View a markdown version of this page

LSSUS04-BP02 Use digital twins to optimize resource usage through in silico experimentation - Life Sciences Lens
Implementation guidanceResources

LSSUS04-BP02 Use digital twins to optimize resource usage through in silico experimentation

Implement digital twin technologies to create virtual representations of manufacturing processes that enable in silico experimentation and optimization without consuming physical resources. Use these virtual environments to test different operational scenarios, optimize process parameters, and minimize resource consumption before implementing changes in physical systems. Use simulation capabilities to reduce the need for physical experiments while improving process efficiency and sustainability outcomes.

Desired outcome: Significantly reduce physical experimentation requirements and resource consumption by using digital twins for process optimization, while improving manufacturing efficiency and reducing time-to-market for process improvements.

Common anti-patterns:

  • You rely solely on physical experiments for process optimization without considering digital simulation alternatives.

  • You implement process changes without first testing them in virtual environments.

  • You don't use historical data to improve digital twin accuracy and predictive capabilities.

  • You don't validate digital twin predictions against real-world outcomes to improve model accuracy.

Benefits of establishing this best practice:

  • Reduce physical experimentation costs and resource consumption.

  • Accelerate process optimization cycles and reduce time-to-market for improvements.

  • Minimize material waste and energy consumption during process development.

  • Enable safe testing of extreme operational scenarios without risk to physical equipment.

  • Improve process understanding and predictive capabilities for better decision-making.

  • Support regulatory submissions with comprehensive simulation data and analysis.

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

Implementation guidance

Digital twins in life sciences manufacturing provide unprecedented opportunities to optimize processes while minimizing resource consumption and environmental impact. These virtual representations enable process development teams to explore optimization scenarios that would be costly, time-consuming, or potentially risky to test in physical systems. For example, chromatography process optimization can involve testing hundreds of parameter combinations virtually before implementing the most promising approaches in actual equipment.

The effectiveness of digital twins depends on the quality of underlying data and models. Life sciences manufacturing processes often involve complex biochemical interactions that require sophisticated modeling approaches. However, the investment in creating accurate digital twins pays dividends through reduced physical experimentation, faster optimization cycles, and improved process understanding. Integration with real-time monitoring data keeps digital twins accurate and provides valuable insights throughout the manufacturing lifecycle.

Implementation steps

  1. Identify and prioritize manufacturing processes for digital twin development:

    • Assess processes with high resource consumption or frequent optimization needs.

    • Prioritize critical processes like chromatography, fermentation, and purification.

    • Evaluate data availability and modeling complexity for each process.

    • Use AWS IoT TwinMaker to create digital representations of manufacturing equipment.

  2. Develop comprehensive digital twin models:

    • Create physics-based models using AWS SimSpace Weaver for complex process simulations.

    • Integrate historical process data using Amazon S3 and AWS Glue for data preparation.

    • Use Amazon SageMaker AI to build machine learning models that enhance digital twin accuracy.

    • Implement real-time data integration using AWS IoT Core and Amazon Kinesis.

  3. Establish simulated experimentation capabilities:

    • Create simulation environments for testing different operational scenarios.

    • Implement parameter optimization algorithms using Amazon SageMaker AI.

    • Use AWS Batch for running large-scale simulation experiments.

    • Develop automated experiment design and execution workflows using AWS Step Functions.

  4. Integrate digital twin insights into manufacturing operations:

    • Create dashboards using Quick for visualizing simulation results.

    • Implement automated recommendations based on digital twin optimization results.

    • Use AWS Lambda for real-time process adjustments based on digital twin predictions.

    • Establish feedback loops to continuously improve digital twin accuracy.

  5. Validate and continuously improve digital twin performance:

    • Compare digital twin predictions with actual manufacturing outcomes.

    • Implement continuous learning capabilities using Amazon SageMaker AI.

    • Establish regular model updates and validation cycles.

Resources

Related best practices:

Related documents:

Related examples:

Related tools: