Gregorio, J; Povey, D (2025) NPL’s Data Quality Framework and its Integration with the HVMC: A proposal for Enhancing National Programmes. NPL Report. MS 59

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Abstract

The manufacturing sector is undergoing a significant transformation driven by the adoption of advanced digital tools and technologies, collectively known as Industry 4.0. This shift emphasises the integration of cyber-physical systems, the Internet of Things (IoT), and cloud computing to create smart factories. Central to this transformation is the concept of data quality, which is essential for making informed decisions, ensuring operational efficiency, and maintaining product quality.

This report explores the integration of the National Physical Laboratory’s (NPL) Data Quality Framework with the High Value Manufacturing Catapult (HVMC) and the wider catapult network. It highlights NPL’s offerings and outlines pathways for future collaboration on national programmes. The report focuses on two key work groups: Certification by Analysis (CbA) and Model-Based Enterprise (MBE).

The Certification by Analysis (CbA) committee emphasises the use of digital tools to support and enhance traditional certification processes. It highlights the importance of data quality in virtual testing and certification, ensuring that data-driven decisions are reliable and traceable. The report discusses the development of a data-driven credibility assessment framework, which evaluates the dependability of sources, data, and methodologies, focusing on robustness and uncertainty for data quality and simulation confidence.

The Model-Based Enterprise (MBE) user group focuses on the integration of digital models and data management practices to create a seamless and traceable digital thread throughout the supply chain. The report examines the Connected Model-Based Enterprise Environment (C-MBEE) testbed programme, which aims to enhance digital supply chain integration through software interoperability analysis and data traceability. It discusses the development of vocabulary mapping software and the practical application of hashing algorithms to improve data integrity and traceability.

By adopting Model-Based Engineering (MBD) practices and leveraging frameworks like the C-MBEE testbed programme, manufacturers can significantly improve digital supply chain integration, enhance data traceability, and ensure the integrity of their data. These efforts align with the broader goals of digital transformation and competitive advantage in the manufacturing sector, highlighting the critical importance of robust data management and traceability in modern manufacturing environments [1].

This report provides a comprehensive understanding of how data quality frameworks can support the digital transformation of the manufacturing sector. It underscores the need for high-quality data to drive reliable and traceable decisions, ultimately contributing to the sector's long-term success and sustainability.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	MS 59
Subjects:	Mathematics and Scientific Computing > Modelling
Divisions:	Data Science
Identification number/DOI:	10.47120/npl.MS59
Last Modified:	04 Apr 2025 09:22
URI:	https://eprintspublications.npl.co.uk/id/eprint/10149