The Challenge of Deploying Failure Modes and Effects Analysis in Complex System Applications—Quantification and Analysis
Abstract
:1. Introduction
- Functional Failure Modes and Effects Analysis (Functional FMEA) to evaluate those failures associated with functional requirements of products and systems;
- Design Failure Modes and Effects Analysis (Design FMEA) to analyse those failures associated with design elements;
- Process Failure Modes and Effects Analysis (Process FMEA) to assess the potential failures encountered in manufacturing and assembly processes.
2. Materials and Methods
2.1. Data Preparation
2.1.1. Construction of the Dependent Variable
2.1.2. Construction of the Independent Variables
2.2. Model Specification for Analysis
3. Results
3.1. Descriptive Statistics
3.2. Model Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Variable Name | Symbol | Description | Information Type | Data Source and Remarks |
---|---|---|---|---|
Fault Tree Analysis | FTA_PERF | Performance value coded from Likert-scale | Ordered discrete | Questionnaire |
Functional Flow Boundary Diagram | FFBD_PERF | |||
Analytical Hierarchical Process | AHP_PERF | |||
Quality Function Deployment | QFD_PERF | |||
Knowledge-Based System | KBS_PERF | |||
Define, Analysis, Improve, Recommend, Evaluate and Control | DAIREC_PERF | |||
Axiomatic Design | AD_PERF | |||
Experience Level | EXP | Coded as follows: EXP = 0, if the number of experiences = <9 EXP = 1, if the number of experiences > 9 and = <15 EXP = 2, if the number of experiences > 15 and = <23 EXP = 3, if the number of experiences > 23 and = <30 EXP = 4, if the number of experiences > 30 | Continuous | LinkedIn profiles and face-to-face questions |
Organisational size | SIZE | Coded as follows: SIZE = 0, if the number of employees <=20,000 SIZE = 1, if the number of employees > 20,000 and =<50,000 SIZE = 2, if the number of employees > 50,000 and =<100,000 SIZE = 3, if the number of employees > 100,000 and =<250,000 SIZE = 4, if the number of employees > 250,000 | Annual reports of businesses | |
Industry membership | MEM | Industry membership values coded from participants fields of work | Binary | LinkedIn profiles and face-to-face questions |
Level of challenge to deploy FMEA (dependent variable) | LC | Score variable consisting of the following challenges: Excessive use of resources Applicability Capture interaction failures between system, sub-system and components levels The knowledge gap between design and manufacturing phases Inability to trace risks | Ordered discrete | Questionnaire, composed from multiple questions |
Variable Name | Symbol | Description | Information Type | Data Source and Remarks |
