Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory
Abstract
:1. Introduction
- (1)
- This research provides an analytical framework using a novel hybrid MCDM approach for managers to prioritize the quality improvement program part, and the case application in a Chinese auto factory shows the effectiveness and robustness of the proposed method.
- (2)
- The voice of customers (VOC) information has been taken into consideration for strategic QIP part prioritization, and the rough set-based attribute reduction (RSAR) technique is adopted to establish the criteria.
- (3)
- The combined weighting technique, including subjective and objective items, is employed based on fuzzy DEMATEL and the anti-entropy method, which is embedded into fuzzy VIKOR procedures to obtain the ranking order.
- (4)
- The application of fuzzy-based techniques facilitates managers investigating the evaluation information and implementing the proposed MCDM framework.
2. Quality Improvement Part Selection Based on the Hybrid MCDM Approach
2.1. Hierarchy Criteria Construction
2.1.1. Criteria Development
2.1.2. Criteria Establishment Using the Rough Set-Based Attribute Reduction Technique
2.2. The Hybrid MCDM Framework for QI Part Selection
2.2.1. The Fuzzy VIKOR Approach Integrated with the Combined Weighting Technique
2.2.2. Implementation Steps of the Proposed Method
- Crossover rate: pc = 0.3; mutation rate: pm = 0.05;
- Initial population size: M0 = 70; maximum population size: M = 256;
- The optimal solution not terminating the number of iterations: N = 30;
- Weighting coefficient:
- (1)
- Alternatives will be the compromise solutions if the condition C1 is not satisfied; while is decided by the formula for maximum m (the alternatives ranking are “in closeness”).
- (2)
- Alternatives and will fall into the compromise solution set if the condition C2 is not satisfied.
3. Case Application
3.1. Data Collection and Experiment Parameters
3.2. The Best QI Solution Generation
- (1)
- Q (A7) − Q (A6) = 0.2479 > 0.167, which satisfied the acceptance condition.
- (2)
- The top priority by S, R and Q is always alternative A6.
4. Results and Findings
4.1. Solutions Compared to the Existing Selected QI Part
4.2. The Best Choice Compared to Shemshadi’s and Chaghooshi’s Method
4.3. Sensitivity Analysis
- (1)
- Sensitivity analysis on the decision makers’ weights :
- (2)
- Sensitivity analysis on the relative importance of subjectivity item φ:
- (3)
- Sensitivity analysis on group utility weight v:
5. Conclusions
5.1. Research Originality: Theoretical and Practical Implications
5.2. Limitations and Future Research
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Dimension | Criterion | Description of Criterion | Sources |
---|---|---|---|
Failure dimension | Severity (a1) | The serious effect and influence of the certain failure mode or component with 10 rating scales | [32,33] |
Occurrence (a2) | The failure frequency of parts/components reflected by the R/1000 index | [32,33,34] | |
Detection (a3) | The ability to detect or recognize the failure | [32,33] | |
Cost dimension | Spare part price (a4) | The price of replacement part within the warranty period for the automotive organization belonging to the cost cluster | [15] |
Warranty cost (a5) | Only the good part can lead to the profits; the index is related to the defect rate, production volume and total stage number variables | [31,34,35] | |
Cost per unit (a6) | The expenditure occurring within the warranty period for failure remedy and being the staple constituent of warranty cost | [19,36] | |
