Lab Digital Twins for Centralising Data and Achieving AI Readiness

Transform your lab with digital twins that centralise data and streamline AI integration. See how real-time insights can prepare your lab for the future of AI.

Lab Digital Twins for Data Centralization and AI Readiness

A global chemical manufacturer operates several laboratories across their facilities, where scientists conduct critical product innovation through various experiments and tests. These labs generate immense volumes of invaluable data, such as chemical compositions, experimental results, process optimisations, and quality control metrics. However, the company's lab data management practices were outdated, with scientists often recording data in paper documents or storing it in fragmented local databases that lack structure and centralisation. Studies have shown that as much as 60% of lab data is rendered inaccessible due to these outdated practices, severely limiting the ability to leverage this knowledge for future research, process improvement, and advanced analytics.

Key Issues
  • Global Chemical Manufacturer operates multiple labs focused on product innovation.
  • Outdated data management practices hinder effective data utilisation
  • Studies reveal that 60% of lab data is inaccessible for future use due to these outdated practices
  • The fragmentation of data across different systems hinders knowledge sharing and innovation
Challenges

The company's primary challenge was the loss of critical knowledge from its laboratories. Inaccessible data made it difficult to conduct comparative analyses, hindered collaboration between global labs, and created inefficiencies in bringing innovations to market. Additionally, the lack of a standardised format for lab data prevented the company from applying advanced AI techniques or benefiting from predictive analytics that could drive faster product innovation.

The company sought a solution to :

  • Automatically ingest and standardise all data from laboratory sources.
  • Create a centralised, machine-readable, and AI-ready data structure.
  • Enable seamless searchability and accessibility across all global labs for faster decision-making.
  • Prepare their data for use in advanced AI applications and predictive modelling.

Solution

IndustryApps proposed a Lab Digital Twin solution using its DataSpace platform to transform how the manufacturer handled lab data. Key elements of the solution included :

Impact

  • Increased Innovation Speed: By eliminating the silos and improving the availability of historical data, the company can now accelerate innovation cycles. Researchers can build on past experiments, reducing redundancy and improving the efficiency of lab operations.
  • AI and Predictive Analytics: With AI-ready data models, the company can now apply advanced AI and predictive analytics to forecast outcomes, identify new product opportunities, and optimise processes. This enables quicker time-to-market and improved product quality.
  • Improved Compliance and Traceability: The standardised data models ensure complete traceability of lab processes, experiments, and results. This not only enhances regulatory compliance but also provides a solid foundation for quality control and auditing.
  • Data Centralisation and Accessibility: The company now has a centralised repository of all lab data, making it accessible from any global location. This allows scientists to easily search and retrieve data, compare results across different labs, and make faster, more informed decisions.

This Chemical Manufacturer transformed its laboratories into data-driven innovation centres with the help of IndustryApps DataSpace. The company now benefits from a future-proof, scalable infrastructure that empowers its teams with interoperable, structured data, enhancing collaboration, speed of innovation, and the ability to harness the full potential of AI technologies.

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Frequently Asked Questions

What is industrial data management, and why is it important?

Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.

Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.

How does an AI co-pilot improve industrial operations?

An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.

An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.

What’s the difference between cloud and on-premise solutions?

Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.

 Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.

How secure is your industrial automation software?

Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.

 Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.

Can your software integrate with my current systems?

Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.

 Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.

How long does it take to implement your solutions?

Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.

 Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.

What benefits does AI-supported data mapping provide?

AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.

AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.

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