The author also points out the challenges of AI, including the need for expertise, security and privacy concerns, and potential biases in the data. Despite these challenges, the author believes that AI/ML can make the vision of an integrated, flexible, and efficient supply chain a reality. However, this requires active buy-in, cost recognition, and continuous attention.
Key takeaways:
- AI can help make supply chains more agile by identifying and remediating technical debt, extending visibility, harmonizing data, processing data, and delivering actionable insights.
- Successful AI initiatives require buy-in from all stakeholders and recognition of the costs, including the complexity of models and the need for computational resources and skilled human managers.
- Challenges to implementing AI in supply chains include the need for in-house expertise, especially around generative AI, concerns about security and privacy, and potential for bias and disruptions in supplies.
- Despite these challenges, the potential benefits of an AI-enabled supply chain, such as heightened responsiveness, flexibility, and efficiency, make it a worthwhile investment.