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dependence on internal IT capabilities. Generative AI is constantly evolving and is fast becoming a technology that enables supply chain professionals to solve their business problems using natural language rather than computer code.
AI or machine learning( ML) capabilities become simpler to deploy since they can draw from a single, reliable source of truth. With these technologies in place, predictive analytics alert teams to potential problems, allowing them to reroute shipments or adjust production before minor setbacks become major crises. Simultaneously, prescriptive analytics present options about what to do next, guiding teams towards solutions that have the highest chance of success.
If we take a disruption like the shocking Baltimore bridge collapse of 2024 which closed a major port, a manager with access to unified data via a gateway could use generative AI to work out almost immediately which customers would be affected, allowing fast action to reduce the business impact.
The single view of supply chain data provided by data gateway architecture reduces human error, supports better forecasting, and speeds up decision-making. It is as simple as that.
If organizations adopt it, they will find their supply chain professionals spend less time wrestling with poorly integrated systems. They will be able to make the best data-driven calls that enable a supply chain to adapt to many kinds of volatility. Their actions will be fast, effective, but not furious, ensuring minimal disruption and maximum benefit to the bottom line. Being AI ready is the key foundation for real-time decision intelligence. ■
Mark
Holmes www. intersystems. com / uk
Mark Holmes is Global Head of Supply Chain Market Strategy at InterSystems, where he helps supply chain organisations unify data to improve visibility, agility, and resilience. He works with retailers and logistics providers worldwide to turn real time data and AI into practical improvements in decision making and performance across complex supply chains.
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