SCSUS07-BP01 Plan and design for automation for supply chain sustainability - Supply Chain Lens
Implementation guidance

SCSUS07-BP01 Plan and design for automation for supply chain sustainability

Consider optimizing your supply chain sustainability through automation, planning, and designing for automation. Plan for designing completely or partially, where applicable, all the time-consuming tasks to reduce the usage time of a required AWS resource, to collect data for peaks and valleys analysis to feed them into ML models or advanced analysis to enable automatic scalability based on demand.

Provision the AWS Cloud services you need to support your operations based on your business needs.

Desired outcome: Streamline supply chain operations through automation, reducing manual processes, and optimizing resource utilization to align with sustainability goals.

Benefits of establishing this best practice: Enhances operational efficiency, reduces costs, and improves responsiveness to changing business needs.

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

Implementation guidance

Automate the provisioning of the AWS services you need to support your operations to keep the technology infrastructure quickly adjustable in case your business needs change. Automation is a best practice in the context of supply chain and sustainability, as well as implementing predictive horizontal and vertical scaling of compute resources, turning on and off resources based on usage and demand, and forecasting the demand, business peaks and valleys due to seasonality, and promotions for direct and indirect emissions optimization.

Implementation steps

  1. Implement infrastructure as code (IaC) approaches to automate the provisioning and management of AWS services supporting supply chain operations.

  2. Configure predictive scaling policies for compute resources based on historical demand patterns and seasonality analysis.

  3. Establish automated resource scheduling to turn resources on/off based on actual usage patterns and business demand.

  4. Deploy machine learning models to forecast demand patterns and optimize resource allocation for sustainability.

  5. Implement automated monitoring and alerting systems to track resource utilization and sustainability metrics.

  6. Create continuous improvement processes to refine automation strategies based on performance and sustainability outcomes.