A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing
Abstract
:1. Introduction
2. Literature Review
2.1. European Foundation for Quality Management Model
2.2. Fuzzy Best-Worst Method
2.3. Fuzzy Multi-Objective Optimization by Ratio Analysis
3. The Methodology
3.1. Definition of a Finite Set of Criteria
3.2. Definition of a Finite Set of Enterprises
3.3. Definition of Set of Decision Makers
3.4. Modeling of the Existing Uncertainties
- equally important (E1):
- slightly more important (E2):
- medium more important (E3):
- much more important (E4):
- extremely more important (E5):
- very low value (V1):
- low value (V2):
- fairly low value (V3):
- medium value (V4):
- fairly high value (V5):
- high value (V6):
- very high value (V7):
3.5. The Proposed Fuzzy Best-Worst Method
- is TFN that corresponds to the relative importance of the best criterion over the rest criteria
- is TFN that corresponds to the relative importance of the worst criterion over the rest criteria
- is TFN that corresponds to the weight of the best criterion
- is TFN that corresponds to the weight of the worst criterion
- is TFN that corresponds to the weight of the criterion .
3.6. The Proposed Fuzzy Multi-Objective Optimization by Ratio Analysis
4. Case Study
4.1. An application of the proposed Fuzzy Best-Worst Method for Determining the Criteria Weights
4.2. An Application of the Proposed Fuzzy Multi-Objective Optimization by Ratio Analysis
5. Conclusions
- (1)
- Modeling of existing uncertainties based on TFNs,
- (2)
- The relative importance of the EFQM criteria is set as a fuzzy group decision-making problem;
- (3)
- The weight vector of EFQM criteria at the level of each DM is determined by FBWM; from the aspect of practical application, applying FBWM has certain advantages related to the AHP framework;
- (4)
- The aggregated weighted vector of EFQM criteria is given by using a fuzzy geometric mean. The authors believe that the aggregation procedure applied in this research has significantly better characteristics in relation to the aggregation of estimates of DMs because, due to the occurrence of inconsistency in the estimates of DMs, it can be easily determined and the error can be eliminated more quickly;
- (5)
- The SMEs are ranked by using the proposed FMOORA based on the fuzzy reference point; compared to other similar MADMs extended with fuzzy sets theory the proposed FMOORA requires less complex calculation.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AHP | Analytic Hierarchy Process |
BE | Business Excellence |
BEM | Business Excellence Model |
BWM | Best Worst Method |
DM | Decision Makers |
EFQM | European Foundation for Quality Management model |
ELECTRE | Elimination and Choice Translating Reality |
FBO | Fuzzy Best-Ordered Matrix |
FBWM | Fuzzy Best-Worst Method |
FLP | Fuzzy Linear Programming |
FMEA | Failure Mode and Effect Analysis |
FMOORA | Fuzzy Multi-Objective Optimization by Ratio Analysis |
FOW | fuzzy Other-to-Worst Vector |
FWO | Fuzzy Worst Ordered Matrix |
GRIM | Graded Mean Integration Representation |
MADM | Multi-Attribute Decision-Making |
MBNQA | Malcolm Baldrige National Quality Award |
MCDM | Multi-Criteria Decision-Making |
MCOA | Modified Center of Area |
MOORA | Multi-Objective Optimization by Ratio Analysis |
PROMETHEE | The Preference Ranking Organization Method for Enrichment of Evaluations |
TFN | Triangular Fuzzy Number |
TOPSIS | Technique for Order of Preference by Similarity to Ideal Solution |
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MADM | Computational Time | Simplicity | Mathematical Computational | Stability | Information Type |
---|---|---|---|---|---|
MOORA | Very less | Very simple | minimum | good | quantitative |
TOPSIS | Very high | Very critical | maximum | poor | mixed |
AHP | moderate | Moderate critical | moderate | medium | quantitative |
