SCPERF02-BP02 Use machine learning capabilities for supply chain applications

AWS Supply Chain unifies data and provides machine learning--powered actionable insights, built-in contextual collaboration, and demand planning.

Desired outcome: High requirement workloads can be made easier using machine learning capabilities. Certain pre-built algorithms can reused to fit your workflow which can save time for building the right solutions.

Benefits of establishing this best practice: Agility, performance, and re-usability.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Use AWS Supply Chain to reduce the heavy lifting of the workloads, which involves deep machine learning skills and time-consuming algorithm development activities.

Implementation steps

1. Evaluate existing supply chain processes to identify opportunities where machine learning can provide value and improve efficiency.

2. Implement AWS Supply Chain to use pre-built machine learning models for demand forecasting and supply planning.

3. Integrate machine learning capabilities with existing supply chain systems to enhance decision-making and automation.

4. Train teams on machine learning tools and best practices to maximize the value of AI-powered supply chain solutions.

5. Monitor machine learning model performance and continuously refine algorithms based on actual business outcomes.

6. Expand machine learning usage to additional supply chain use cases as capabilities and confidence grow.