| Due Date |
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|---|---|
| Status | WIP |
| Stakeholders | marie.goavec@syensqo.com, brian.bian@syensqo.com |
| Outcome | |
| Contributors | |
| Responsible | |
This page aims to translate the functional requirements into architecture tangible elements creating a engineering value perspective on the initiative - to assess business capabilities relevance versus architecture complexity.
Architecture Requirements Assessment
| Quality Attribute | Requirement - Architecture Concerns | Architectural Complexity | Business Criticality/Relevance | Business Requirement Item |
|---|---|---|---|---|
| Interoperability | How "AI system" gather info from public & internal sources for scientists real-time evaluation? | HIGH | HIGH | Understanding Experimental Requirements and Information Collection |
| Usability | How "AI system" filter and displays complex data structure and diagrams? | LOW | HIGH | Molecular Modeling and Simulation |
| Usability | How "AI system" supports/makes proposals to scientists on molecular modeling and simulation? | MEDIUM | HIGH | Molecular Modeling and Simulation |
| Usability | How "AI System" monitors digital reactor and formulation workstation processes to ensure accuracy/consistency of sample preparation? | HIGH | HIGH | Molecular Modeling and Simulation |
| Consistency | How "AI System" ensures accuracy/consistency of sample preparation? | MEDIUM | LOW | Execution of Experimental Plan |
| Usability | How "scientists" monitor progress of digital reactor and formulation process in real time? | LOW | LOW | Execution of Experimental Plan |
| Interoperability | How "Central Control System" capture instrument data and promote it to Data Analysis System? | MEDIUM | HIGH | Sample Analysis and Data Processing |
| Interoperability | How "Central Control System" onboard/integrate new instruments? | LOW | MEDIUM | Sample Analysis and Data Processing |
| Consistency | How can "AI System" make recommendations for the experimental scheme to the scientists based on historical data and current results? | HIGH | HIGH | Experimental Optimization and Iteration |
| Interoperability | What types of sensors "AI System" will connect and how it will monitor Environment and instrument parameters in real-time to schedule maintenance (to reduce downtime)? | MEDIUM | LOW | Intelligent Management and Maintenance |
| Safety | How "AI System" automatically checks existing group safety regulations/procedures and evaluates the safety of new instrument/processes/Management of Change to recommend additional safety monitoring parameters? | MEDIUM | HIGH | Safety and Compliance |
| Usability | How "AI System" allows scientists to access experiments database for evaluation? | LOW | LOW | Knowledge Management and Collaborative Work |
| Usability | How "Virtual Assistant" provides suggestions on best practices to scientists? | LOW | HIGH | Safety and Compliance |
| Usability | How "AI System" enhances team collaboration, allows sharing data and offers personalized collaboration tips (recommendations)? | LOW | LOW | Knowledge Management and Collaborative Work |
| Usability | How "Reporting - Visualization System" keeps track on the regular work-flow to create analysis? | LOW | MEDIUM | Automatic Report Generation and Environmental Control |
| Usability | How "AI System" automatically adjusts parameters based on experimental needs for optimal conditions? | MEDIUM | LOW | Intelligent Management and Maintenance |
| Usability | How "Reporting - Visualization System" allows multi-project management? | LOW | MEDIUM | Visualization System and Resource Scheduling |
| Interoperability | How "Voice User Interface (VUI)" allows personas in the lab to interact with Instruments and manage experiments? | HIGH | LOW | Voice User Interface |
Architectural Concerns
- Wet-Lab
- The "Big Data Hub" should be compliant with CyberSec OT/IT constraints.
- The solution must provide extensible and resilient interfaces mechanism for integrating with Syensqo LIMS, ELN and AI systems.
- Foundational or Pre-trained AI/LLM models consumed by Edge or Regular computing must be validated from the Syensqo AI standpoint and CyberSec constraints (+AI Risk).
- Local network capacity must be adequately dimensioned to support the throughput of the number of sensors and their potentially complex and large data types capturing.
- Dry-Lab
- Interoperability between systems (Machine Learning, Modeling-Simulation and LIMS/ELN) becomes critical to achieve efficiently the target seamless workflow.
- Data integration should be taken into consideration to ensure the lineage and data consistency across different systems where users will perform their activities.
- Computer power is also critical for fine-tunning, training and real-time suggestions (here also low-latency network is a sensitive aspect to take into account).
→ It is also a concern to consider that eventually the solution used for Shanghai may not be generalizable or reusable in other regions, outside of China, due to aspects related to:
- Contract and legal for having the same vendors
- Interoperability with Syensqo Application Platform for experiments (LIMS, ELN)
- Data classification and data exchanges between regions
Utility Tree
The value engineering work on the user requirements allows to create such mind-map diagram so to visually capture the architectural significant requirement and its business criticality and complexity to be implementend.
Personas - Profiles
- Syensqo Lab Expert
- AI System
- Scientists
- Researchers
- Lab Technicians
- Virtual Assistant
- Voice User Interface (VUI)
Environments
- Wet Lab:
- Dry Lab:
- OnPrem:
- Cloud Infra:
Components - Building Blocks
- AI System
- Digital Reactor
- Formulation Workstation
- Analytical Instruments
- Data Analysis System
- Central Control System
- Reporting - Visualization System
- Voice User Interface (VUI)
High Level Design Architecture
There are three different views to describe the laboratory environment and the application/infrastructure architecture to accomplish the business needs.
- Shanghai Lab Setup
- High Level Design (LabPC - application context)
- High Level Design (application and OT-IT network architecture: IEC 62443 - zones & conduits)
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- High Level Design (application, OT/IT + workflow context)
References
Smart Solution