Journal of Quality Engineering and Management

Abstract Purpose: This research investigates the impact of technical, relational, and business components of e-business processes on value creation in the digital supply chain, emphasizing the role of information sharing using a neural network modeling approach. The main focus is on the mediating role of e-business capabilities in enhancing the impact of these components on supply chain competitive performance.
Methodology: This research is applied and descriptive-correlational. The research population consists of experts, managers, and employees of manufacturing companies operating in the capital's industrial park. Sampling was carried out using a non-probability, available, and contingent method, and data were collected through a standard questionnaire, the validity and reliability of which were confirmed by the indices AVE> 0.5, CR > 0.7, and α > 0.7. To validate the model and test the hypotheses, the variance-based structural equation modeling method in SmartPLS version 4.0 and the artificial neural network module in SPSS 29 were used.
Findings: After fitting the research model with the variance-based structural equation approach and the multilayer perceptron neural network, the research findings showed that in both approaches, the information sharing variable had the highest impact, and both approaches were able to predict the competitive performance of the digital supply chain. To evaluate the models fitted using the two approaches, the root mean square error was used. The root mean square error values for the multilayer perceptron neural network approach and the variance-based structural equation approach are 0.021 and 0.879, respectively. Therefore, the multilayer perceptron neural network method can accurately predict the competitive performance of the digital supply chain with much lower error and can serve as an optimal model.
Originality/Value: This study presents an integrated model to explain the role of e-business process capabilities in enhancing the competitive performance of the supply chain. The findings offer practical guidance for strategic decision-making and planning in manufacturing firms, particularly within dynamic business environments.