VIKOR | less | simple | moderate | medium | quantitative |
ELECTRE | high | Moderate critical | moderate | medium | mixed |
PROMETHEE | high | Moderate critical | moderate | medium | mixed |
Authors | Number, Type and Domains of Linguistic Terms | Group Decision-Making/Aggregation Method | Criteria Weights/Aggregated Criteria Weights | Consistency Check | Application Domain |
---|---|---|---|---|---|
[28] | 6/TFNs/ [1–9] | - | The proposed procedure for solving FLP [28]/TFNs | Procedure based on self-reliance coefficient and possibility level | Illustrative example |
[29] | 5/TFNs/ [1–4.5] | - | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Illustrative example |
[30] | 5/TFNs/ [1–4.5] | - | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Weighing sub-indicators for finding power plant prob-lems |
[31] | 6/TFNs/ [1–9] | - | method to fully solve FLP with TFNs [32] | Conventional BWM [26] | Maintenance assessment in the hospitals |
[33] | 5/TFNs/ [1–4.5] | - | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Supplier selection |
[34] | 5/TFNs/ [1–4.5] | Yes/- | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Determination criteria weights for sustainable supplier selection problems |
[35] | 5/TFNs/ [1–4.5] | - | Procedure proposed by [28] | Fuzzy Consistency Index [29] | Determine the importance and weight of Fine–Kinney parameters prior to be used in ranking hazards |
[36] | 5,7,9/TFNs/ [1–9] | Yes/- | Procedure proposed by the [28]/mean method, the max-min method, and the method based on consensus degree | Fuzzy Consistency Index [37] | Illustrative example |
[38] | 5/TFNs/ [1–4.5] | - | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Evaluating Driver Behavior Factors Related to Road Safety |
[39] | 5/TFNs/ [1–4.5] | - | mixed approach by [40]/TFNs/crisp | Procedure by [40] | Criteria weights for evaluation of a sustainable credit score system |
[41] | 5/TFNs/ [1–4.5] | Yes/- | Procedure proposed by [28]/precise numbers by GMIR/ averaging method | Fuzzy Consistency Index [29] | Assess the potential environmental impacts of the process of ship recycling |
[42] | 6/TFNs/ [1–5.5] | Yes/- | Procedure proposed by [28]/precise numbers by GMIR/ averaging method | Fuzzy Consistency Index [29] | Determination of criteria weights in the problem evaluate the service level of bike-sharing enterprises |
[43] | -/TFNs/ [0–1] | - | Procedure proposed by [28]/TFNs | Fuzzy Consistency Index [29] | Determination weights of criteria and sub-criteria in the problem selection of locations in the emerging economy for electronic waste |
The proposed model | 9/TFNs/ [1–9] | Yes/- | Procedure proposed by [28]/fuzzy geometric mean/TFNs | Fuzzy Consistency Index [29] | Determination criteria weights for the problem of assessing the quality of the enterprise’s operations |
Authors | Number, Type and Domains of Linguistic Terms | Group Decision Making/ Aggregation Method | The Normalized Fuzzy Decision Matrix Procedure | The Fuzzy Ratio Method | Fuzzy Reference Point | Fuzzy Multiplicative Form | Application Domain |
---|---|---|---|---|---|---|---|
[16] | 5/TFNs/ [0.22–1] | +/fuzzy arithmetic mean | The vector normalization procedure [49] | Defuzzification by MCOA | - | - | Sustainable reverse logistic provider |
[17] | 5/TFNs/ [1–9] | +/fuzzy arithmetic mean | The vector normalization procedure [49] | By applying Euclidean distance | - | - | Course selection |
[18] | 5/TFNs/ [1–9] | - | - | Defuzzification by MCOA | - | - | sustainable supplier selection |
[19] | 5/TFNs/ [1–9] | +/fuzzy arithmetic mean | The vector normalization procedure [49] | Defuzzification by MCOA | - | - | design and fabrication of an automated hammering machine |
[20] | 7/TFNs/ [0–10] | - | The vector normalization procedure [49] | By applying Euclidean distance | - | - | Green supplier selection |
