The impact of shared data: expediting digital transformation for improved operations and supply

Abstract

In recent years, companies aiming to maintain a strong market position have accelerated their efforts in digital transformation and automation. In the Vietnamese business market, the impact of the pandemic has further expedited and strengthened this process across various sectors, particularly in manufacturing. This study examines the relationship between shared data architecture and the performance of the supply chain by exploring the intermediary role of data-driven digital transformation, the connection between human-robot technology, and intelligent logistics management. The role of shared data in digital transformation has not been widely discussed, and previous research has often focused on technology and robots while overlooking the critical factor of humans. This is the unique aspect of this study. To address these gaps, the research utilizes Vroom-Yetton's decision-making theory and augmented intelligence theory as the foundation. Quantitative methods are employed to test the scale and collect data from manufacturing companies operating in warehouses using AGVs, AMRs, or drones connected to the IoT. A questionnaire was distributed to middle and senior managers in manufacturing companies. The results, comprising 458 samples, were analyzed using SEM with the Smart PLS software. The findings indicate that SDA and DDDT significantly influence OSCP and CbHTR, which are the core of all operational activities in an intelligent supply chain. This research expands the perspective of knowledge management and decision-making theories, while providing new insights for OSCP. It contributes positively to how managers utilize data for decision-making in digital transformation and the application of technology in supply chain operations.