Smart Agents for Sustainable Retail and Paint Supply Chains
Keywords:
Agentic AI Decision Systems,Autonomous Supply Chain Optimization,Sustainable Retail Logistics,Green Paint Manufacturing Processes,AI-Driven Demand Forecasting,Carbon Footprint Reduction in Supply Chains,Intelligent Inventory Management,Circular Economy in Manufacturing,Predictive Procurement Analytics,ESG-Compliant Operations Strategy.Abstract
Agentic systems are autonomous socio-technical systems that leverage artificial intelligence for more intelligent and tactically effective operation. The educational retail and chemical paint manufacturing industries have significant footprints for education, economic vitality, and environmental impact. Within those industries, agentic artificial intelligence enables supply-chain optimization and promotes sustainability across four dimensions: reduced negative influence on the social environment and community; improved capability for quality service, customer satisfaction, and community engagement; reduced use of non-renewable resources; and reduced risk of failure to meet applicable environment-related regulations.
Realizing the potential of agentic artificial intelligence requires accessible Sensor-to-Decision System of Systems data infrastructure capable of overcoming traditional silos within and among organizations. For the retail industry, the infrastructure supports agentic processes that enhance demand forecasting, inventory, risk of stockout, logistics, and fulfillment by detecting demand signals, understanding customer preferences and price responses, striking a balance between service and cost, minimizing lead time, and applying customer-centric fulfilment policies. Similarly, for the paint industry, the infrastructure enables agentic processes that enhance raw-material supply risk control and formulation transparency by assessing supply risk, cultivating strong supplier-firm relationships, establishing formulation-traceability matrices, exploring substitutes for environmentally harmful substances, monitoring product quality, and ensuring compliance with relevant product-requirement regulations.
References
1. Carpitella, S. (2024). Overcoming barriers to digital transformation towards greener supply chains in automotive paint shop operations. Sustainability, 16(5), 1948.
2. Qu, C., & Kim, E. (2024). Reviewing the roles of AI-integrated technologies in sustainable supply chain management: Research propositions and a framework for future directions. Sustainability, 16(14), 6186.
3. Zejjari, I., & Benhayoun, I. (2024). The use of artificial intelligence to advance sustainable supply chain: Retrospective and future avenues explored through bibliometric analysis. Discover Sustainability, 5, 174.
4. Winkelmann, S., Guennoun, R., Möller, F., Schoormann, T., & van der Valk, H. (2024). Back to a resilient future: Digital technologies for a sustainable supply chain. Information Systems and e-Business Management, 22(2), 315–350.
5. Rajput, S. (2024). Analysis of Industry 4.0 technological enablers for sustainable supply chain transparency in a fuzzy environment. Business Strategy and the Environment, 33(8), 8616–8636.
6. Espahbod, S., Tashakkori, A., Mohsenibeigzadeh, M., Zarei, M., Golshan Arani, G., Dzikuć, M., & Dzikuć, M. (2024). Blockchain-driven supply chain analytics and sustainable performance: Analysis using PLS-SEM and ANFIS. Sustainability, 16(15), 6469.
7. Vidal, U., Obregon, M., Ramos, E., Verma, R., & Coles, P. S. (2024). Sustainable and risk-resilient circular supply chain: A Peruvian paint manufacturing supply chain model. Sustainable Futures, 7, 100207.
8. Sakaline, G., & Buics, L. (2024). Advancing towards sustainable retail supply chains: AI-driven consumer segmentation in superstores. Engineering Proceedings, 79(1), 73.
9. Bressanelli, G., Adrodegari, F., Perona, M., & Saccani, N. (2024). Virtual agri-food supply chains: A holistic digital twin for sustainable food ecosystem design, control and transparency. Sustainable Production and Consumption, 46, 161–179.
10. Khot, S. B., & Thiagarajan, S. (2024). Unveiling the relationships between enablers in resiliently sustainable supply chains using an integrated TISM-Fuzzy MICMAC approach: Study of Indian automobile sector post-COVID-19. Multidisciplinary Science Journal, 6(12), e2024256.
11. Hasan, M. R., Khan, M. A., & Wuest, T. (2024). Towards Industry 5.0: A systematic literature review on sustainable and green composite materials supply chains. Procedia CIRP, 123, 421–428.
12. Bhatia, M. S., Kumar, S., & Singh, R. (2024). Artificial intelligence-enabled sustainable retail supply chain management: A systematic review and future research agenda. Journal of Retailing and Consumer Services, 78, 103756.
13. Gunasekaran, A., Yusuf, Y. Y., & Adeleye, E. O. (2024). Intelligent supply chain analytics for sustainable retail operations: Emerging technologies and applications. International Journal of Production Economics, 270, 109156.
14. Sharma, M., Luthra, S., Kumar, A., & Joshi, S. (2024). Smart autonomous agents for circular economy driven supply chains: A sustainability perspective. Resources, Conservation and Recycling, 203, 107410.
15. Choudhary, K., Mangla, S. K., & Kazançoğlu, Y. (2024). Agent-based decision support systems for sustainable supply chain resilience and transparency. Technological Forecasting and Social Change, 202, 123409.
16. Gupta, H., Kusi-Sarpong, S., & Rezaei, J. (2024). AI-enabled green supply chain transformation: A multi-criteria sustainability assessment framework. Journal of Cleaner Production, 447, 141392.
17. Singh, A., Mishra, N., & Ali, I. (2024). Intelligent retail inventory optimization using machine learning for sustainable supply chain performance. Computers & Industrial Engineering, 188, 109865.
18. Kumar, V., Ramkumar, M., & Venkatesh, V. G. (2024). Digital twins and smart agents for sustainable supply chain visibility and traceability. Annals of Operations Research, 338(1–2), 421–447.
19. Agrawal, R., Wankhede, V. A., & Kumar, A. (2024). Sustainable retail ecosystems through AI, IoT and autonomous decision-making technologies. Business Strategy and the Environment, 33(7), 7345–7364.
20. Purnomo, A., Syafrianita, S., Simatupang, T. M., & Abdul Mutalib, M. I. (2024). How managers’ green transformational leadership affects green resilient supply chain: The moderating impact of green ambidexterity and green innovation. Uncertain Supply Chain Management, 12(3), 1611–1624.
Additional Files
Published
Data Availability Statement
"This study uses illustrative and synthetic data examples to demonstrate proposed agentic AI models. No proprietary or third-party datasets are publicly available due to confidentiality agreements with the retail and paint manufacturing organizations referenced. Data supporting the conceptual framework are available from the corresponding author upon reasonable request."
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
