Authors : Obadina A. Babatunde

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/4sr3bmvm

Scribd : https://tinyurl.com/mttdjb3u

DOI : https://doi.org/10.38124/ijisrt/26jul1065

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Resins, pigments (titanium dioxide), solvents, plasticizers, and petrochemical monomers are a small range of internationally traded chemical feedstocks whose lead times and prices are highly volatile and critical to the paint, ink, and plastics manufacturing industries. This paper investigates and discusses the possibility of using predictive analytics, including classic time-series econometrics, machine learning, and reinforcement learning techniques, to predict import needs for chemical raw materials and to optimize inventory decisions in the chemical value chain. Based on the peer-reviewed literature in operations research, the paper summarizes methodologies, industry evidence, and architectures for linking forecasting outputs to inventory management policies such as the economic order quantity (EOQ) model, dynamic safety stock, and multi-echelon optimization. The practical challenges related to data quality, organisational preparedness and the volatility resulting from geopolitical and logistical uncertainty are also discussed, followed by managerial recommendations for resin, pigment and solvent-intensive manufacturing procurement and supply chain functions.

References :

1. Al Mestneer, R., & Bollino, C. A. (2024). Long-term forecasting models of oil demand emerging from the global petrochemical sector. Energies, 17(20), 5046. https://doi.org/10.3390/en17205046
2. Alnahhal, M., Aylak, B. L., Al Hazza, M., & Sakhrieh, A. (2024). Economic order quantity: A state-of-the-art in the era of uncertain supply chains. Sustainability, 16(14), 5965. https://doi.org/10.3390/su16145965
3. Arunadevi, E., & Umamaheswari, S. (2026). Inventory optimization under tri-phased demand with dual aging and controlled backlogging. iScience, 29(4), 115268. https://doi.org/10.1016/j.isci.2026.115268
4. Bergsma, R., de Ruijt, C., & Bhulai, S. (2025). A systematic review of machine learning approaches in inventory control optimization. Operations Research Perspectives, 15, Article 100367. https://doi.org/10.1016/j.orp.2025.100367
5. Bustos, O., Pomares-Quimbaya, A., & Stellian, R. (2025). Machine learning, stock market forecasting, and market efficiency: A comparative study. International Journal of Data Science and Analytics, 20(7), 6815–6839. https://doi.org/10.1007/s41060-025-00854-4
6. CEPE European Council of the Paint, Printing Ink, and Artist’s Colours Industry. (2021). The coatings industry under pressure from developments in the raw materials market. IST International Surface Technology, 14(2), 6–7. https://doi.org/10.1007/s35724-021-0469-z
7. Chen, Y., Dong, S., Qian, S., & Chung, K. (2024). Impact of oil price volatility and economic policy uncertainty on business investment: Insights from the energy sector. Heliyon, 10(5), e26533. https://doi.org/10.1016/j.heliyon.2024.e26533
8. Diggle, A., & Walker, T. R. (2022). Environmental and economic impacts of mismanaged plastics and measures for mitigation. Environments, 9(2), 15. https://doi.org/10.3390/environments9020015
9. Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246
10. Gonçalves, J. N. C., Carvalho, M. S., & Cortez, P. (2020). Operations research models and methods for safety stock determination: A review. Operations Research Perspectives, 7, 100164. https://doi.org/10.1016/j.orp.2020.100164
11. Grand View Research. (2023). Paints and coatings industry research data book, 2023–2030. Grand View Research.
12. Harris, F. W. (1913). How many parts to make at once? Factory, the Magazine of Management, 10(2), 135–136, 152.
13. Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690
14. Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008
15. Hu, T., Chen, C., & Wei, H. (2024). A novel methodology for forecasting petrochemical product prices in the East China market by applying ARIMAX time series and machine learning models. In Proceedings of the International Petroleum Technology Conference, Dhahran, Saudi Arabia. https://doi.org/10.2523/IPTC-23114-MS
16. ICIS. (n.d.). Raw materials price forecast: Compliance and methodology. ICIS.
17. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by the COVID-19 outbreak. International Journal of Production Research, 58, 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
18. Lee, C.-Y., Chou, B.-J., & Huang, C.-F. (2022). Data science and reinforcement learning for price forecasting and raw material procurement in the petrochemical industry. Advanced Engineering Informatics, 51, 101443. https://doi.org/10.1016/j.aei.2021.101443
19. Ma, S., & Fildes, R. (2021). Retail sales forecasting with meta-learning. European Journal of Operational Research, 288(1), 111–128. https://doi.org/10.1016/j.ejor.2020.05.038
20. Mandl, C., & Minner, S. (2023). Data-driven optimization for commodity procurement under price uncertainty. Manufacturing & Service Operations Management, 25(2), 371–390. https://doi.org/10.1287/msom.2020.0890
21. Pandit, P., Sagar, A., Ghose, B., Dey, P., Paul, M., Alqadhi, S., Mallick, J., Almohamad, H., & Abdo, H. G. (2023). Hybrid time series models with an exogenous variable for improved yield forecasting of major Rabi crops in India. Scientific Reports, 13(1), 22240. https://doi.org/10.1038/s41598-023-49544-w
22. Pilcher, G. R. (2023). Coatings raw materials following the supply chain crisis. PCI Magazine (Paint & Coatings Industry).
23. Raabe, D. (2023). The materials science behind sustainable metals and alloys. Chemical Reviews, 123(5), 2436–2608. https://doi.org/10.1021/acs.chemrev.2c00799
24. Rust Bullet Australasia. (2025). The 4 components of paints & coatings. https://rustbullet.com.au/the-4-components-of-paints-coatings/
25. Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
26. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
27. Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926. https://doi.org/10.1016/j.cor.2020.104926
28. Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: Mapping the territory. International Journal of Production Research, 60(24), 7527–7550. https://doi.org/10.1080/00207543.2022.2029611
29. Sikharulidze, M., & Fuschi, D. L. (2023). Causes of the paints and coatings raw materials supply chain crisis and its impact on businesses. Journal of Organisational Studies and Innovation, 10(2), 37–52. https://doi.org/10.51659/josi.22.172
30. Zhu, X., Ninh, A., Zhao, H., & Liu, Z. (2021). Demand forecasting with supply-chain information and machine learning: Evidence in the pharmaceutical industry. Production and Operations Management, 30(9), 3231–3252. https://doi.org/10.1111/poms.13456.