This research explores the impact of automated decision-making and decision-making competence on production efficiency in the manufacturing industry of Saudi Arabia. Additionally, it examines the moderating role of big data and predictive analytics, and problem-solving decision-making on the relationship of automated decision-making and decision-making competence. A quantitative approach was employed, collecting data from 283 productions, line, and shift managers, as well as engineers working in manufacturing industry. A structured questionnaire was used, adopting scales from previous research to measure the core constructs. The data were analyzed using Stata-SEM, with confirmatory factor analysis (CFA) to validate the measurement model and path analysis to test the hypothesized relationships. Findings: The results show that attitudes towards automated decision-making and decision-making competence significantly influence production efficiency. Decision-making competence also mediates the relationship between attitudes towards automated decision-making and production efficiency. Furthermore, big data and predictive analytics, as well as problem-solving decision-making, were found to significantly moderate the relationship of automated decision-making and decision-making competence; enhancing production outcomes. This study contributes to the literature by providing empirical evidence on the role of decision-making competence and the integration of advanced data analytics in boosting production efficiency. It offers practical implications for managers in the manufacturing industry, emphasizing the importance of leveraging technology and enhancing decision-making capabilities to optimize production processes.