An Efficient Model for NPD Performance Evaluation Using DEMATEL and Fuzzy ANP—Applied to the TFT-LCD Touch Panel Industry in Taiwan
Abstract
:1. Introduction
2. Literature Review
2.1. TFT-LCD Industry in Taiwan
2.2. NPD
2.3. DEMATEL
2.4. FANP
3. Proposed Model for the NDP Performance Evaluation of TFT-LCD Touch Panel Enterprises
3.1. Identifying the Evaluation Criteria of NPD
3.2. Developing the Network Structure
3.2.1. Establish Direct-Relation Matrix
3.2.2. Total-Relation Matrix
3.2.3. Calculate Relevance and Level of Impact
3.2.4. Build Causal Diagram for Evaluation System
3.2.5. Complete the DEMATEL Hierarchical Structure
3.3. Obtaining the Priority Order of the Strategies
- (1)
- Form the network structure with well-defined goal, the aspects and criteria, where the relationship of the external criteria and internal relationship of criteria are determined in the final phase.
- (2)
- Form pair-wise comparison matrices through the scale of one to nine points received from all experts’ responses to the questionnaires.
- (3)
- Obtain the weights and analyze consistency. The priority of the criteria can be compared by the calculation of eigenvectors and eigenvalues.
- (4)
- Create fuzzy positive matrixes. The entries in the pair wise comparison matrixes are transformed into positive triangular fuzzy numbers, known as linguistic variables. As suggested by Buckley [52], the fuzzy positive reciprocal matrix can be defined as Equations (12) and (13).
- (5)
- Combine the determinants of all members of the decision-making team. Geometric average means is used to integrate the fuzzy weight matrixes of experts.
- (6)
- Process the defuzzification to obtain the final sequence order of decision factors. Based on the equation proposed by Chen [53], the closeness coefficient is defined as follows: the purpose is to obtain the center of triangular object, while the center value in fuzzy theory denotes the entire fuzzification collection, converting the values ai, bi, and ci from the fuzzification collection into Bij through Equation (14).
- (7)
- Create super-matrix. Each sub-matrix with priority vectors will be combined into an initial super-matrix. As it may not fit the column stochastic rule, each column matrix will be normalized to form a weighted super-matrix. Finally, the weighted super-matrix is multiplied until reaching Equation (15) with convergence.
4. Case Study
4.1. Identifying the Evaluation Aspects and Criteria of NPD
Aspects | Sub Criteria |
---|---|
Market Assessments (MA) | S11 Product Life Cycle |
S12 Regulatory Certification | |
S13 Validate Goal Market | |
S14 Sales Forecast | |
Customer Demands (CD) | S21 Product Quality Attributes |
S22 Product Pricing | |
S23 After-Sales Service | |
S24 Product Quality Rate | |
Production Requirements (PR) | S31 Manufacturing Capacity |
S32 Equipment Capacity | |
S33 New Product Attributes | |
Quality Criteria (QC) | S41 High and Low Temperature Test |
S42 High Temperature and High Humidity Test | |
S43 High Impact Test | |
S44 Writing Durability |
4.2. DEMATEL
4.2.1. Direct-Relation Matrix
Aspects | Market Assessments | Customer Demands | Product Requirements | Quality Criteria |
---|---|---|---|---|
Market Assessments | 0 | 2.426 | 2.747 | 2.193 |
Customer Demands | 2.011 | 0 | 3.182 | 2.398 |
