A comprehensive model for determining technological innovation level in supply chains using green investment, eco-friendly design and customer collaborations factors

Abstract

Technological innovations play a crucial role in designing an effective green supply chain. However, it is crucial to know the factors influencing technological innovation in a green supply chain. Some preconceptions show that technological innovation in a business can be affected by internal and external factors, and therefore there must be correlations between such factors to flourish the technological innovation and, subsequently, the green supply chain. Besides, predicting the technological innovation level in a supply chain can be vital and direct it to the Industry 5.0 goals. In this research, a 3-phased framework will be proposed to predict the Technological Innovation Level of Green Supply Chains. The scope of this research includes Green Investment, Eco-friendly Design and Customer Collaborations. In the 1st phase of the framework, dependent and independent factors considering the scope of the Research will be determined; and then, using statistical data analysis, the weight of factors, which reflects their impact on technological innovation (dependent factor), will be determined. Then, in the 2nd phase, a comprehensive model will be developed and trained. Using the data of supply chains that were gathered in the first phase, the train and test data would be selected. In continuation, the model will be trained and its performance will be evaluated using some metrics. Then, in the last phase (phase 3), the developed model will be used to predict the technological level of supply chains. The outcomes of this research can help top managers of supply chains to predict the level of technological innovation by investing a certain budget in improving the dependent variables. The outcomes demonstrated that Customer Collaboration (0.481), Eco-friendly design (0.419) and Green Investment (0.41) have significant impacts on technological innovation improvement in the studied cases, respectively. Besides, the results showed the superiority of the K-nearest Neighbor algorithm while using the Minkowski distance method and considering 5 neighbors. The findings indicated that the proposed framework could predict Technological Innovation with 0.751 accuracies. The outcomes of this research can be helpful for industry owners to predict the expected technological innovation level of their system by investing a certain budget in green investment, eco-friendly design and customer collaborations in their enterprises.