Data integration for a centralised structure The PARCview data visualisation tool delivers a single source of truth for all your manufacturing data. It aggregates information from across enterprise- wide ERP, MES, lab, and quality systems, allowing you to visualise and analyse everything in one environment. You can connect via XML, SQL, Web Services, OPCHDA, and more, enabling effortless integration across facilities and data types. The result: actionable insights that drive continuous improvement across your global operations.
MES
ERP
01 0110 0001
0110100
Shift logs
Lab data
DCSs, PLCs, other sources
Data historian
Figure 2 PARCview data visualisation tool
ensures that AI models have the comprehensive, contextualised data they need to perform effectively. As always, a human-first approach to AI adoption is key. When adding industrial AI to your process operations, the goal is to empower your people, not replace them. When operators and engineers define problems, select data, and validate outputs, AI becomes an extension of their expertise rather than an opaque ‘black box’. Conclusion The message is clear: AI success starts with a strong data foundation. This requires intentional planning, expert collaboration, and continuous improvement. SMEs are indispensable to this process, and the right data infrastructure empowers them to make better, faster decisions. Building an AI-ready data ecosystem is not just about adopting new technologies; it is about transforming the way your organisation manages and values data. Start small, identify solvable pain points, and use early wins to build momentum. This approach fosters curiosity, reduces scepticism, and builds a sustainable culture of digital innovation. With a robust historian, centralised integration, contextualised asset management, and open connectivity, your plant can unlock the full potential of AI to drive smarter operations, higher efficiency, and sustained competitive advantage. Empowering the Future of Industrial Intelligence. www.dataPARC.com / dp-info@dataparc.com
Asset-based tag organisation Through dataPARC’s Asset Hub, plants can transform raw tags into meaningful digital assets. These assets represent real-world equipment and processes, complete with descriptive attributes like temperature, flow, or rate. This framework lets users quickly find and compare similar assets, identify underperformance, and build reusable templates for common components such as pumps or boilers. It is a scalable way to organise information, and a critical step toward AI readiness. Visualisation and trend analysis tools AI may reveal opportunities, but humans turn those insights into improvement. dataPARC’s advanced trending and visualisation tools enable teams to analyse historical batches, compare product runs, and isolate process variability. By viewing multiple data sets side by side, engineers can pinpoint inefficiencies, validate model predictions, and identify quick wins that boost productivity immediately. Integration with AI platforms dataPARC acts as the enabler for industrial AI. The open architecture supports API, OPC, and direct data export for seamless hand-off to machine learning models. By connecting real-time data from across the plant into one cohesive ecosystem, it also
www.dataparc.com
67
Powered by FlippingBook