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

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Abstract

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.

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Acknowledgement

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

Author information

Authors and Affiliations

  1. Department of Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow, UK

    Kwaku Adu-Amankwa

  2. Department of Engineering, University of Leicester, Leicester, UK

    Ashraf K.A. Attia, Mukund Nilakantan Janardhanan & Imran Patel

Authors
  1. Kwaku Adu-Amankwa
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  2. Ashraf K.A. Attia
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  3. Mukund Nilakantan Janardhanan
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  4. Imran Patel
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Corresponding author

Correspondence to Mukund Nilakantan Janardhanan.

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Survey questions and response summary

Survey questions and response summary

Q1

Q2

Q3

Q4

1–5 employees

5–10 employees

10–100 employees

100–250 employees

1–5 machines

5–10 machines

10–50 machines

More than 50 machines

Owned

Leased

8 h

16 h

24 h

21%

5%

26%

47%

47%

21%

32%

0%

21%

79%

37%

26%

37%

Q5

Q6

£10,000

£20,000

£30,000

£40,000

£50,000

£60,000

None

Few

Some

Most

All

21%

11%

21%

11%

21%

16%

68%

11%

11%

5%

5%

  1. Q1

    What is the total number of employees in your organisation?

  2. Q2

    How many CNC machines does your company currently operate?

  3. Q3

    Do you currently own your CNC machines or lease them from another vendor?

  4. Q4

    What is the total operating hours of your CNC machines per day?

  5. Q5

    What is your current total annual maintenance costs for CNC machines in £GBP?

  6. Q6

    Are any of your CNC machines use data collection sensors or are internet connected?

Q7.1

Q7.2

Lowest

Low

Moderate

High

Highest

Lowest

Low

Moderate

High

Highest

26%

16%

47%

11%

0%

11%

47%

11%

21%

11%

Q7.3

Q7.4

Lowest

Low

Moderate

High

Highest

Lowest

Low

Moderate

High

Highest

11%

21%

26%

26%

16%

5%

26%

42%

5%

21%

  1. Q7

    On a scale from 1 to 5 (1 being the lowest, and 5 being the highest), how would you rate each of the following?

    1. Q7.1.

      Current maintenance costs of your CNC machines compared to total operations costs

    2. Q7.2.

      Loss of productivity due to maintenance

    3. Q7.3.

      Impact of down time due to machine breakdown

    4. Q7.4.

      Effectiveness of your current maintenance processes

Q8

Q9

Q10

Once weekly

Once every two weeks

Once every month

Once every quarter

Once every year

Corrective

Preventive

Predictive

Reduce maintenance costs

Reduce machine down time

Reduce labour costs

Increase machine reliability

16%

32%

26%

21%

5%

21%

74%

5%

25%

25%

25%

25%

  1. Q8

    On average, how often do you face a machine stop due to a performance issue or breakdown?

  2. Q9

    What is your current maintenance strategy for your CNC machines?

  3. Q10

    What were your primary motives of choosing a predictive maintenance approach (check all that applies)?

Q11

Q12

Q13

1–10%

10–20%

20–30%

30–50%

More than 50%

1–10%

10–20%

20–30%

30–50%

More than 50%

Yes

No

0%

0%

0%

100%

0%

100%

47%

11%

21%

11%

58%

42%

  1. Q11

    On average, what were your maintenance costs per machine as a percentage of total operations costs before using predictive maintenance?

  2. Q12

    On average, what are your current maintenance costs per machine as a percentage of total operations costs after using predictive maintenance?

  3. Q13

    Do you consent that I want to be contacted with a summary of the research results when the research is concluded on September 2018?

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Adu-Amankwa, K., Attia, A.K., Janardhanan, M.N. et al. A predictive maintenance cost model for CNC SMEs in the era of industry 4.0. Int J Adv Manuf Technol 104, 3567–3587 (2019). https://doi.org/10.1007/s00170-019-04094-2

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  • DOI: https://doi.org/10.1007/s00170-019-04094-2

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