[9] | 7/TFNs/ [0–1] | - | The vector normalization procedure [49] | Defuzzification by MCOA | Defuzzification by MCOA | Defuzzification by center of area | Selection of solar power plant location |
The proposed model | 7/TFNs/ [1–9] | - | - | - | Extended Grzegorzewski’s method [50] | - | Ranking production enterprises |
V5 | V5 | V4 | V6 | V5 | V5 | V4 | |
V5 | V5 | V5 | V5 | V5 | V5 | V6 | |
V6 | V6 | V4 | V5 | V4 | V6 | V5 | |
V7 | V6 | V6 | V7 | V7 | V5 | V7 | |
V5 | V6 | V4 | V5 | V4 | V4 | V5 | |
V6 | V6 | V6 | V4 | V6 | V4 | V6 | |
V4 | V4 | V4 | V4 | V3 | V5 | V5 | |
V5 | V4 | V4 | V5 | V3 | V4 | V7 | |
V6 | V4 | V4 | V3 | V3 | V5 | V5 | |
V6 | V7 | V6 | V6 | V7 | V6 | V7 | |
V7 | V6 | V5 | V5 | V3 | V6 | V7 | |
V5 | V5 | V5 | V6 | V6 | V4 | V5 | |
V6 | V5 | V4 | V5 | V3 | V5 | V4 | |
V6 | V5 | V6 | V6 | V7 | V6 | V7 | |
V4 | V5 | V4 | V5 | V6 | V6 | V7 | |
V6 | V4 | V4 | V5 | V5 | V5 | V4 | |
V6 | V6 | V5 | V5 | V7 | V6 | V7 | |
V6 | V5 | V4 | V5 | V6 | V6 | V6 | |
V6 | V5 | V5 | V6 | V5 | V4 | V7 | |
V6 | V5 | V5 | V6 | V5 | V5 | V6 |
i = 1 | |||||||
i = 2 | |||||||
i = 3 | |||||||
i = 4 | |||||||
i = 5 | |||||||
i = 6 | |||||||
i = 7 | |||||||
i = 8 | |||||||
i = 9 | |||||||
i = 10 | |||||||
i = 11 | |||||||
i = 12 | |||||||
i = 13 | |||||||
i = 14 | |||||||
i = 15 | |||||||
i = 16 | |||||||
i = 17 | |||||||
i = 18 | |||||||
i = 19 | |||||||
i = 20 | |||||||
Rank | |||||||||
---|---|---|---|---|---|---|---|---|---|
0.0114 | 0.0171 | 0.0479 | 0.0450 | 0.0200 | 0.0082 | 0.1050 | 0.1050 | 15–19 | |
0.0114 | 0.0171 | 0.0139 | 0.0600 | 0.0200 | 0.0082 | 0.0450 | 0.0600 | 6–14 | |
0.0086 | 0.0129 | 0.0479 | 0.0600 | 0.0350 | 0 | 0.0600 | 0.0600 | 6–14 | |
0 | 0.0129 | 0 | 0 | 0 | 0.0082 | 0 | 0.0129 | 1 | |
0.0114 | 0.0129 | 0.0479 | 0.0450 | 0.0350 | 0.0279 | 0.0600 | 0.0600 | 6–14 | |
0.0086 | 0.0129 | 0 | 0.1050 | 0.0150 | 0.0279 | 0.0450 | 0.1050 | 15–19 | |
0.0214 | 0.0300 | 0.0479 | 0.1050 | 0.0525 | 0.0082 | 0.0600 | 0.1050 | 15–19 | |
0.0114 | 0.0300 | 0.0479 | 0.0600 | 0.0525 | 0.0279 | 0 | 0.0600 | 6–14 | |
0.0086 | 0.0300 | 0.0479 | 0.1575 | 0.0525 | 0.0082 | 0.0600 | 0.1575 | 20 | |
0.0086 | 0 | 0 | 0.0450 | 0 | 0 | 0 | 0.0450 | 2–5 | |
0 | 0.0129 | 0.0139 | 0.0600 | 0.0525 | 0 | 0 | 0.0600 | 6–14 | |
0.0114 | 0.0171 | 0.0139 | 0.0450 | 0.0150 | 0.0279 | 0.0600 | 0.0600 | 6–14 | |
0.0086 | 0.0171 | 0.0479 | 0.0600 | 0.0350 | 0.0082 | 0.1050 | 0.1050 | 15–19 | |
0.0086 | 0.0171 | 0 | 0.0450 | 0 | 0 | 0 | 0.0450 | 2–5 | |
0.0214 | 0.0171 | 0.0479 | 0.0600 | 0.0150 | 0 | 0 | 0.0600 | 6–14 | |
0.0086 | 0.0300 | 0.0479 | 0.0600 | 0.0200 | 0.0082 | 0.1050 | 0.1050 | 15–19 | |
0.0086 | 0.0129 | 0.0139 | 0.0600 | 0 | 0 | 0 | 0.0600 | 6–14 | |
0.0086 | 0.0171 | 0.0479 | 0.0600 | 0.0150 | 0 | 0.0450 | 0.0600 | 6–14 | |
0.0086 | 0.0129 | 0.0139 | 0.0450 | 0.0200 | 0.0279 | 0 | 0.0450 | 2–5 | |
0.0086 | 0.0171 | 0.0139 | 0.0450 | 0.0200 | 0.0082 | 0.0450 | 0.0450 | 2–5 |
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Petrović, T.; Vesić Vasović, J.; Komatina, N.; Tadić, D.; Klipa, Đ.; Đurić, G. A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing. Axioms 2022, 11, 704. https://doi.org/10.3390/axioms11120704
Petrović T, Vesić Vasović J, Komatina N, Tadić D, Klipa Đ, Đurić G. A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing. Axioms. 2022; 11(12):704. https://doi.org/10.3390/axioms11120704
Chicago/Turabian StylePetrović, Tijana, Jasmina Vesić Vasović, Nikola Komatina, Danijela Tadić, Đuro Klipa, and Goran Đurić. 2022. "A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing" Axioms 11, no. 12: 704. https://doi.org/10.3390/axioms11120704
APA StylePetrović, T., Vesić Vasović, J., Komatina, N., Tadić, D., Klipa, Đ., & Đurić, G. (2022). A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing. Axioms, 11(12), 704. https://doi.org/10.3390/axioms11120704