A DECISION SUPPORT AND CONTROL SYSTEM FOR AN EFFICIENT DIGITAL TECHNOLOGY IMPLEMENTATION IN MANUFACTURING

Nowadays there is an increasing pressure on industrial and manufacturing companies to adopt and implement digital technologies. At the same time, however, evidence on the evaluation of their performance and economic benefits is scarce. The combination of this two phenomena generates the need for new decision support systems and decision-making approaches for the adoption and selection of digital technologies. To address the issue, the topic is broken down according to a classification of justification approaches for the adoption of advanced manufacturing technologies: the three categories, economic, analytic and strategic approaches, are linked to an increasing level of technological integration. In terms of strategic approach, a novel methodological framework for the selection of digital technologies is proposed. The framework establishes a link between a company's strategic decision-making and the process of selecting technology, passing through key internal performance-related processes. Within this framework, a multi-criteria decision-making model based on a combination of fuzzy logic and AHP (Analytic Hierarchy Process) is developed. This model not only identifies the optimal individual digital technology but also assesses the best combination of digital technologies. The goal is to harness the true value of digitalization, which stems from the synergy between various technologies and resources. By integrating this model into one of the framework's levels, it successfully merges both strategic and analytical approaches. To further explore the economic aspect, typically associated with standalone technologies, the thesis includes an experiment involving augmented reality technology conducted in a laboratory setting. This technology is tested, compared against a traditional system, and evaluated using various learning curve models applicable for both performance and economic assessments. The analysis results provide new insights into the advantages of this specific technology and highlight specific variables that influence performance, which can be incorporated into future decision support systems and multi-criteria decision-making tools.