# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
### Methodology  
In this context, an advanced digital strategy is developed to optimize the management of corporate spaces, improve operational efficiency, and reduce costs and environmental impact. The proposed approach uses IoT sensors to assess employee presence or workstation activation, and innovative ML technologies to automate and refine building management [53]. The building manager can operate with greater awareness and effectiveness, thanks to access to timely data and reliable forecasts. This allows more informed decisions to be made about space use and resource management [54]. The developed methodology consists of 4 main phases (Figure 4):  
1. Space analysis via the BIM model: Through the use of IoT sensors, which can be easily installed and configured, it is possible to start the occupancy assessment process. - Use analysis: Evaluation of office space use to identify inefficiencies or cases of overutilization. - Occupancy measurement: Determine the occupancy percentage for each floor, with a detailed map of space use.
2. Space analysis and management (statistics): Using data obtained from a dedicated booking application, through the utilization of BIM-integrated software, facilitates the examination of the actual use of workspaces, with the objective of optimizing their management. The specific objectives are as follows: - Presence forecasting: Estimating building occupancy over the next 30 days based on historical booking data, allowing better maintenance services and the anticipation of space and resource requirements. - Savings analysis: Quantification of potential maintenance cost savings through appropriate operational downsizing. By analyzing space utilization, it is possible to identify areas that can be consolidated or downsized, thus reducing operating costs. - Duration of bookings: Calculation of the average duration of room bookings to optimize space availability. Knowing the typical duration of bookings allows you to better plan the use of meeting rooms, avoiding overlaps and improving efficiency in resource allocation. - Temporal assessment of room use: Examination of meeting room usage by time of day and day of the week and the identification of high- and low-occupancy periods. This provides a better understanding of space utilization patterns, allowing for more flexible and adaptive management.
3. Insight and automated alerts: Provide automatic alerts to the building manager [55] to improve space management [56]. These alerts signal when there is a high probability that a particular floor or the entire building will reach 100% occupancy on a particular day or period, allowing for more efficient planning of the necessary resources.
4. Machine learning: This stage uses ML to make reliable predictions and generate insights [57], going beyond the traditional programming approach [58]. The system learns from the relationships between data and outcomes, identifying patterns and connections that may not be obvious to human intelligence [59]. This 'knowledge discovery' process not only provides useful results, but can also reveal new, previously unknown/ignored/unexpected information.