Integration of Improvement Strategies and Industry 4.0 Technologies in a Dynamic Evaluation Model for Target-Oriented Optimization

• Published in: Applied sciences 2022-02, Vol.12 (3), p.1530
• Main Authors: Gallego-García, Sergio, Groten, Marcel, Halstrick, Johannes
• Format: Article
• Language: English
• Subjects: Automation; Computer applications; Continuous improvement; dynamic maturity assessment model; Evaluation; improvement strategies; Indicators; Industrial applications; Industry 4.0; Innovations; Lean manufacturing; Manufacturing; New technology; Optimization; organizational capabilities; project evaluation; Supply chains; Sustainability; System theory; Technological change; Theory of constraints; Virtual reality
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12031530

Many improvement techniques, methods, and technologies have been the driver of the development of supply chain systems. However, many managers and companies are focused only on new technologies without considering a comprehensive evaluation, and therefore lacking a real need and purpose. As a result, practitioners are often confused with regard of how to integrate improvement strategies and new technologies, as well as how to evaluate their convenience. Thus, this research aims to develop a model for the assessment for each manufacturing capability. This assessment aims to enable a continuous business transformation aligned with organizational goals; thus, a dynamic maturity assessment is chosen. Based on this, the study seeks to provide an integration model for relevant improvement strategies and new technologies that can be applied to any organization. As a result, the paper develops a sequence model, the GUVEI-Model, for the application of Industry 4.0 related technologies for continuous improvement in five different clusters. Furthermore, the research develops an evaluation scheme of optimization alternatives. Based on this conceptual development, a simulation model is built for specific use cases, such as additive manufacturing or virtual reality. The results show how the use cases along the GUVEI-Model application improve relevant indicators significantly, with the first two steps, obtaining and using data, acting as enablers of the three subsequent optimization steps that allow the virtualization, expansion, and improvement of capabilities and a higher impact on the target indicators than the first two steps. Finally, a discussion is presented about the utility of digital twin models for dynamic maturity level assessment and for simulating project improvement impacts before, during, and after their implementation.