---|---|---|---|---|
Fault Tree Analysis | FTA_m | Taking FTA_m as illustrative to represent all listed variables, the coding as follows: FTA_m = 0, if FTA_PERF = 0 FTA_m = 1, if FTA_PERF = 1 & 2 FTA_m = 2, if FTA_PERF = 3 & 4 FTA_m = 3, if FTA_PERF = 5 | Ordered discrete | Original formed Likert scales |
Functional Flow Boundary Diagram | FFBD_m | |||
Analytical Hierarchical Process | AHP_m | |||
Quality Function Deployment | QFD_m | |||
Knowledge-Based System | KBS_m | |||
Define, Analysis, Improve, Recommend, Evaluate and Control | DAIREC_m | |||
Axiomatic Design | AD_m | |||
Experience Level | EXP_m | Recoded as follows: EXP_m = 0, if EXP = 0 & 1 EXP_m = 1, if EXP = 2 EXP_m = 2, if EXP = 3 EXP_m = 3, if EXP = 4 | Continuous | |
Organisational size | SIZE_m | Recoded as follows: SIZE_m = 0, if SIZE = 0 &1 SIZE_m = 1, if SIZE = 2 SIZE_m = 2, if SIZE = 3 SIZE_m = 3, if SIZE = 4 | ||
Industry membership (Kept same) | MEM | Industry membership values coded from participants fields of work | Binary | LinkedIn profiles and face-to-face questions |
Level of challenge to deploy FMEA (dependent variable) | LC_m | Recoded as follows: LC_m = 0, if LC = 0 & 1 LC_m = 1, if LC = 2 LC_m = 2, if LC = 3 LC_m = 3, if LC = 4 & 5 | Ordered discrete | Questionnaire, composed from multiple questions |
Observation | LC | FTA_ PERF | FFBD_PERF | AHP_PERF | QFD_PERF | KBS_ PERF | DAIREC_PERF | AD_ PERF | EXP | SIZE | MEM |
---|---|---|---|---|---|---|---|---|---|---|---|
Discrete | Discrete | Discrete | Discrete | Discrete | Discrete | Discrete | Discrete | Continuous | Continuous | Binary | |
1 | 4 | 0 | 5 | 0 | 5 | 0 | 0 | 0 | 25 | 43,224 | 1 |
2 | 2 | 5 | 0 | 3 | 4 | 1 | 1 | 0 | 20 | 9500 | 0 |
3 | 4 | 0 | 0 | 3 | 5 | 0 | 0 | 0 | 14 | 30,000 | 1 |
4 | 3 | 4 | 5 | 5 | 4 | 0 | 5 | 0 | 10 | 237,000 | 1 |
5 | 4 | 0 | 5 | 0 | 5 | 4 | 0 | 0 | 16 | 26,004 | 1 |
6 | 4 | 5 | 3 | 0 | 5 | 0 | 4 | 0 | 42 | 9989 | 1 |
7 | 4 | 0 | 5 | 5 | 4 | 5 | 5 | 0 | 8 | 153,000 | 0 |
8 | 4 | 4 | 0 | 0 | 4 | 0 | 0 | 0 | 10 | 137,250 | 1 |
9 | 3 | 5 | 1 | 5 | 4 | 5 | 0 | 0 | 21 | 3205 | 0 |
10 | 5 | 0 | 1 | 5 | 4 | 5 | 0 | 0 | 11 | 61,117 | 0 |
11 | 3 | 0 | 0 | 0 | 4 | 5 | 5 | 0 | 26 | 37,543 | 1 |
12 | 4 | 4 | 0 | 3 | 4 | 3 | 0 | 0 | 11 | 237,000 | 1 |
13 | 5 | 0 | 0 | 4 | 4 | 4 | 0 | 0 | 43 | 54,500 | 1 |
14 | 5 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 20 | 3205 | 0 |
15 | 2 | 5 | 5 | 3 | 4 | 5 | 5 | 0 | 19 | 3205 | 0 |
16 | 3 | 4 | 3 | 0 | 0 | 3 | 0 | 0 | 7 | 105,000 | 0 |
17 | 2 | 4 | 5 | 5 | 1 | 3 | 5 | 0 | 29 | 61,117 | 0 |
18 | 3 | 1 | 4 | 4 | 4 | 5 | 5 | 0 | 21 | 85,000 | 0 |
19 | 4 | 5 | 4 | 3 | 5 | 5 | 0 | 0 | 9 | 9500 | 0 |
