The rise of AI in supply chains is transforming the way companies manage and collaborate, with trust and information sharing being critical factors for success. But how exactly does AI improve these two components and ensure a more efficient supply chain?
In the academic paper “Artificial intelligence for supply chain collaboration: implications for information sharing and trust”, Weisz et al. (2024) examine how AI is reshaping the landscape of supply chain collaborations. The paper presents a five-stage framework for AI applications in supply chains and highlights the evolving relationship between trust, information sharing and AI capabilities.
The role of information sharing and trust in AI-driven supply chains
Information sharing is often seen as the backbone of successful supply chain collaboration. When companies share data effectively, it leads to better coordination, faster response times and reduced costs. However, trust plays an equally important role, as companies are more willing to share valuable information when there is a solid foundation of trust.
In the context of supply chains, trust develops over time, enabling deeper collaboration. Weisz et al. emphasise that despite technological advances, "real collaboration problems persist" as building trust is a long-term process. The study also emphasises that many companies continue to avoid strategic collaboration and limit information sharing to a transactional level.
How AI improves information sharing and trust in supply chains
AI has the potential to radically transform collaboration in the supply chain by addressing the challenges of trust and information sharing. Weisz et al. present a framework that categorises the role of AI in improving supply chain collaboration into five stages:
1. Complementary AI applications in supply chains
In the early phases of AI integration, AI complements existing systems. Examples include chatbots and basic automation tools that provide real-time insight into stocks and shipments. Trust is still low at this stage and AI mainly serves as a support tool rather than taking over supply chain management.
2. Supplementary AI applications for the optimisation of supply chains
In this phase, AI begins to support decision-making processes by automating tasks such as data input and analysis. Information sharing becomes more efficient and trust between supply chain partners begins to grow as they rely on AI to predict demand, optimise inventory and suggest solutions based on past data.
3.Coollaborative AI applications to promote cooperation in supply chains
In this phase, AI takes on a more active role by facilitating collaboration through real-time data sharing and predictive analyses. Partners in the supply chain can make joint decisions and optimise inventory and transport planning with the help of AI-driven tools.
4. Autonomous AI applications for efficient supply chain processes
With increasing trust in AI, the systems take over important decision-making processes autonomously. AI automatically shares data, routes shipments and adjusts production plans based on real-time demand estimates. Supply chain partners collaborate on the basis of shared data and the role of AI becomes more prominent in the management of operations.
5. AI replaces traditional systems in supply chain management
In the final phase, AI completely replaces existing systems and automates the entire supply chain process from start to finish. At this stage, trust in AI is high and companies benefit from continuous real-time optimisation, predictive analytics and fully autonomous decision-making systems that dynamically adapt to changes in the supply chain.
Implications for managers when introducing AI in supply chains
The AI Collaboration Framework provides valuable insights for supply chain managers. By understanding these five stages, managers can recognise where their company is on its AI journey and what steps are needed to drive collaboration forward. The paper also emphasises the need for oversight, especially as AI systems become more autonomous. Human oversight is critical to mitigate risks such as biased data sets or algorithmic errors that can lead to costly disruptions.
Furthermore, as AI takes on a greater role in supply chains, managers need to address issues of trust - both interpersonally and towards the AI itself. The transition from personal trust to trust in AI systems requires transparency and traceability. AI systems must be able to clearly explain their decision-making processes to ensure acceptance and build trust among stakeholders.
The future of AI in supply chain collaboration
While the research emphasises the potential of AI to transform supply chains, it also recognises some limitations. For example, the transition from human collaboration to AI-driven systems requires a better understanding of how trust and automation evolve in practice. In addition, the role of human oversight, particularly in high-risk scenarios such as demand forecasting, remains a topic for future research.
Overall, the five-step AI collaboration framework is a practical tool for managers and researchers looking to navigate the complexities of AI-driven collaboration in the supply chain. As AI continues to evolve, companies must remain agile and open to integrating AI capabilities to improve collaboration, optimise processes and ultimately remain competitive in an increasingly data-driven world.
Conclusion
The study by Weisz et al. (2024) illustrates the transformative potential of AI for supply chains, particularly with regard to the exchange of information and building trust between the companies involved. The five-stage framework presented shows how the role of AI is evolving from a supporting function to autonomous control. It becomes clear that the successful use of AI depends not only on technical advances, but also on the ability of companies to build trust in these systems. Despite advancing automation, human oversight remains crucial to minimize risks and optimize collaboration. Overall, companies must remain flexible and transparent to take full advantage of AI in an increasingly data-driven world.
For a detailed discussion of the original research, you can view the full study here .
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