Engineering project health monitoring: Application of automatic, real-time analytics to PDM systems

Christopher Snider, James Gopsill, David Jones, Ben Hicks

Research output: Contribution to conferencePaperpeer-review


Modern engineering work, both project-based and operations, is replete with complexity and variety making the effective development of detailed understanding of work underway difficult, which in turn impacts on management and assurance of performance. Leveraging the digital nature of modern engineering work, recent research has demonstrated the capability and opportunity for implementation of broad-spectrum data analytics for development of detailed management information. Of key benefit is that these analytics may be both real-time and automatic. This paper contextualises such analytics with respect to PDM through exploration of the potential for driving the analytics directly from data typically captured within PDM systems. Through review of twenty-five analytics generated from engineering-based digital assets, this paper examines the subset that may be applied to PDM-driven analysis on systems as-is, examines the coverage of such analytics from the perspective of the potential managerial information and understanding that could be inferred, and explores the potential for maximizing the set of analytics driven from PDM systems through capture of a minimal set of supplementary data. This paper presents the opportunity for integration of detailed analytics of engineering work into PDM systems and the extension of their capability to support project management and team performance.

Original languageEnglish
Number of pages11
Publication statusPublished - 08 Dec 2018
Event15th IFIP WG 5.1 International Conference, PLM 2018: Product Lifecycle Management to Support Industry 4.0 - Turin, Italy
Duration: 02 Jul 201804 Jul 2018


Conference15th IFIP WG 5.1 International Conference, PLM 2018
Period02 Jul 201804 Jul 2018


  • Analytics
  • Data analysis
  • Engineering management


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