Production Requirements | 1.347 | 1.557 | 0 | 1.483 |
Quality Criteria | 1.540 | 1.614 | 2.657 | 0 |
Aspects | Market Assessments | Customer Demands | Product Requirements | Quality Criteria |
---|---|---|---|---|
Market Assessments | 0.000 | 0.330 | 0.373 | 0.298 |
Customer Demands | 0.265 | 0.000 | 0.432 | 0.326 |
Product Requirements | 0.177 | 0.212 | 0.000 | 0.202 |
Quality Criteria | 0.203 | 0.219 | 0.361 | 0.000 |
4.2.2. Total-Relation Matrix
Aspects | Market Assessments | Customer Demands | Product Requirements | Quality Criteria |
---|---|---|---|---|
Market Assessments | 0.948 | 1.324 | 1.795 | 1.375 |
Customer Demands | 1.155 | 1.073 | 1.828 | 1.389 |
Product Requirements | 0.777 | 0.884 | 1.006 | 0.925 |
Quality Criteria | 0.929 | 1.042 | 1.489 | 0.917 |
No. | Name of Aspects | ||||
---|---|---|---|---|---|
Market Assessments | 1.276 | 0.0821 | 1.358 | 1.194 | |
Customer Demands | 1.153 | 0.2517 | 1.153 | 1.153 | |
Product Requirements | 0.000 | 1.8933 | 0.000 | 0.000 | |
Quality Criteria | 0.416 | 0.6184 | 0.416 | 0.416 |
4.2.3. Causal Diagram
Criteria | S11 | S12 | S13 | S14 | S21 | S22 | S23 | S24 | S31 | S32 | S33 | S41 | S42 | S43 | S44 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S11 | 0.221 | 0.249 | 0.273 | 0.273 | 0.357 | 0.306 | 0.274 | 0.298 | 0.302 | 0.240 | 0.302 | 0.259 | 0.264 | 0.254 | 0.258 |
S12 | 0.222 | 0.129 | 0.199 | 0.189 | 0.244 | 0.214 | 0.181 | 0.200 | 0.201 | 0.224 | 0.286 | 0.240 | 0.247 | 0.239 | 0.242 |
S13 | 0.276 | 0.205 | 0.177 | 0.242 | 0.287 | 0.264 | 0.219 | 0.255 | 0.248 | 0.272 | 0.334 | 0.306 | 0.315 | 0.305 | 0.308 |
S14 | 0.256 | 0.194 | 0.252 | 0.166 | 0.293 | 0.278 | 0.238 | 0.247 | 0.233 | 0.262 | 0.319 | 0.282 | 0.288 | 0.279 | 0.284 |
S21 | 0.314 | 0.217 | 0.232 | 0.238 | 0.225 | 0.279 | 0.246 | 0.297 | 0.285 | 0.216 | 0.279 | 0.240 | 0.242 | 0.238 | 0.242 |
S22 | 0.291 | 0.227 | 0.259 | 0.249 | 0.315 | 0.215 | 0.240 | 0.282 | 0.271 | 0.266 | 0.302 | 0.269 | 0.277 | 0.266 | 0.270 |
S23 | 0.245 | 0.185 | 0.211 | 0.201 | 0.270 | 0.243 | 0.156 | 0.243 | 0.229 | 0.292 | 0.354 | 0.326 | 0.333 | 0.318 | 0.325 |
S24 | 0.262 | 0.188 | 0.221 | 0.216 | 0.313 | 0.259 | 0.204 | 0.198 | 0.267 | 0.192 | 0.240 | 0.213 | 0.217 | 0.212 | 0.214 |
S31 | 0.245 | 0.180 | 0.194 | 0.189 | 0.275 | 0.215 | 0.195 | 0.241 | 0.176 | 0.242 | 0.265 | 0.228 | 0.234 | 0.225 | 0.224 |
S32 | 0.223 | 0.159 | 0.171 | 0.169 | 0.261 | 0.205 | 0.167 | 0.218 | 0.227 | 0.155 | 0.249 | 0.204 | 0.206 | 0.198 | 0.201 |
S33 | 0.274 | 0.202 | 0.241 | 0.217 | 0.294 | 0.263 | 0.211 | 0.236 | 0.248 | 0.241 | 0.239 | 0.273 | 0.280 | 0.274 | 0.276 |
S41 | 0.253 | 0.172 | 0.180 | 0.173 | 0.263 | 0.208 | 0.183 | 0.203 | 0.222 | 0.209 | 0.273 | 0.179 | 0.259 | 0.219 | 0.221 |
S42 | 0.250 | 0.171 | 0.188 | 0.172 | 0.264 | 0.202 | 0.187 | 0.207 | 0.213 | 0.210 | 0.270 | 0.250 | 0.183 | 0.219 | 0.218 |
S43 | 0.237 | 0.166 | 0.169 | 0.161 | 0.250 | 0.194 | 0.172 | 0.192 | 0.203 | 0.186 | 0.265 | 0.212 | 0.222 | 0.166 | 0.226 |
S44 | 0.230 | 0.160 | 0.163 | 0.160 | 0.243 | 0.188 | 0.173 | 0.188 | 0.201 | 0.184 | 0.254 | 0.204 | 0.211 | 0.219 | 0.163 |
No. | Criteria | Di | Rj | Di + Rj | Di − Rj |
---|---|---|---|---|---|
S11 | Product Life Cycle | 3.908 | 3.131 | 7.039 | 0.777 |