20 | 2 | 4 | 5 | 4 | 4 | 3 | 5 | 0 | 8 | 3205 | 0 |
21 | 1 | 5 | 5 | 5 | 1 | 5 | 0 | 5 | 13 | 43,224 | 1 |
22 | 3 | 4 | 0 | 0 | 4 | 3 | 0 | 0 | 8 | 173,000 | 1 |
23 | 3 | 4 | 4 | 0 | 5 | 0 | 0 | 0 | 13 | 54,500 | 0 |
24 | 4 | 0 | 5 | 0 | 5 | 5 | 5 | 0 | 14 | 48,000 | 0 |
25 | 3 | 0 | 5 | 3 | 4 | 4 | 0 | 0 | 26 | 237,000 | 1 |
26 | 2 | 4 | 3 | 3 | 4 | 4 | 4 | 0 | 28 | 237,000 | 1 |
27 | 2 | 4 | 4 | 3 | 4 | 3 | 4 | 0 | 15 | 237,000 | 1 |
28 | 1 | 5 | 4 | 4 | 0 | 5 | 4 | 5 | 11 | 37,543 | 1 |
29 | 1 | 5 | 1 | 5 | 0 | 0 | 5 | 0 | 8 | 61,117 | 0 |
30 | 3 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 15 | 9500 | 1 |
31 | 2 | 5 | 4 | 4 | 4 | 5 | 3 | 0 | 15 | 43,224 | 1 |
32 | 1 | 5 | 5 | 0 | 0 | 5 | 0 | 5 | 20 | 173,000 | 1 |
33 | 1 | 4 | 4 | 3 | 1 | 4 | 3 | 5 | 18 | 153,000 | 0 |
34 | 2 | 2 | 5 | 0 | 4 | 3 | 5 | 0 | 5 | 3205 | 0 |
35 | 2 | 0 | 5 | 4 | 5 | 3 | 2 | 0 | 29 | 211,915 | 1 |
36 | 1 | 5 | 5 | 5 | 0 | 1 | 5 | 5 | 17 | 133671 | 0 |
37 | 3 | 1 | 0 | 0 | 4 | 0 | 0 | 5 | 21 | 237,000 | 1 |
38 | 2 | 2 | 5 | 0 | 4 | 0 | 5 | 0 | 20 | 655,700 | 1 |
39 | 4 | 0 | 4 | 5 | 5 | 0 | 0 | 0 | 27 | 153,000 | 0 |
40 | 3 | 0 | 4 | 0 | 5 | 4 | 3 | 0 | 8 | 61,117 | 0 |
41 | 0 | 5 | 1 | 4 | 0 | 4 | 5 | 5 | 6 | 237,000 | 1 |
- To optimise the dependent variable: LC1.1. Gen LC_m = 0 if LC = 0|LC = 11.2. Replace LC_m = 1 if LC = 21.3. Replace LC_m = 2 if LC = 31.4. Replace LC_m =3 if LC = 4|LC = 5
- To optimise the independent variables2.1. Gen FTA_m = 0 if FTA_PERF = 02.2. Replace FTA_m = 1 if FTA_PERF = 1|FTA_PERF = 22.3. Replace FTA_m = 2 if FTA_PERF = 3|FTA_PERF = 42.4. Replace FTA_m = 3 if FTA_PERF = 52.5. Gen FFBD_m = 0 if FFBD_PERF = 02.6. Replace FFBD_m = 1 if FFBD_PERF = 1|FFBD_PERF = 22.7. Replace FFBD_m = 2 if FFBD_PERF = 3|FFBD_PERF = 42.8. Replace FFBD_m = 3 FFBD_PERF = 52.9. Gen AHP_m = 0 if AHP_PERF = 02.10. Replace AHP_m = 1 if AHP_PERF = 1|AHP_PERF = 22.11. Replace AHP_m = 2 if AHP_PERF = 3|AHP_PERF = 42.12. Replace AHP_m = 3 if AHP_PERF = 52.13. Gen QFD_m = 0 if QFD_PERF == 02.14. Replace QFD_m = 1 if QFD_PERF = 1|QFD_PERF = 22.15. Replace QFD_m = 2 if QFD_PERF = 3|QFD_PERF = 42.16. Replace QFD_m = 3 if QFD_PERF = 52.17. Gen KBS_m = 0 if KBS_PERF = 02.18. Replace KBS_m = 1 if KBS_PERF = 1|KBS_PERF = 22.19. Replace KBS_m = 2 if KBS_PERF = 3|KBS_PERF = 42.20. Replace KBS_m = 3 if KBS_PERF = 52.21. Gen DAIREC_m = 0 if DAIREC_PERF = 02.22. Replace DAIREC_m = 1 if DAIREC_PERF = 1|DAIREC_PERF = 22.23. Replace DAIREC_m = 2 if DAIREC_PERF = 3|DAIREC_PERF = 42.24. Replace DAIREC_m = 3 if DAIREC_PERF = 52.25. Gen AD_m = 0 if AD_PERF = 02.26. Replace AD_m = 1 if AD_PERF = 1|AD_PERF = 22.27. Replace AD_m = 2 if AD_PERF = 3|AD_PERF = 42.28. Replace AD_m = 3 if AD_PERF = 52.29. Gen EXP_m = 0 if EXP = 0|EXP = 12.30. Replace EXP_m = 1 if EXP = 22.31. Replace EXP_m = 2 if EXP = 32.32. Replace EXP_m = 3 if EXP = 42.33. Gen SIZE_m = 0 if SIZE = 0|SIZE = 12.34. Replace SIZE_m = 1 if SIZE = 22.35. Replace SIZE_m = 2 if SIZE = 32.36. Replace SIZE_m = 3 if SIZE = 4
