A predictive maintenance cost model for CNC SMEs in the era of industry 4.0

  • Kwaku Adu-Amankwa
  • Ashraf K.A. Attia
  • Mukund Nilakantan JanardhananEmail author
  • Imran Patel


Within the subject area of maintenance and maintenance management, authors identified a deficiency in studies focussing on the expected value from adopting predictive maintenance (PdM) techniques for machine tools (MTs). Authors identified no studies focussing on presenting a PdM value analysis or cost model specifically for small-medium enterprises (SMEs) operating computer numerically controlled (CNC) MTs. This paper’s novelty is addressing SMEs’ minimal representation in literature by explanatorily collecting data from SMEs within the focal area via surveys, modelling and analysing datasets, then proposes a cost-effective PdM system architecture for SME CNC machine shops that predicts cost savings ranging from £22,804 to £48,585 over a range of 1–50 CNC MTs maintained on a distributed numerically controlled (DNC) network. It affirms PdM’s tangible value creation for SME CNC machine shops with predicted positive impacts on their MT cost and performance drivers. These exploratory research findings corroborate SMEs pooling together to optimise their CNC MT maintenance cost through the recommended system architecture. Finally, it introduces opportunities for further PdM research taking into account SMEs’ perspective. The paper’s industrial application is confirmed from the surveyed SMEs that demonstrated their current utility of PdM; then anonymous positive feedback on the online dashboard, shared with participant companies, confirmed the research results supported SMEs in considering exploring the path to adapting PdM. It is anticipated that beneficiaries of this research will be maintenance managers, business executives and researchers who seek to understand the expected financial and performance impact of adopting PdM for a MT’s overall life cycle costs.


Industry 4.0 Predictive maintenance Machine maintenance cost Machine tool 



The authors would like to appreciate the colleagues Macvil J. Carvalho and Sumit Dilip Pawale for their assistance with data collection.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Kwaku Adu-Amankwa
    • 1
  • Ashraf K.A. Attia
    • 2
  • Mukund Nilakantan Janardhanan
    • 2
    Email author
  • Imran Patel
    • 2
  1. 1.Department of Design, Manufacture and Engineering ManagementUniversity of StrathclydeGlasgowUK
  2. 2.Department of EngineeringUniversity of LeicesterLeicesterUK

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