S12 | Regulatory Certification | 1.499 | 0.476 | 1.975 | 1.023 |
S13 | Validate Goal Market | 3.411 | 1.256 | 4.668 | 2.155 |
S14 | Sales Forecast | 3.511 | 1.001 | 4.512 | 2.510 |
S21 | Product Quality Attributes | 3.131 | 3.928 | 7.059 | −0.797 |
S22 | Product Pricing | 3.782 | 1.892 | 5.674 | 1.890 |
S23 | After-Sales Service | 3.178 | 0.997 | 4.175 | 2.181 |
S24 | Product Quality Rate | 1.340 | 1.863 | 3.203 | −0.522 |
S31 | Manufacturing Capacity | 1.954 | 2.310 | 4.264 | −0.356 |
S32 | Equipment Capacity | 0.737 | 1.815 | 2.552 | −1.078 |
S33 | New Product Attributes | 2.663 | 3.992 | 6.655 | −1.329 |
S41 | High Impact Test | 1.048 | 2.673 | 3.721 | −1.626 |
S42 | Writing Durability | 1.251 | 2.739 | 3.990 | −1.488 |
S43 | High and Low Temperature Test | 0.978 | 2.397 | 3.375 | −1.419 |
S44 | High Temperature and High Humidity Test | 0.727 | 2.648 | 3.374 | −1.921 |
4.3. FANP
4.3.1. Forming Pair-Wise Comparison Matrices
4.3.2. Constructing Fuzzy Positive Matrices
4.3.3. Integrating the Opinions of Decision Makers
Aspects | Market Assessments | Customer Demands | Product Requirements | Quality Criteria |
---|---|---|---|---|
Market Assessments | (1.00, 1.00, 1.00) | (1.00, 1.104, 1.17) | (0.438, 0.575, 0.906) | (0.438, 0.635, 1.060) |
Customer Demands | (0.855, 0.906, 1.007) | (1.00, 1.00, 1.00) | (0.492, 0.599, 0.820) | (0.624, 0.743, 1.00) |
Product Requirements | (1.104, 1.739, 2.284) | (1.219, 1.669, 2.034) | (1.00, 1.00, 1.00) | (0.534, 0.673, 1.00) |
Quality Criteria | (0.944, 1.575, 2.284) | (1.00, 1.346, 1.601) | (1.00, 1.486, 1.873) | (1.00, 1.00, 1.00) |
4.3.4. Defuzzification
Aspects | Market Assessments | Customers Demand | Product Requirements | Quality Criteria |
---|---|---|---|---|
Market Assessments | 1.00 | 1.091 | 0.640 | 0.711 |
Customer Demands | 0.920 | 1.00 | 0.637 | 0.789 |
Product Requirements | 1.709 | 1.640 | 1.00 | 0.736 |
Quality Criteria | 1.601 | 1.316 | 1.453 | 1.000 |
4.3.5. Examining the Consistency
Aspects | Market Assessments | Customer Demands | Product Requirements | Quality Criteria | Weights |
---|---|---|---|---|---|
Market Assessments | 0.230 | 0.131 | 0.381 | 0.273 | 0.254 |
Customer Demands | 0.379 | 0.203 | 0.114 | 0.133 | 0.207 |
Product Requirements | 0.210 | 0.392 | 0.330 | 0.395 | 0.332 |
Quality Criteria | 0.181 | 0.274 | 0.174 | 0.200 | 0.207 |
4.3.6. Forming Initial Super-Matrix
No. | MA | CD | PR | QC | S11 | S12 | S13 | S14 | S21 | S22 | S23 | S24 | S31 | S32 | S33 | S41 | S42 | S43 | S44 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MA | 0.157 | 0.239 | 0.239 | 0.239 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
CD | 0.580 | 0.508 | 0.508 | 0.508 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
PR | 0.165 | 0.188 | 0.188 | 0.188 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