- To run the ordered probit model3.1. Global ylist LC_m3.2. Global xlist FTA_m FFBD_m AHP_m QFD_m KBS_m DAIREC_m AD_m EXP_m SIZE_m MEM3.3. Describe $ylist $xlist3.4. Summarise $ylist $xlist3.5. Tabulate $ylist3.6. Oprobit $ylist $xlist, level (90)
- To obtain the marginal effects at means4.1. Mfx, predict (outcome (0))4.2. Mfx, predict (outcome (1))4.3. Mfx, predict (outcome (2))4.4. Mfx, predict (outcome (3))
- To obtain the predicted probabilities at means5.1. Predict p1, pr outcome (0)5.2. Predict p2, pr outcome (1)5.3. Predict p3, pr outcome (2)5.4. Predict p4, pr outcome (3)5.5. Summarise p1 p2 p3 p45.6. Tabstat p1 p2 p3 p4, by (FTA_m)5.7. Tabstat p1 p2 p3 p4, by (AD_m)
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Mean | Standard Deviation | Minimum | Maximum | FTA_PERF | FFBD_PERF | AHP_PERF | QFD_PERF | KBS_PERF | DAIREC_PERF | AD_PERF | EXP | SIZE | MEM | LC | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FTA_PERF | 2.683 | 2.184 | 0 | 5 | 1.000 | ||||||||||
FFBD_PERF | 3.024 | 2.091 | 0 | 5 | 0.072 | 1.000 | |||||||||
AHP_PERF | 2.439 | 2.086 | 0 | 5 | 0.245 | 0.141 | 1.000 | ||||||||
QFD_PERF | 3.439 | 1.732 | 0 | 5 | −0.524 | −0.065 | −0.276 | 1.000 | |||||||
KBS_PERF | 2.853 | 2.007 | 0 | 5 | 0.035 | 0.209 | 0.225 | −0.154 | 1.000 | ||||||
DAIREC_PERF | 2.268 | 2.292 | 0 | 5 | 0.197 | 0.395 | 0.236 | −0.213 | 0.128 | 1.000 | |||||
AD_PERF | 0.854 | 1.905 | 0 | 5 | 0.337 | 0.089 | 0.124 | −0.685 | 0.132 | 0.032 | 1.000 | ||||
EXP | 1.561 | 1.163 | 0 | 4 | −0.213 | −0.058 | −0.073 | 0.197 | −0.167 | −0.114 | 0.004 | 1.000 | |||
SIZE | 1.780 | 1.275 | 0 | 4 | −0.044 | 0.078 | 0.124 | −0.276 | −0.112 | 0.056 | 0.237 | 0.018 | 1.000 | ||
MEM | 0.536 | 0.505 | 0 | 1 | −0.046 | −0.178 | −0.206 | 0.067 | −0.069 | −0.192 | 0.162 | 0.283 | 0.269 | 1.000 | |
LC | 2.780 | 1.255 | 0 | 5 | −0.600 | −0.303 | −0.245 | 0.689 | −0.162 | −0.466 | −0.599 | 0.172 | −0.174 | −0.046 | 1.000 |
Sample size: 41 Degree of freedom: 9 LR : 57.03 p-value > : 0.0000 Pseudo : 0.4335 Log-likelihood: −37.26 | ||||
Variables | Coefficients | Standard Deviation | Significance | [95% Conf. Interval] |
FTA_PERF | −0.249 | 0.109 | ** | - |
FFBD_PERF | −0.158 | 0.103 | - | - |
AHP_PERF | −0.043 | 0.105 | - | - |
QFD_PERF | 0.466 | 0.204 | ** | - |
KBS_PERF | 0.007 | 0.101 | - | - |
DAIREC_PERF | −0.293 | 0.099 | *** | - |
AD_PERF | −0.477 | 0.182 | *** | - |
