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The integration of AI into supply chain operations has grown significantly, with its application spanning areas such as demand planning, procurement, process standardisation, and last-mile delivery optimisation. Additionally, its adoption in emerging domains, including sustainability tracking and measurement, has seen remarkable progress, reflecting the technology’s expanding role in enhancing operational efficiency and strategic decision-making. According to reports by EY, AI adoption rates in sustainability initiatives have reached as much as 60%, a milestone reflecting the growing reliance on AI to address contemporary operational challenges.

In line with this development, the last couple of years have been marked by another evolution of AI, and particularly one that has been popularised by OpenAI’s GPT. Renownedly known as generative AI – Gen AI – the technology indeed has revolutionized supply chain operations, streamlining them and positively impacting aspects such as identifying priorities within an organisation’s supply chain strategy, proactively responding to risk events, and driving risk mitigation through early identification of risks and continuous monitoring of improvements in risk profiles within complex multi-tier supply chain networks. Indeed, it can be considered to be a game-changing technology.

What is Gen AI?

Generative AI, broadly defined, focuses on the development of advanced algorithms and models capable of generating synthetic data that closely mirrors real-world data. This technology spans a wide array of applications, with two notable examples making significant contributions to the field: a) StyleGAN and b) OpenAI’s GPT. StyleGAN excels in image generation, using a style-based approach to create highly realistic and diverse images. It allows artists to manipulate specific visual attributes, opening new avenues for creativity in digital art. On the other hand, OpenAI’s GPT is renowned for its ability to generate human-like text with remarkable fluency and coherence, advancing tasks such as question answering, essay writing, and conversational responses.

These examples highlight the growing interest and impact of generative AI, with a recent report by Bandi, et al., 2023 revealing that the global market for generative AI was valued at USD 10.79 billion in 2022. The market is projected to experience significant growth, reaching approximately USD 118.06 billion by 2032, with a compound annual growth rate (CAGR) of 27.02% from 2023 to 2032.

Note. Source: (Hyperight, 2024)

Supply Chain Relationship Management: The Status Quo

With Gen AI revolutionising supply chain operations, it becomes a valid consideration to evaluate its potential in managing relationships within these networks. This question gains significance given the globally interconnected nature of modern supply chains. This, coupled with recent years of heightened volatility and geopolitical tensions, have underscored the fragility of these networks, a reality that has caused critical shortages of food, medicines, or even electric batteries. Simultaneously, there is growing awareness of global supply chains’ exposure to human rights violations and unsustainable environmental practices, to which, policymakers in, for example, the United States and Europe have proposed legislative measures mandating comprehensive supply chain traceability.

One of the significant challenges in addressing concerns related to global supply chains is the lack of comprehensive knowledge about their interdependent connections. Many companies operating within global supply chains struggle with limited visibility beyond their direct relationships. In addition, while various technological innovations, such as electronic product codes, radio frequency identification, and blockchain, have shown some success, their impact has often been constrained to only one or two tiers. This limitation is largely due to many companies’ reluctance to share data. A report by Zheng & Brintrup, 2024 suggests that there is minimal incentive for companies to disclose information about their suppliers with several factors contributing to this, including the belief that such information provides a competitive advantage. Companies often fear that revealing their supply chain data could lead to buyers working directly with suppliers, potentially exposing pricing structures. Additionally, businesses may simply be unwilling to disclose their manufacturing and purchasing practices to buyers.

Gen AI’s Application in Supply Chain Relationship Management

Yet, the use of Gen AI can strengthen inter-organisational relationships by improving communication, personalisation, and collaborative problem-solving across different entities. This, in turn, creates a platform for better goal alignment and more efficient resource sharing, which enhances supply chain coordination. As a result, this approach leads to improved performance outcomes and greater operational efficiency.

One notable application is in supply chain mapping. Traditional methods of mapping supply chain relationships often rely on manual data collection and analysis, which can be time-consuming and prone to errors. Gen AI, leveraging advanced natural language processing (NLP) capabilities, can automatically extract structured supply chain information from unstructured natural text. For instance, it can analyse large datasets, such as trade documents, news articles, and industry reports, to answer complex questions like “Who supplies whom with what from where?”. By identifying and mapping these relationships, Gen AI provides organisations with a clearer picture of their supply chain networks, highlighting key connections and potential bottlenecks.

