## 4 Discussion and Conclusions
* BIM model generation: The collaborative approach driven by BIM managers and supported by the administration will lead to the generation of Digital Building Logbooks [75] or Digital Twins, which comprise the static data, stored in a geometric and semantic model, and dynamic data [76], generated by IoT sensors integrated in the built assets. * Data exploitation: The digital logbooks, acting as single-data lakes that support the collaborative BIM process, will allow the exploitation of data through single APIs to apply simulations and calculations. Moreover, since the last version of the data models is to be used by any of the actors to perform their activities, and the modifications are built upon the latest previous changes, the knowledge remains consolidated and structured in a way that potentially allows data analysis. These analytical capabilities can improve the process performance by application of machine learning technologies to support the operation, predictive maintenance, and end of life of the building stock in cooperation with existing legacy systems, such as Building Management Systems (BMS), Enterprise Resource Planning (ERPs) systems, and Computerized Maintenance Management Systems (CMMS). The research presented in the current paper delivers, as a result, the methodology for the digitalization of renovation workflows showcased in a facade renovation use case, as one of the set of workflows developed in the pilots in the BIM4 Ren project. The digital workflows themselves are also provided as companion material. As part of the future research, these digital workflows will support the definition of the BIM4 Ren software system architecture and associated tools developed in the project, by means of further exploiting the MVD and IDS. The latter entails a novel mechanism to formalize information needs and its representation to describe the subset of the IFC schema needed to satisfy one or many exchange requirements of the AEC industry in the renovation process. These workflows, which will be revised and enhanced throughout the whole BIM4 Ren project, will eventually incorporate the detailed EIR tables and associated model view definitions according to the approach described in this document and will enable an interoperable and efficient renovation process by means of bringing together the involved stakeholders and software tools. In this regard, the BIM4 Ren project software tools will be adapted to cope with the digital workflows, ensuring that they meet the users' requirements. The workflows will help to digitally implement the renovation project, guiding the use of the software platform or tools, with the ability to support or execute the linked tasks to cover the whole renovation process. The methodology presented will enable an interoperable and efficient renovation process by means of bringing together the involved stakeholders and software tools. The outcomes of this research will be further validated in the BIM4 Ren pilots, where the optimization of the processes will be measured through KPIs, which will systematically allow the comparison of the manual process and the digital automated process performance in terms of efficiency. The measurement of performance in each of the process tasks under a continuous improvement approach, through systematized tasks, will permit the identification of process weak points as areas of improvement for process performance. Moreover, future steps of this research will be focused on mapping the BIM software and tools to the BPMN diagrams to provide guidance to the end user to set up a renovation project according to the selected renovation scenario. It will allow the potential BIM tool developers to adjust the software functionalities strictly to the end users' requirements and orchestrate the tools under a collaborative and integrated perspective. Data curation, A.A., P.E., N.L. and M.W. ; Investigation, A.A., P.E., N.L. and M.W. ; Writing--original draft preparation, A.A., P.E. and N.L. ; Writing--review & editing, A.A., P.E., N.L. and M.W.