Table of Contents

• 1.0 Use Case #1: Transforming Temperature Mapping …

  • 1.1 Intelligent, Automated Dashboards

    • 1.1.1 Temporary Sensor Tracking

    • 1.1.2 Real-Time Fixed Sensor Monitoring

    • 1.2.3 Comparative Sensor Mapping:

  • 1.2 Automated Data Processing & Predictive Analyti …

  • 2.1 AI-Powered URS Generation:

  • 2.2 Automated Test Case Generation

  • 2.3 Intelligent Test Automation

• 3.0 Use Case #3: Open-DCP – Transforming Decision- …

  • 3.1 Open DCP Modules

• 4.0 Use Case #4: Enhancing Industrial Operations w …

1.0 Use Case #1: Transforming Temperature Mapping using (cTM)

Presented by xLM

The Continuous Temperature Mapping (cTM) service from xLM offers a revolutionary approach to temperature management. This innovative solution is engineered to improve regulatory compliance and streamline operations by automating the traditionally labor-intensive temperature mapping process. cTM achieves this by integrating automation, machine learning, and sophisticated data visualization techniques.

1.1 Intelligent, Automated Dashboards

1.1.1 Temporary Sensor Tracking 

Monitors data from strategically placed NFC/RF loggers, displaying key performance indicators such as maximum and minimum temperatures, deviation graphs, and failure prediction analyses.

1.1.2 Real-Time Fixed Sensor Monitoring 

Provides live analysis and detailed data summaries from permanently installed sensors.

1.2.3 Comparative Sensor Mapping: 

Combines data from fixed and temporary sensors using proximity algorithms to ensure the highest accuracy predictions of fixed sensor locations.

1.2 Automated Data Processing & Predictive Analytics: 

Utilizes automated data labeling, pre-processing, and feature engineering to improve predictive analytics for forecasting temperature trends and detecting anomalies, supporting proactive environmental management.

cTM fundamentally transforms the way temperature data is obtained, analyzed, and visualized, reducing errors, increasing confidence in compliance efforts, and accelerating decision-making. This solution represents a significant advancement toward both operational excellence and regulatory assurance in Environmental Monitoring in distribution centers, cold boxes, freezers, etc..

Note: cTM supports RH, Pressure and monitoring of any environmental parameter.

2.0 Use Case #2: Continuous Intelligent Validation (cIV) - Revolutionizing GxP Software Automation

Presented by xLM

cIV is a transformative advancement in GxP test automation, revolutionizing the validation lifecycle through cutting-edge AI solutions. Leveraging modern language models and agentic workflows, cIV provides unmatched efficiency and precision for software validation processes.

2.1 AI-Powered URS Generation:

Leveraging modern language models and agentic workflows, cIV provides unmatched efficiency and precision for software validation processes. Vector databases and integrated Service Desk tools further enhance the efficiency of this process.

This URS agent can generate any URS in a few minutes in your template and delivers an impressively accurate URS. This URS generates will be based on your knowledge base and/or generally available industry standards.

2.2 Automated Test Case Generation

By utilizing advanced and innovative AI frameworks, cIV meticulously develops detailed test cases based on requirements. The system ensures extensive coverage and adaptability to evolving user needs.

This agent takes the requirements presented in a URS and generate detailed test cases to not only ensure traceability but also deliver the input to the Test Automation Agent.

2.3 Intelligent Test Automation

cIV autonomously executes test scripts, simulating user interactions across diverse software environments. Robust validation is ensured through cIV's comprehensive cross-browser compatibility.

cIV elevates testing standards through reductions in manual effort, improvements in accuracy, and the minimization of errors. With its innovative and future-proof architecture, cIV leads the way in automated validation solutions, setting a new industry benchmark.

This agent can execute software test cases on the fly and capture all the evidence while executing. The output of the test execution is presented in a CSV compliant PDF within minutes.

3.0 Use Case #3: Open-DCP – Transforming Decision-Making with AI

Presented by Tobias Ladner, Roche

Open-DCP is an innovative platform that leverages cutting-edge AI to optimize and streamline organizational decision-making. Utilizing advanced algorithms empowers businesses to analyze complex datasets, predict outcomes, and implement efficient, data-driven strategies. Specifically designed by Roche/Genentech, Open-DCP tackles the stringent requirements of regulated pharmaceutical environments, providing a scalable solution for global manufacturing networks.

In the pharmaceutical industry, the crucial need to balance compliance with Good Manufacturing Practices (GMP) and the adoption of Industry 4.0 innovations often presents challenges. Traditional data platforms often prioritize data scientists and engineers, potentially leaving process engineers with inadequate tools in regulated settings. To address this gap, Roche/Genentech developed Open-DCP. This microservice-based browser application empowers process engineers with streamlined deployment, guaranteed compliance, and enhanced data-driven decision-making. Roche/Genentech’s implementation of Open-DCP has yielded substantial improvements.

The platform ensures greater adherence to GxP standards through validated documentation, fully aligning with CFR21 Part 11 requirements. Its scalable design allows seamless operation across multiple manufacturing facilities, effectively managing the complexity of global pharmaceutical networks. Moreover, its open-source nature promotes transparency and collaboration, allowing other companies and regulatory agencies to use and review the system.

Open-DCP demonstrably highlights how open-source platforms can successfully bridge the gap between innovation and compliance within the pharmaceutical industry. By providing process engineers with precisely tailored data analytics and reporting tools, Open-DCP enhances operational efficiency, ensures compliance, and fosters a collaborative approach to manufacturing in the age of Industry 4.0.

3.1 Open DCP Modules

4.0 Use Case #4: Enhancing Industrial Operations with NVIDIA and AVEVA

Presented by David Smith, AVEVA

The collaboration between NVIDIA and AVEVA is presented in this case study, with the objective of revolutionizing industrial operations using Deep Reinforcement Learning (DRL). This partnership combines NVIDIA's advanced computing systems with AVEVA's robust dynamic simulation models to encourage secure, eco-friendly, and autonomous industrial manufacturing operations.

The primary benefits of this collaboration are numerous and significant, encompassing increased operational efficiency, a reduction in environmental impact, substantial cost savings, and enhanced decision-making skills. By leveraging innovative technologies such as high-fidelity digital twins and accelerated computing, the partnership can simulate and optimize various processes.

Furthermore, this joint initiative serves to underscore the transformative potential that artificial intelligence holds within industrial settings. It offers scalable solutions that effectively balance the dual objectives of efficiency and sustainability, demonstrating how technology can be harnessed to create a more sustainable and productive industrial landscape. Through this collaboration, the integration of AI not only enhances operational capabilities but also paves the way for a greener and more efficient future.