In addition to mapping, the relational knowledge embedded in pretrained language models (pretrained LMs) serves as a valuable resource for managing supply chain relationships effectively. These models can aggregate data from various sources and analyse it to predict factors such as supplier reliability, financial stability, and alignment with organisational goals. By employing AI-powered insights, procurement teams gain real-time access to consolidated information from extensive databases, market trends, social media insights, and even news reports. For instance, original equipment manufacturers (OEMs) like Ford, Toyota, and General Motors often face challenges in managing thousands of suppliers and navigating the complexities of numerous contracts. However, by leveraging LLMs, one OEM uncovered significant cost-saving opportunities. The model identified price reductions available for exceeding specific volume thresholds, an opportunity previously overlooked by the procurement team due to the overwhelming contract complexities. This discovery led to millions of dollars in procurement savings, demonstrating the transformative potential of LLMs in simplifying contract management and optimising costs.

Supply Chain Mapping -  Source: (Makouala, 2024)

The ability of Gen AI to retrieve and analyse relational knowledge also supports proactive decision-making. By understanding the intricate dependencies within a supply chain, organisations can anticipate potential disruptions and develop strategies to address them. For instance, if a critical supplier is identified as being located in a region prone to geopolitical instability, the organisation can explore alternative suppliers or adjust inventory levels to minimise the impact of potential disruptions. This predictive capability underscores the transformative potential of Gen AI in fostering resilience and adaptability in supply chain networks.

Moreover, the application of Gen AI extends beyond mapping and dependency analysis to include real-time monitoring and communication. By integrating Gen AI tools into supply chain management systems, businesses can monitor changes in supply chain relationships as they occur. Alerts generated by AI systems can inform stakeholders of shifts in supplier performance, new regulatory requirements, or changes in market conditions. This real-time visibility enables organisations to respond swiftly to emerging challenges, ensuring continuity and stability.

Despite its potential, the adoption of Gen AI in Operations and Supply Chain Management (OSCM) faces several significant challenges. Key issues pertain to data-related concerns, such as quality, privacy, and security, alongside the complexities of Gen AI technologies, including model sophistication and reliance on third-party platforms. Gen AI models demand substantial volumes of high-quality data, yet supply chain data is often fragmented, inconsistent, and difficult to access, thereby reducing model accuracy and effectiveness. Additionally, limitations in existing IT infrastructure and a shortage of skilled personnel further complicate the integration of Gen AI into OSCM processes, presenting considerable barriers to its widespread implementation.

The benefits of using Gen AI for managing supply chain relationships are clear. It reduces the reliance on manual processes, enhances accuracy, and provides actionable insights into complex networks. Improved visibility not only strengthens operational efficiency but also supports compliance with regulatory standards and promotes ethical sourcing practices. As global supply chains continue to grow in complexity, the adoption of Gen AI offers a sustainable approach to achieving greater transparency and control. However, challenges are eminent ranging from data-related concerns to imitations in existing IT infrastructure and a shortage of skilled personnel.

In conclusion, Gen AI represents a transformative force in managing supply chain relationships. Its applications in supply chain mapping, retrieval of relational knowledge, and real-time monitoring provide unparalleled opportunities to enhance visibility and address hidden dependencies. By embracing Gen AI, organisations can navigate the challenges of modern supply chains with greater confidence, ensuring resilience and long-term success.

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I corsi APICS, riconosciuti a livello globale, offrono preziose competenze in supply chain management, affrontando anche i temi di AI, blockchain e di altre tecnologie per aumentare la visibilità nella Supply Chain.

I nostri programmi APICS CPIM (Certified in Planning & Inventory Management),  CSCP  (Certified Supply Chain Professional) e CTSC  (Certified in Transformation for Supply Chain), condotti dai nostri Master Instructor, con pluriennale esperienza presso multinazionali, oltre al programma on-line Technology Certificate, supportano le organizzazioni nei programmi di miglioramento della Supply Chain e ti aiutano a sviluppare competenze state-of-the-art nella gestione della supply chain e delle operations.

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Iscriviti oggi stesso e fai il primo passo verso l'ottimizzazione delle tue strategie di supply chain. Contatta Jessica Mariotti per informazioni sui nostri corsi, classi interaziendali e in-house in Italia e Svizzera, agevolazioni Early Bird e iscrizioni. Email: j.mariotti@advanceschool.org -  phone numbers under Contacts