EXP | 0.186 | 0.178 | - | - |
SIZE | 0.123 | 0.185 | - | - |
MEM | −0.798 | 0.467 | * | - |
−6.32 | 1.572 | - | −9.425 → −3.226 | |
−2.902 | 1.191 | - | −5.246 → −0.558 | |
−0.604 | 1.169 | - | −2.895 → 1.686 | |
0.704 | 1.161 | - | −1.574 → 2.977 | |
2.355 | 1.157 | - | 0.087 → 4.623 |
Sample size: 41 Degree of freedom: 9 LR : 51.41 p-value > : 0.0000 Pseudo : 0.4599 Log-likelihood: −30.18 | ||||
Variables | Coefficients | Standard Deviation | Significance | [90% Conf. Interval] |
FTA_m | −0.41 | 0.208 | ** | - |
FFBD_m | −0.284 | 0.204 | - | - |
AHP_m | −0.017 | 0.196 | - | - |
QFD_m | 0.927 | 0.36 | ** | - |
KBS_m | −0.062 | 0.203 | - | - |
DAIREC_m | −0.426 | 0.182 | ** | - |
AD_m | −0.71 | 0.288 | ** | - |
EXP_m | 0.007 | 0.237 | - | - |
SIZE_m | 0.136 | 0.271 | - | - |
MEM | −0.471 | 0.49 | - | - |
−2.761 | 1.214 | - | −4.759 → −0.763 | |
−0.603 | 1.154 | - | −2.502 → 1.295 | |
0.725 | 1.139 | - | −1.148 → 2.599 |
Variables | Categories of LC_m for Deploying FMEA in Complex Systems | |||
---|---|---|---|---|
3 | 2 | 1 | 0 | |
FTA_m ** | −7.148 | −9.199 | 14.89 | 1.455 |
FFBD_m | −4.495 | −6.365 | 10.30 | 1.007 |
AHP_m | −0.294 | −0.379 | 0.61 | 0.06 |
QFD_m ** | 16.15 | 20.79 | −33.65 | −3.29 |
KBS_m | −1.084 | −1.395 | −2.226 | −0.221 |
DAIREC_m ** | −7.423 | −9.554 | 15.46 | 1.511 |
AD_m ** | −12.38 | −15.93 | 25.79 | 2.521 |
EXP_m | 0.121 | 0.155 | −0.25 | −0.024 |
SIZE_m | 2.375 | 3.056 | −4.947 | −0.483 |
MEM | −8.443 | −10.15 | 16.92 | 1.669 |
Variables | Value | The Predicted Probabilities for LC_m Categories | |||
---|---|---|---|---|---|
3 | 2 | 1 | 0 | ||
FTA_m | 0 | 65.36 | 26.82 | 7.729 | 0.074 |
1 | 7.575 | 37.59 | 52.63 | 2.196 | |
2 | 22.57 | 26.78 | 39.71 | 10.92 | |
3 | 10.17 | 18.05 | 52.24 | 46.52 | |
AD_m | 0 | 38.54 | 29.49 | 28.14 | 3.818 |
1 | - | - | - | - | |
2 | - | - | - | - | |
3 | 0.787 | 4.893 | 14.52 | 79.79 |
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Alruqi, M.; Baumers, M.; Branson, D.T.; Girma, S. The Challenge of Deploying Failure Modes and Effects Analysis in Complex System Applications—Quantification and Analysis. Sustainability 2022, 14, 1397. https://doi.org/10.3390/su14031397
Alruqi M, Baumers M, Branson DT, Girma S. The Challenge of Deploying Failure Modes and Effects Analysis in Complex System Applications—Quantification and Analysis. Sustainability. 2022; 14(3):1397. https://doi.org/10.3390/su14031397
Chicago/Turabian StyleAlruqi, Mansoor, Martin Baumers, David T. Branson, and Sourafel Girma. 2022. "The Challenge of Deploying Failure Modes and Effects Analysis in Complex System Applications—Quantification and Analysis" Sustainability 14, no. 3: 1397. https://doi.org/10.3390/su14031397