The background of this paper stems from the uncertainties related to machine down time can have a very high cost to modern manufacturing organizations in the form of, productivity losses, product lead times, and poor-quality low product yields. The objective of this research is to review applicable risk management systems and identify the most suitable system for CNC maintenance service. Recurrent CNC machine malfunctions can result in major economic losses, poor customer service, and loss of business reputation. Many examples of the occurrences of down time disruption due to machine failure and or maintenance issues are prolific throughout modern organizations. These instances cause process volatility, disruptions to productivity, digital incidents throughout the Industrial Internet of things (known as IIoT) process, which are all contributors to CNC machine down time risk. While “just-in-time” and lean concepts may benefit the organization in general, these concepts may contribute to risk during process stoppages. The identification, assessment and management of risk is the focus of this article. A combination of modern methods and techniques are presented for comparison to identify the most relevant method to use in this research. The method identified in this case is the Analytical Hierarchical Process (AHP) which is then used as the as the method of choice for this research to successfully address and manage the consequences of risks associated with CNC machine down time. The results of this research utilize the selected risk management system of AHP analysis and priority matrix methods. This is then simulated in the model of potential occurrences of variable risks. The estimation of prospective risk factor effects is identified and analyzed for the CNC production process. The outcomes of this research have identified the most appropriate method and tailored it to the CNC maintenance service, therefore enabling the user to execute the quantifiable risk assessment and management techniques.
| Published in | American Journal of Mechanical and Industrial Engineering (Volume 6, Issue 1) |
| DOI | 10.11648/j.ajmie.20210601.12 |
| Page(s) | 7-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
CNC, Quality Service, Risk, AHP, Life Cycle, Prediction
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APA Style
Moriarty Kevin. (2021). Risk Assessment of Computer Numerical Control (CNC) Machine Service Quality. American Journal of Mechanical and Industrial Engineering, 6(1), 7-16. https://doi.org/10.11648/j.ajmie.20210601.12
ACS Style
Moriarty Kevin. Risk Assessment of Computer Numerical Control (CNC) Machine Service Quality. Am. J. Mech. Ind. Eng. 2021, 6(1), 7-16. doi: 10.11648/j.ajmie.20210601.12
AMA Style
Moriarty Kevin. Risk Assessment of Computer Numerical Control (CNC) Machine Service Quality. Am J Mech Ind Eng. 2021;6(1):7-16. doi: 10.11648/j.ajmie.20210601.12
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Download@article{10.11648/j.ajmie.20210601.12,
author = {Moriarty Kevin},
title = {Risk Assessment of Computer Numerical Control (CNC) Machine Service Quality},
journal = {American Journal of Mechanical and Industrial Engineering},
volume = {6},
number = {1},
pages = {7-16},
doi = {10.11648/j.ajmie.20210601.12},
url = {https://doi.org/10.11648/j.ajmie.20210601.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20210601.12},
abstract = {The background of this paper stems from the uncertainties related to machine down time can have a very high cost to modern manufacturing organizations in the form of, productivity losses, product lead times, and poor-quality low product yields. The objective of this research is to review applicable risk management systems and identify the most suitable system for CNC maintenance service. Recurrent CNC machine malfunctions can result in major economic losses, poor customer service, and loss of business reputation. Many examples of the occurrences of down time disruption due to machine failure and or maintenance issues are prolific throughout modern organizations. These instances cause process volatility, disruptions to productivity, digital incidents throughout the Industrial Internet of things (known as IIoT) process, which are all contributors to CNC machine down time risk. While “just-in-time” and lean concepts may benefit the organization in general, these concepts may contribute to risk during process stoppages. The identification, assessment and management of risk is the focus of this article. A combination of modern methods and techniques are presented for comparison to identify the most relevant method to use in this research. The method identified in this case is the Analytical Hierarchical Process (AHP) which is then used as the as the method of choice for this research to successfully address and manage the consequences of risks associated with CNC machine down time. The results of this research utilize the selected risk management system of AHP analysis and priority matrix methods. This is then simulated in the model of potential occurrences of variable risks. The estimation of prospective risk factor effects is identified and analyzed for the CNC production process. The outcomes of this research have identified the most appropriate method and tailored it to the CNC maintenance service, therefore enabling the user to execute the quantifiable risk assessment and management techniques.},
year = {2021}
}
TY - JOUR T1 - Risk Assessment of Computer Numerical Control (CNC) Machine Service Quality AU - Moriarty Kevin Y1 - 2021/03/22 PY - 2021 N1 - https://doi.org/10.11648/j.ajmie.20210601.12 DO - 10.11648/j.ajmie.20210601.12 T2 - American Journal of Mechanical and Industrial Engineering JF - American Journal of Mechanical and Industrial Engineering JO - American Journal of Mechanical and Industrial Engineering SP - 7 EP - 16 PB - Science Publishing Group SN - 2575-6060 UR - https://doi.org/10.11648/j.ajmie.20210601.12 AB - The background of this paper stems from the uncertainties related to machine down time can have a very high cost to modern manufacturing organizations in the form of, productivity losses, product lead times, and poor-quality low product yields. The objective of this research is to review applicable risk management systems and identify the most suitable system for CNC maintenance service. Recurrent CNC machine malfunctions can result in major economic losses, poor customer service, and loss of business reputation. Many examples of the occurrences of down time disruption due to machine failure and or maintenance issues are prolific throughout modern organizations. These instances cause process volatility, disruptions to productivity, digital incidents throughout the Industrial Internet of things (known as IIoT) process, which are all contributors to CNC machine down time risk. While “just-in-time” and lean concepts may benefit the organization in general, these concepts may contribute to risk during process stoppages. The identification, assessment and management of risk is the focus of this article. A combination of modern methods and techniques are presented for comparison to identify the most relevant method to use in this research. The method identified in this case is the Analytical Hierarchical Process (AHP) which is then used as the as the method of choice for this research to successfully address and manage the consequences of risks associated with CNC machine down time. The results of this research utilize the selected risk management system of AHP analysis and priority matrix methods. This is then simulated in the model of potential occurrences of variable risks. The estimation of prospective risk factor effects is identified and analyzed for the CNC production process. The outcomes of this research have identified the most appropriate method and tailored it to the CNC maintenance service, therefore enabling the user to execute the quantifiable risk assessment and management techniques. VL - 6 IS - 1 ER -
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