By Swapnil Bembde, Researcher, R&D Division, Hitachi America, Ltd.
In today’s increasingly complex global economy, supply chains—whether in the auto, electronics or pharmaceutical industries, among others—are more vulnerable than ever. Disruptions due to trade wars, geopolitical conflicts, environmental crises, or unforeseen economic downturns can send ripples through industries, impacting production timelines, cost structures, and overall business continuity.
The global automotive industry’s semiconductor shortage from 2020 to 2022 due to COVID-19 highlights the vulnerability of modern supply chains. It’s been estimated that the disruption resulted in $210 billion in lost revenue1 and 9.5 million fewer vehicles produced2. Such disruptions often trigger cascading delays beyond Tier 1 suppliers, exposing critical gaps in visibility and impeding efforts to anticipate risks or act strategically.
Large companies often rely on thousands of suppliers, each connected to its own network of downstream vendors. A persistent challenge in supply chain management is the lack of visibility into these extended tiers. Traditional methods, such as interviews and basic analytics, have proven insufficient for gathering meaningful supplier data.
Most stakeholders across the supply chain, whether manufacturers, suppliers, logistics providers, and retailers, operate in silos and are often reluctant to share information due to competitive, technological, or compliance-related concerns. As a result, procurement teams struggle to anticipate disruptions, manage cost fluctuations, and identify alternative sources.
Graph Neural Networks (GNNs) offer a breakthrough by learning from patterns in interconnected trade and transactional data. Rather than requiring direct access to proprietary data, GNNs infer hidden relationships and dependencies across the supply chain, enabling deeper insight and more proactive decision-making.
At their core, GNNs are advanced machine learning models designed to analyze complex relationships within interconnected data. Unlike traditional models that focus on linear connections, GNNs can capture intricate interdependencies between different entities in a supply chain.
Imagine supply chain data as a massive, interconnected web. Each node represents a stakeholder—supplier, product, or material. GNNs continuously learn from these relationships to uncover hidden patterns, predict risks, and optimize procurement decisions.
While traditional analytics isolate pieces of information, GNNs create a holistic view of the supply chain, enabling businesses to see beyond direct suppliers and gain insights into the deeper layers of their supplier networks.
GNN-based analytics allow businesses to predict and respond to disruptions proactively rather than reactively. Practical applications include:
To tackle these challenges, we at Hitachi R&D are developing a transformative GNN solution that delivers unparalleled visibility, predictive capability, and strategic insight.
Hitachi’s GNN-based supply chain management platform offers significant competitive advantages. It integrates internal company data from ERP and EDI systems with external data on global trade flows, ESG performance, and geopolitical risk. This fusion enables unprecedented visibility into multi-tier supply chains.
Our proprietary enhancements further illuminate deep-tier supplier networks, improving accuracy in bill-of-material estimation and enabling proactive procurement and risk management.
Our GNN solution has been validated within the Hitachi Group across diverse sectors, including industrial manufacturing, mobility solutions, energy systems, consumer products, healthcare, and infrastructure.
In one case study, a procurement division initially attempted to improve visibility by manually interviewing suppliers—achieving just 20% visibility. After deploying a GNN-based solution, visibility surged to 70%, dramatically enhancing strategic decision-making.
To fully unlock the value of GNN-based supply chain analytics, organizations must address several critical success factors:
Looking ahead, integrating GNNs with multi-modal Large Language Models (LLMs)—capable of processing text, images, and voice—will unlock even deeper insights. Satellite imagery of logistics hubs, video-based factory inspections, and voice data from supplier interactions can all enhance situational awareness.
Beyond procurement efficiency, GNN-powered analytics can help organizations meet broader goals. For instance, businesses aiming to reduce their carbon footprint can use supply chain visibility to monitor emissions and optimize logistics for sustainability—initiatives well aligned with Hitachi’s values.
Contact us to learn more about Hitachi’s GNN-based supply chain management solution.
Researcher, R&D Division, Hitachi America, Ltd.
Swapnil Bembde is a researcher at Hitachi America R&D, where he focuses on applying advanced AI to enhance supply chain resilience and operational efficiency. Previously, at Hitachi’s R&D division in Japan, he contributed to manufacturing optimization initiatives for OEMs. His work spans industries such as manufacturing, mobility, energy, and finance, helping organizations shift from reactive responses to strategic, data-driven decisions through predictive analytics. His areas of expertise include optimization, machine learning, data processing, and knowledge graph technologies. Swapnil is also a visiting scholar with the Stanford Network Analysis Project (SNAP) in Stanford’s Computer Science Department, where he conducts research with leading scholars. He holds bachelor’s and master’s degrees in electrical engineering from the Indian Institute of Technology, Bombay.
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