# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 1 Introduction  
The two most important global megatrends shaping present-day industry and society are digital transformation and environmental sustainability. Manufacturing companies are increasingly aligning digital transformation initiatives with sustainability objectives, driving convergence between Industry 4.0 and green manufacturing paradigms [1, 2]. This integration has positioned sustainability as a core element of production planning and operations, requiring companies to develop practices that enhance environmental performance while maintaining productivity [3]. Such convergence requires companies to design production processes and supply-chain networks that maximize efficiency and resource usage while minimizing resource consumption, pollution, and waste. Advanceddigital technologies, such as artificial intelligence (AI), big data, or the Internet of Things (IoT), can help to achieve this goal by enabling more effective process optimization, resource usage tracking, and decision making on operational and strategic levels [3; 4]. Thus, the twin pursuits of digital innovation and sustainability jointly push the development of leaner, smarter, and more eco-efficient manufacturing systems [2; 5]. However, regulatory and market forces are intensifying the demand for comprehensive emission (carbon) reporting. The EU's Corporate Sustainability Reporting Directive (CSRD), for example, requires companies to measure and disclose greenhouse-gas emissions across their full value chains, explicitly including indirect (Scope 3) emissions from upstream and downstream activities [6]. Although the CSRD applies only to large, listed firms, its effects cascade down supply chains. Large OEMs and retailers now demand that their suppliers (often SMEs) supply cradle-to-gate emission data. In practice, companies must therefore account for emissions from suppliers, product use, and, notably, transportation. These requirements have proven especially challenging for SMEs, which often lack the staff, capital, and data infrastructure to assemble detailed emissions inventories and analysis [7; 8]. In short, emissions (carbon) reporting is no longer an option: stakeholders (investors, regulators, and customers) expect granular footprint data, and failure to provide it can harm competitiveness and access to markets and finance [9]. The significance of transportation-related emissions becomes particularly evident when conducting lifecycle assessments (LCAs). According to ISO 14040 [10] and ISO 14044 [11], which define the principles and framework of LCAs, transport activities represent a mandatory part of the life cycle inventory (LCI). Here, all inputs and outputs within the system boundaries of a product must be systematically identified and quantified [10; 11]. This includes fuel consumption, emissions output (e.g., CO\({}_{2}\), NOx, and particulate matter), and the precise definition of spatial system boundaries across cradle-to-gate or cradle-to-grave scopes [12; 13]. Based on empirical evidence from conducted LCA studies, transport emissions typically represent 5-10% of total product carbon footprints in automotive manufacturing. On the technological side, SMEs are faced with missing or non-existent digital tools that focus on tracking transport activities. Many SMEs lack, e.g., dedicated transportation management systems (TMSs) or tracking platforms due to cost, a lack of necessity, and/or complexity [14]. Without a TMS, logistics data is often siloed into spreadsheets or disparate legacy systems. Manual data entry and the use of disconnected software tools often result in fragmented and inconsistent transport emissions data, making it difficult for organizations to obtain a complete or reliable picture of their carbon footprint. This issue is particularly severe in modern multimodal supply chains, where shipments typically span multiple transport modes (road, rail, sea, and air), each involving different carriers, systems, and data formats.