| Title |
Forecast Innovative Development Level in Green Supply Chains Using a Comprehensive Fuzzy Algorithm |
| ID_Doc |
31476 |
| Authors |
Delgoshaei, A; Beighizadeh, R; Arffin, MKAB; Leman, ZB; Ali, A |
| Title |
Forecast Innovative Development Level in Green Supply Chains Using a Comprehensive Fuzzy Algorithm |
| Year |
2023 |
| Published |
International Journal Of Fuzzy Systems, 25, 2 |
| DOI |
10.1007/s40815-022-01416-7 |
| Abstract |
Technology advancements are essential to creating a successful green supply chain. Both internal and external features can influence a business's innovative development; thus, there must be relationships between these aspects for Innovative Development to succeed. Additionally, anticipating a supply chain's pattern of Innovative Developments might be crucial and help the business owners have a more perceived perspective about their business technological level future. In the first stage of this research, the correlations between management pledge, environmentally friendly design, and green material usage on the expected innovation level in green supply chains are investigated. Then, a new method for using fuzzy weights of the factors used in the Support Vector Machine algorithm is proposed. Then, using a new model coded that is proposed and coded by Python, the innovation level related to technology in green supply chains will be classified and predicted. The results are compared with Linear Regression, Logistic Regression, Random Forest, Gaussian NB, and Multi-Layer Perceptron. The outcomes demonstrate the superiority of the Support Vector Machine algorithm in terms of achieved maximum score and minimum error. The results of the data analysis showed that, in the cases under study, Top Management Obligations and Responsibilities (0.385), Environmentally friendly Design (0.392), and Green Material Usage (0.443) all had a substantial impact on the advancement of Innovative Developments. The suggested support vector machine model was also evaluated in 30 case studies, demonstrating that the model is capable of accurately forecasting technological advancement in green supply chains (score: 0.81). This paper contributes to the industry owners to allocate a set budget to the use of green materials, Management Obligations and Responsibilities, and Environmentally friendly Design in their businesses, leading them to reach a pre-expected level of Innovative Developments in their system. |
| Author Keywords |
Supply chain management; Technology management; Support vector machine; Environmentally friendly design; Green material usage; Top management |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000894978500001 |
| WoS Category |
Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems |
| Research Area |
Automation & Control Systems; Computer Science |
| PDF |
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