# The Future of Manufacturing and Industry 4.0
## 2 An Overview of Published Articles
The first level, enablers, includes foundational technologies such as digitalization, IoT, AI, big data, and mobile networks, which facilitate data collection and analysis. The second level, value creators, comprises strategic business approaches like platformization, servitization, and the sharing economy, which provide competitive advantages. The third level, outcomes, highlights sustainability as a key factor for ensuring long-term innovation and economic resilience. The study develops a knowledge framework based on five key dimensions to assist manufacturers in evaluating their readiness for DDBMI adoption: infrastructure and technology, organizational capabilities, theoretical foundations, strategic solutions, and evaluation metrics. By underscoring the interconnectedness of these elements, the study advocates for an evidence-based approach in decision-making, strategy formulation, and technology deployment. Its findings contribute to the theoretical understanding of organizational preparedness for digital transformation while providing industry leaders with practical insights for aligning their business models with emerging technological advancements. Future research should focus on empirical validation, industry-specific case studies, and the optimization of DDBMI strategies to enhance their applicability and effectiveness in diverse manufacturing contexts [56]. Cost-effective solutions are essential in industrial applications, leading to the development of a low-cost stationary 3D scanning system for reverse engineering, engineering metrology, and machine diagnostics. This study presents a scanning system that seamlessly integrates with IoT networks, in line with the I4.0 paradigm, to enhance connectivity and automation in manufacturing environments. The proposed system uses an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. The generated point cloud data are processed and transmitted through a wireless communication module, enabling real-time integration with external devices such as cloud storage platforms and diagnostic controllers. Designed with affordability and efficiency in mind, the system incorporates 3D-printed components and microcontroller-based control to reduce costs while maintaining functionality. In addition, the scanner provides high-speed digitization of mechanical parts, with an average measurement uncertainty of 3 mm. Its modular design enables easy integration into IoT-enabled environments, and real-time data processing is enhanced with a moving average filtering algorithm that reduces noise and improves measurement accuracy. Experimental validation demonstrates the system's effectiveness in reconstructing 3D models, although reflective surfaces present challenges due to infrared light distortion. The study suggests that future enhancements should focus on integrating time-of-flight (ToF) sensors, improving scanning resolution, and expanding the system's capacity for larger objects. This research contributes to the development of affordable, high-precision 3D scanning solutions that advance smart manufacturing and 3D printing applications [57]. ML techniques are increasingly used in manufacturing, providing new opportunities for defect prediction and quality control. The next study presents an ML-based approach for predicting short-shot defects in injection molding using transfer learning. While traditional computer-aided engineering (CAE) methods have helped reduce manufacturing defects,they often show discrepancies when compared to real-world conditions. To improve defect prediction accuracy, this research combines neural networks with real-time process data, bridging the gap between simulated and actual manufacturing environments. A Back Propagation Neural Network (BPNN) model was developed and initially trained with CAE simulation data, and then refined with real process data to improve its predictive capabilities.