Engineering Project Health Monitoring: Application of Automatic, Real-Time Analytics to PDM Systems

  • Chris SniderEmail author
  • James Gopsill
  • David Jones
  • Ben Hicks
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


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.


Data analysis Engineering management Analytics 


  1. 1.
    Cataldo, M., Nambiar, S.: The impact of geographic distribution and the nature of technical coupling on the quality of global software development projects. J. Softw. Evol. Process 24, 153–168 (2012)CrossRefGoogle Scholar
  2. 2.
    Earl, C., Eckert, C., Clarkson, J.: Design change and complexity. In: 2nd Workshop on Complexity in Design and Engineering (2005)Google Scholar
  3. 3.
    Floricel, S., Miller, R.: Strategizing for anticipated risks and turbulence in large-scale engineering projects. Int. J. Proj. Manag. 19(8), 445–455 (2001)CrossRefGoogle Scholar
  4. 4.
    Watson, J.: Keynote address at the University of Bath (2012)Google Scholar
  5. 5.
    Snider, C., Škec, S., Gopsill, J.A., Hicks, B.J.: The characterisation of engineering activity through email communication and content dynamics, for support of engineering project management. Des. Sci. 3 (2017)Google Scholar
  6. 6.
    Snider, C., Gopsill, J.A., Jones, S., Shi, L., Hicks, B.: Understanding engineering projects: an integrated vehicle health management approach to engineering project monitoring. In: Proceedings of the International Conference on Engineering Design, ICED 2015, vol. 3, no. DS 80–03 (2015)Google Scholar
  7. 7.
    Snider, C., Emanuel, L., Gopsill, J.A., Joel-Edgar, S., Hicks, B.J.: Identifying the influences on performance of engineering design and development projects. In: International Conference on Engineering Design, ICED 2017 (2017)Google Scholar
  8. 8.
    Toor, S.-R., Ogunlana, S.O.: Beyond the ‘iron triangle’: stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects. Int. J. Proj. Manag. 28(3), 228–236 (2010)CrossRefGoogle Scholar
  9. 9.
    Baccarini, D., Collins, A.: Critical success factors for projects. In: Surfing the Waves: Management Challenges; Management Solutions (2003)Google Scholar
  10. 10.
    Hill, A., Song, S., Dong, A., Agogino, A.M.: Identifying shared understanding in design using document analysis. In: Proceedings of the 13th International Conference on Design Theory and Methodology (2001)Google Scholar
  11. 11.
    Hansen, M.T.: Knowledge networks: explaining effective knowledge sharing in multiunit companies. Organ. Sci. 13(3), 232–248 (2002)CrossRefGoogle Scholar
  12. 12.
    Schmidt, R., Lyytinen, K., Keil, M., Cule, P.: Identifying software project risks: an international Delphi study. J. Manag. Inf. Syst. 17(4), 5–36 (2001)CrossRefGoogle Scholar
  13. 13.
    Mesihovic, S., Malmqvist, J., Pikosz, P.: Product data management system-based support for engineering project management. J. Eng. Des. 15(4), 389–403 (2004)CrossRefGoogle Scholar
  14. 14.
    Sackett, P.J., Bryan, M.G.: Framework for the development of a product data management strategy. Int. J. Oper. Prod. Manag. 18(2), 168–179 (1998)CrossRefGoogle Scholar
  15. 15.
    Palos, S., Kiviniemi, A., Kuusisto, J.: Future perspectives on product data management in building information modeling. Constr. Innov. 14(1), 52–68 (2014)CrossRefGoogle Scholar
  16. 16.
    Li, J., Tao, F., Cheng, Y., Zhao, L.: Big data in product lifecycle management. Int. J. Adv. Manuf. Technol. 81, 667–684 (2015)Google Scholar
  17. 17.
    Patrashkova-Volzdoska, R., McComb, S., Green, S., Compton, W.: Examining a curvilinear relationship between communication frequency and team performance in cross-functional project teams. IEEE Trans. Eng. Manag. 50(3), 262–269 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Chris Snider
    • 1
    Email author
  • James Gopsill
    • 2
  • David Jones
    • 1
  • Ben Hicks
    • 1
  1. 1.University of BristolBristolUK
  2. 2.Universtiy of BathBathUK

Personalised recommendations