson Education. |
| [10] | Chukwuma, O., Nwachukwu, A., & Okeke, C. (2022). The role of big data analytics in risk management in the Nigerian oil supply chain. Supply Chain Management: An International Journal, 27(6), 889-902. |
| [11] | Fatorachian, H., & Kazemi, H. (2021). Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 32(1), 17–32. |
| [12] | Ghadge, A., Wurtmann, H., & Seuring, S. (2020). Managing risks in supply chains: A decision-making perspective. Supply Chain Management: An International Journal, 25(5), 633-647. |
| [13] | Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). Supply chain resilience: Role of complexities and strategies. International Journal of Production Research, 55(17), 5273-5283. |
| [14] | Ibrahim, A., Oduwole, O., & Ojo, A. (2023). Leveraging big data analytics for supply chain optimization in the Nigerian oil industry. Journal of Supply Chain Management, 59(2), 114-126. |
| [15] | Ivanov, D., Dolgui, A., & Sokolov, B. (2021). Digital supply chain twin: Exploring the synergies between big data and artificial intelligence. International Journal of Production Research, 59(20), 6138–6159. |
| [16] | Iwuchukwu, A. G., Nwachukwu, S. C., & Okwor, M. A. (2023). Integrating big data and artificial intelligence for supply chain optimization in the Nigerian oil sector. Journal of Cleaner Production, 370, 133389. |
| [17] | Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). SAGE Publications. |
| [18] | Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2020). Purchasing and supply chain management(7th ed.). Cengage Learning. |
| [19] | Nwaogbe, O., Nduka, O., & Ijeoma, A. (2022). The impact of big data analytics on decision-making in the Nigerian oil sector. International Journal of Oil, Gas and Coal Technology, 25(1), 32-46. |
| [20] | Obi, E., Okwor, M., & Nwogbaga, E. (2023). Integrating big data analytics and IoT for enhanced supply chain management in the Nigerian oil sector. Journal of Cleaner Production, 367, 132999. |
| [21] | Odularu, G. O., & Okeke, P. O. (2021). Digital transformation in the Nigerian oil and gas industry: Challenges and opportunities. Energy Research & Social Science, 75, 101980. |
| [22] | Olawuyi, T. (2021). Enhancing supply chain resilience through artificial intelligence: Insights from the Nigerian oil sector. International Journal of Supply Chain Management, 10(4), 112-123. |
| [23] | Olawuyi, T. (2022). Application of big data analytics in Nigerian offshore oil production. Marine and Petroleum Geology, 136, 105454. |
| [24] | Olufayo, A. E., & Onifade, A. T. (2022). Leveraging artificial intelligence for supply chain risk management in the Nigerian oil sector. Supply Chain Management: An International Journal, 27(3), 321-335. |
| [25] | Osu, A. R. (2020). Infrastructure challenges in digitalizing Nigeria’s oil and gas sector. Energy Policy, 145, 111729. |
| [26] | Oyewole, D. O., Onuoha, S. O., & Oladele, A. (2021). Bridging the digital skills gap in Nigeria's oil industry: A workforce development strategy. Journal of Human Resource Development, 8(2), 105-118. |
| [27] | 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. |
| [28] | Sharma, A., Luthra, S., Joshi, S., & Kumar, A. (2022). Developing and analyzing supply chain resilience: Integration of artificial intelligence and big data analytics. Journal of Cleaner Production, 331, 129862. |
| [29] | Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: A dynamic capabilities perspective. Production and Operations Management, 27(10), 1849–1867. |
| [30] | Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. |
| [31] | Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2020). 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. |
| [32] | Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2019). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. |
| [33] | Wamba, S. F., Kala Kamdjoug, J. R., Wanko, C. E. T. & Taguimdje, S.L., 2020. Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), pp. 1893-1924. |
| [34] | Wang, Y., Kung, L., Gupta, S. & Ozdemir, S., 2019. Leveraging big data analytics to improve quality of care in healthcare organizations: A configurational perspective. British Journal of Management, 30(2), pp. 519-537. |
APA Style
Eteyen, O. (2024). Digital Transformation in Supply Chain Management: Leveraging AI and Big Data for Enhanced Efficiency in the Nigerian Oil Sector. International Journal of Management and Fuzzy Systems, 10(2), 35-42. https://doi.org/10.11648/j.ijmfs.20241002.11
ACS Style
Eteyen, O. Digital Transformation in Supply Chain Management: Leveraging AI and Big Data for Enhanced Efficiency in the Nigerian Oil Sector. Int. J. Manag. Fuzzy Syst. 2024, 10(2), 35-42. doi: 10.11648/j.ijmfs.20241002.11
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Download@article{10.11648/j.ijmfs.20241002.11,
author = {Oboho Eteyen},
title = {Digital Transformation in Supply Chain Management: Leveraging AI and Big Data for Enhanced Efficiency in the Nigerian Oil Sector
},
journal = {International Journal of Management and Fuzzy Systems},
volume = {10},
number = {2},
pages = {35-42},
doi = {10.11648/j.ijmfs.20241002.11},
url = {https://doi.org/10.11648/j.ijmfs.20241002.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20241002.11},
abstract = {The paper analyzed the impact of digital transformation, specifically through artificial intelligence (AI) and big data, on enhancing supply chain efficiency in the Nigerian oil sector. The research aimed to evaluate how these technologies improve operational performance and identify the challenges and opportunities associated with their implementation. Using a systematic content analysis in reviewing recent related studies, the findings revealed that AI and big data significantly enhance decision-making, predictive maintenance, inventory management, and risk mitigation, contributing to overall supply chain efficiency. However, the study also identifies several challenges, including inadequate infrastructure, a shortage of skilled personnel, and organizational resistance to change. Despite these barriers, the opportunities for optimizing operations and improving supply chain resilience are considerable. The paper concluded that, with targeted investments in technology and workforce development, the Nigerian oil sector can fully leverage AI and big data to achieve sustained operational excellence and competitive advantage in a dynamic global market.
},
year = {2024}
}
TY - JOUR T1 - Digital Transformation in Supply Chain Management: Leveraging AI and Big Data for Enhanced Efficiency in the Nigerian Oil Sector AU - Oboho Eteyen Y1 - 2024/12/25 PY - 2024 N1 - https://doi.org/10.11648/j.ijmfs.20241002.11 DO - 10.11648/j.ijmfs.20241002.11 T2 - International Journal of Management and Fuzzy Systems JF - International Journal of Management and Fuzzy Systems JO - International Journal of Management and Fuzzy Systems SP - 35 EP - 42 PB - Science Publishing Group SN - 2575-4947 UR - https://doi.org/10.11648/j.ijmfs.20241002.11 AB - The paper analyzed the impact of digital transformation, specifically through artificial intelligence (AI) and big data, on enhancing supply chain efficiency in the Nigerian oil sector. The research aimed to evaluate how these technologies improve operational performance and identify the challenges and opportunities associated with their implementation. Using a systematic content analysis in reviewing recent related studies, the findings revealed that AI and big data significantly enhance decision-making, predictive maintenance, inventory management, and risk mitigation, contributing to overall supply chain efficiency. However, the study also identifies several challenges, including inadequate infrastructure, a shortage of skilled personnel, and organizational resistance to change. Despite these barriers, the opportunities for optimizing operations and improving supply chain resilience are considerable. The paper concluded that, with targeted investments in technology and workforce development, the Nigerian oil sector can fully leverage AI and big data to achieve sustained operational excellence and competitive advantage in a dynamic global market. VL - 10 IS - 2 ER -
Department of Logistics Management, Universidad Catolica San Antonio, Murcia, Spain
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