QC | 0.098 | 0.065 | 0.065 | 0.065 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S11 | 0 | 0 | 0 | 0 | 0.575 | 0.254 | 0.254 | 0.254 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S12 | 0 | 0 | 0 | 0 | 0.087 | 0.207 | 0.207 | 0.207 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S13 | 0 | 0 | 0 | 0 | 0.184 | 0.332 | 0.332 | 0.332 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S14 | 0 | 0 | 0 | 0 | 0.155 | 0.207 | 0.207 | 0.207 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S21 | 0 | 0 | 0 | 0 | 0.491 | 0.489 | 0.489 | 0.566 | 0.469 | 0.489 | 0.489 | 0.489 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S22 | 0 | 0 | 0 | 0 | 0.167 | 0.138 | 0.138 | 0.212 | 0.181 | 0.138 | 0.138 | 0.138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S23 | 0 | 0 | 0 | 0 | 0.066 | 0.067 | 0.067 | 0.101 | 0.082 | 0.067 | 0.067 | 0.067 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S24 | 0 | 0 | 0 | 0 | 0.277 | 0.306 | 0.306 | 0.121 | 0.268 | 0.306 | 0.306 | 0.306 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
S31 | 0 | 0 | 0 | 0 | 0.236 | 0.183 | 0.272 | 0.154 | 0.352 | 0.214 | 0.258 | 0.183 | 0.388 | 0.183 | 0.183 | 0 | 0 | 0 | 0 |
S32 | 0 | 0 | 0 | 0 | 0.156 | 0.142 | 0.285 | 0.112 | 0.300 | 0.169 | 0.164 | 0.142 | 0.170 | 0.142 | 0.142 | 0 | 0 | 0 | 0 |
S33 | 0 | 0 | 0 | 0 | 0.608 | 0.676 | 0.444 | 0.734 | 0.347 | 0.617 | 0.578 | 0.676 | 0.442 | 0.676 | 0.676 | 0 | 0 | 0 | 0 |
S41 | 0 | 0 | 0 | 0 | 0.324 | 0.323 | 0.249 | 0.308 | 0.229 | 0.308 | 0.312 | 0.341 | 0.341 | 0.341 | 0.310 | 0.341 | 0.341 | 0.341 | 0.341 |
S42 | 0 | 0 | 0 | 0 | 0.267 | 0.266 | 0.223 | 0.195 | 0.343 | 0.277 | 0.279 | 0.331 | 0.331 | 0.331 | 0.273 | 0.331 | 0.331 | 0.331 | 0.331 |
S43 | 0 | 0 | 0 | 0 | 0.219 | 0.228 | 0.281 | 0.275 | 0.246 | 0.209 | 0.215 | 0.171 | 0.171 | 0.171 | 0.226 | 0.171 | 0.171 | 0.171 | 0.171 |
S44 | 0 | 0 | 0 | 0 | 0.190 | 0.183 | 0.247 | 0.222 | 0.181 | 0.206 | 0.194 | 0.158 | 0.158 | 0.158 | 0.191 | 0.158 | 0.158 | 0.158 | 0.158 |
4.3.7. Obtaining the Priority of Total Weight for Evaluation
Aspects | Weights | Criteria | Total Weights | Ranking |
---|---|---|---|---|
Market Assessments | 0.221 | Product Life Cycle | 0.0774 | 5 |
Regulatory Certification | 0.0387 | 10 | ||
Validate Goal Market | 0.0608 | 6 | ||
Sales Forecast | 0.0442 | 7 | ||
Customer Demands | 0.524 | Product Quality Attributes | 0.2531 | 1 |
Product Pricing | 0.0812 | 4 | ||
After-Sales Service | 0.0409 | 9 | ||
Product Quality Rate | 0.1488 | 2 | ||
Product Requirements | 0.183 | Manufacturing Capacity | 0.0439 | 8 |
Equipment Capacity | 0.0298 | 11 | ||
New Product Attributes | 0.1093 | 3 | ||
Quality Criteria | 0.072 | High Impact Test | 0.0244 | 12 |
Writing Durability | 0.0236 | 13 | ||
High and Low Temperature Test | 0.0125 | 14 | ||
High Temperature and High Humidity Test | 0.0114 | 15 |
5. Conclusions
Author Contributions
Conflicts of Interest
Appendix
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Chen, W.-C.; Chang, H.-P.; Lin, K.-M.; Kan, N.-H. An Efficient Model for NPD Performance Evaluation Using DEMATEL and Fuzzy ANP—Applied to the TFT-LCD Touch Panel Industry in Taiwan. Energies 2015, 8, 11973-12003. https://doi.org/10.3390/en81011973
Chen W-C, Chang H-P, Lin K-M, Kan N-H. An Efficient Model for NPD Performance Evaluation Using DEMATEL and Fuzzy ANP—Applied to the TFT-LCD Touch Panel Industry in Taiwan. Energies. 2015; 8(10):11973-12003. https://doi.org/10.3390/en81011973
Chicago/Turabian StyleChen, Wen-Chin, Hui-Pin Chang, Kuan-Ming Lin, and Neng-Hao Kan. 2015. "An Efficient Model for NPD Performance Evaluation Using DEMATEL and Fuzzy ANP—Applied to the TFT-LCD Touch Panel Industry in Taiwan" Energies 8, no. 10: 11973-12003. https://doi.org/10.3390/en81011973