Industry 4.0: framework and methodology for digital transformation

Purpose: This PhD project aims to conduct in-depth research focused on the development of innovative applications related to Digital Transformation and the utilization of data, now available in greater quantity and higher quality. Industry 4.0 (I4.0) is the main current technology innovation wave, however, its adoption is still in early stages due to many barriers, not always well identified. This slowness and incompleteness in Digital Transformation cope with the lack of complete frameworks and roadmaps that companies can follow and implement. The aim of this thesis is therefore to understand what the potential of I4.0 technologies is, the barriers companies must face, how BPM main paradigms (BPR, TQM, Lean Manufacturing, Six Sigma, Lean Six Sigma) can be applied to digital transform processes, and to draw roadmaps and design robust frameworks to help in closing the gap between theory and practice. Method: To meet the aim, the research is divided into clear logical steps. 1st, an in-depth umbrella literature review was conducted to understand what it has been studied about: the different I4.0 technologies, carefully evaluating potential, limitations and requirements of each of them; the variables that must be considered when a company wants to implement I4.0; how BPM and its main paradigms can be support the Digital Transformation; the impact of I4.0 on Resilience and Sustainability. Once the literature has been studied and categorized, a framework and roadmap have been designed based on BPM applied to I4.0, showing how each paradigm relates with the different variables, highlighting strengths and weaknesses. Finally, the last step of this cyclic approach is the design and realization, when possible, of concrete solutions to face specific problems of companies. This phase is divided in the next sub-phases: understanding relevant data, selecting the best sensors at minimal cost, and collecting data; evaluating the optimal system for data transfer (Bluetooth, mobile SIM cards), assessing the best system for managing Big Data via the Cloud; analysing data using AI and statistical methods, implementing predictive maintenance and creating a Digital Twin; leveraging AI to generate feedback control systems; selecting actuators. The followed methodology is recursive as from each implementation it is possible to improve frameworks and roadmaps, understanding what can be generalized and what is specific for a field. Findings: This study found 11 key variables to evaluate, 5 soft and 6 technical. The interaction of them with BPM paradigms showed how this methodology is very suitable for I4.0 adoption, especially with Lean Six Sigma. Additionally, it was unfolded the great positive impact that Digital Transformation can have on Resilience and Sustainability of plants and processes. The implementations, finally, highlighted the need of companies for starting with small pilots able to have quick returns on investments. Conclusion: I4.0 can bring huge value in every field, not only industry and manufacturing. However, its adoption requires to face many barriers, whose impact depends on the company’s maturity. Starting small is a pivotal choice to digital transform the processes. High costs cope with fear of failure. Therefore, to ensure managerial commitment, projects must have quick return of investment. I4.0 can provide great benefits, but its adoption must be carefully planned. Novelty: The innovation in this study relies on multiple aspects. First, literature misses in relating the different point of view about I4.0 adoption, technological and methodological ones. The idea of considering them, alongside the benefits, enabled to design frameworks and roadmaps which are more useful for companies. Second, about BPM, all the analysed papers evaluate how I4.0 can serve it, not the contrary, resulting in few solutions and instruments to implement those technologies. This work addresses how to exploit BPM paradigms to help companies in digital transform. Finally, the followed recursive approach grants a constant development and improvement of the results.