Customer voice | Customer complaint (a7) | The occurrences of customer complaints that can be related to a specific non-conformance, reflected by the customer complaint code via the things go wrong (TGW) per 1000 index | [31,34,37] |
Customer satisfaction (a8) | Satisfaction is linked to an evaluation or discrepancy between prior expectations and the actual (perceived) product performance | [8,34,37] | |
Things go wrong (a9) | The description of product or service non-conformance according to the maintenance experience, product failure without maintenance and minor issues | [38] |
U | a1 | a2 | a3 | a4 | a5 | a6 | a7 | a8 | a9 | D |
---|---|---|---|---|---|---|---|---|---|---|
u1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
u2 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
… | … | … | … | … | … | … | … | … | … | … |
un | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
Linguistic Variables of Influence Description | Corresponding TFNs |
---|---|
No Influence (NI) | (0,0,0.25) |
Very Low Influence (VL) | (0,0.25,0.5) |
Low Influence (L) | (0.25,0.5,0.75) |
High Influence (HL) | (0.5,0.75,1) |
Very High Influence (VH) | (0.75,1,1) |
Linguistic Variables for Rating Alternatives | Corresponding TFNs |
---|---|
Very poor (VP) | (0,1,3) |
Poor (P) | (1,3,5) |
Moderate (M) | (3,5,7) |
Good (G) | (5,7,9) |
Very good (VG) | (7,9,10) |
SA1 | SA2 | SA3 | SA4 | SA5 | SA6 | SA7 | SA8 | SA9 | ||
---|---|---|---|---|---|---|---|---|---|---|
D1 | 0.20 | 0.40 | 0.15 | 0.15 | 0.15 | 0.15 | 0.35 | 0.35 | 0.10 | |
D2 | 0.20 | 0.15 | 0.40 | 0.15 | 0.15 | 0.15 | 0.10 | 0.10 | 0.10 | |
D3 | 0.20 | 0.15 | 0.15 | 0.40 | 0.15 | 0.15 | 0.10 | 0.10 | 0.10 | |
D4 | 0.20 | 0.15 | 0.15 | 0.15 | 0.40 | 0.15 | 0.35 | 0.10 | 0.35 | |
D5 | 0.20 | 0.15 | 0.15 | 0.15 | 0.15 | 0.40 | 0.10 | 0.35 | 0.35 |
SB1 | SB2 | SB3 | SB4 | SB5 | SB6 | SB7 | SB8 | SB9 | SB10 | SB11 | |
---|---|---|---|---|---|---|---|---|---|---|---|
φ | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
1 − φ | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 | 0 |
SC1 | SC2 | SC3 | SC4 | SC5 | SC6 | SC7 | SC8 | SC9 | SC10 | SC11 | |
---|---|---|---|---|---|---|---|---|---|---|---|
v | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
1 − v | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 | 0 |
Indicator | A1 | A2 | A3 | A4 | A5 | A6 | A7 |
---|---|---|---|---|---|---|---|
S | 0.4069 (3) | 0.7935 (7) | 0.4574 (4) | 0.7055 (6) | 0.5150 (5) | 0.2177 (1) | 0.3457 (2) |
R | 0.1660 (5) | 0.1709 (6) | 0.1797 (7) | 0.1550 (3) | 0.1658 (4) | 0.0931 (1) | 0.1168 (2) |
Q | 0.5854 (3) | 0.9494 (7) | 0.7081 (5) | 0.7811 (6) | 0.6778 (4) | 0 (1) | 0.2479 (2) |
Method/Indicator | A1 | A2 | A3 | A4 | A5 | A6 | A7 | |
---|---|---|---|---|---|---|---|---|
Proposed method | Q | 0.5854 | 0.9494 | 0.7081 | 0.7811 | 0.6778 | 0 | 0.2479 |
Rank | 3 | 7 | 5 | 6 | 4 | 1 | 2 | |
Shemshadi’s method | Q | 0.6722 | 0.8894 | 0.6880 | 0.7960 | 0.7666 | 0 | 0.2348 |
Rank | 3 | 7 | 4 | 5 | 6 | 1 | 2 | |
Chaghooshi’s method | Q | 0.5016 | 0.9842 | 0.7118 | 0.7570 | 0.5929 | 0 | 0.2474 |
Rank | 3 | 7 | 5 | 6 | 4 | 1 | 2 |
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Share and Cite
Zhou, F.; Wang, X.; Lin, Y.; He, Y.; Zhou, L. Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory. Sustainability 2016, 8, 559. https://doi.org/10.3390/su8060559
Zhou F, Wang X, Lin Y, He Y, Zhou L. Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory. Sustainability. 2016; 8(6):559. https://doi.org/10.3390/su8060559
Chicago/Turabian StyleZhou, Fuli, Xu Wang, Yun Lin, Yandong He, and Lin Zhou. 2016. "Strategic Part Prioritization for Quality Improvement Practice Using a Hybrid MCDM Framework: A Case Application in an Auto Factory" Sustainability 8, no. 6: 559. https://doi.org/10.3390/su8060559