Engineering Sustainable Supply Chain Optimization In Resource-Constrained Environments: A Geo-Spatial And AI-Based Data Science Perspective

Abstract

At a time characterized by heightened environmental issues and dwindling natural resources, supply chain optimization for sustainability has emerged as a strategic necessity. This paper discusses an integrative approach to engineering sustainable supply chain optimization (SSCO) in resource-limited settings through geo-spatial analytics and artificial intelligence (AI)-driven data science methodologies. The research addresses the unique challenges faced by supply chains in regions with underdeveloped infrastructure, unstable resources, and environmental vulnerability. The proposed approach combines geospatial data with machine learning methods to provide a dynamic data-driven system that enhances decision support for supply chain planning and operations. Geo-spatial analysis enables precise mapping of patterns of resources, transport routes, and environmental impact zones, which enables localized optimization interventions. AI technologies like predictive analytics, reinforcement learning, and optimization algorithms are used to identify patterns, forecast demand, allocate resources to maximize the value achieved, and adapt dynamically to real-time conditions. Key innovations include the development of an multi-layered decision-support platform combining satellite imagery, socio-economic variables, climate variables, and logistics performance indicators. The platform allows stakeholders to model trade-offs among cost, carbon footprint, and service levels to varying constraints. Low-income and climate-vulnerable country case studies confirm the practicability of this approach, with emissions reductions, better delivery efficiency, and enhanced resilience to disruptions. Furthermore, the study highlights the necessity for ethical AI practices and data governance for inclusive benefits to be generated across different communities. It highlights the necessity for intersectoral collaboration and open data platforms to propagate sustainable practices globally. Through the integration of geo-spatial intelligence and AI-driven insights, this research gives a paradigm-redefining perspective to sustainable supply chain engineering. It presents actionable paths to policymakers, logistics managers, and development organizations to unlock the intricacy of resource-constrained settings while promoting environmental stewardship and socio-economic growth. The proposed model carries profound implications for the execution of the United Nations Sustainable Development Goals (SDGs) that are concerned with responsible consumption, climate action, and industry innovation.