# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 1 Introduction
At every modal transition or carrier hand-off, there is a risk of data loss, inconsistency, or missing metadata (e.g., fuel type, vehicle class, and exact distances). These challenges are especially critical in logistics and transport emissions accounting. When detailed route- or mode-specific data is unavailable due to lack of interoperability, non-cooperation by carriers, or missing telematics, companies are often forced to rely on generic emission factors or assumptions, which significantly reduces the precision and validity of their Scope 3 calculations. Such workarounds undermine both the accuracy and comparability of emissions reporting. The effort required to collect consistent freight data \"across multiple journeys\" frequently becomes a major operational bottleneck. In summary, manufacturing SMEs in particular face significant barriers when trying to quantify transport-related emissions using current tools, standards, and available resources [15]. Current existing LCAs and carbon-tracking tools offer only a partial solution and fail to adequately solve these problems. Most commercial LCA software can model process or vehicle emissions based on average per-kilometer factors, but do not incorporate detailed routing or automated enterprise data preparation. For example, standard LCA workflows typically require users to input shipment distances manually and apply default emission factors, rather than calculating emissions based on actual road networks or delivery plans [16]. Similarly, many carbon calculators lack interfaces to company enterprise systems such as enterprise resource planning (ERP) or transportation management systems (TMSs), so the data must be aggregated and entered by hand. In global-acting SMEs in the automotive industry, the supply chain can easily have up to 1000 locations, including suppliers, plants, and customers. Based on the assumption that all the goods could be transported between all these locations, it would result in approximately 500,000 potential primary routes. Finally, each route requires transport data to be identified and analyzed [17]. In practice, this means current tools are neither integrated nor scalable for complex logistics scenarios. A manufacturer might extract order details from its ERP and estimate emissions in an external LCA tool, but each shipment or mode change often requires manual intervention. There is little capacity to automatically reoptimize routes or recalculate emissions in response to changing conditions. Thus, current systems can provide static or periodic carbon reports, but they do not support continuous, high-fidelity freight emission accounting. The reliance on spreadsheets and disconnected software not only consumes time but also increases the risk of errors and omissions. The discussion above highlights a critical need for integrated technological solutions that automate the generation and optimization of transport route data for carbon footprint calculations. By leveraging advanced analytics and enterprise data flows, such a system could address the mentioned limitations effectively. For example, machine learning models can enhance the accuracy and timeliness of carbon footprint calculations by identifying patterns in operational data [18]. Previous efforts to dynamically integrate life cycle inventory (LCI) and ERP in an Industry 4.0 environment have shown promising potential [19]. Tight ERP integration can ensure that activity data (such as production volumes, vehicle logs, and fuel invoices) continuously feed into the supply chain model without manual intervention. Additionally, emerging case studies indicate that AI-driven data collection can reduce reporting burdens and support more effective carbon neutrality strategies [18]. To address this problem, in a previous preliminary work, [PERSON] et al. [17] introduced an ERP-integrated, Python-based routing tool. This approach enabled the automated identification of transport distances between supply chain actors, marking an important step toward automating transport data acquisition for LCAs.