## 3 Results
### Warehouse Process Analysis and User Story Identification
* Effective management of outbound logistics without hindering inbound logistics. * Planning of deliveries and arrivals of materials (including forecasting and reservations). The user requirements are used to define technical requirements. With the purpose of designing a DT using IoT and AI, the technical requirements are aggregated in the following:  
\begin{table}
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* IoT technologies:
* Sensors for real-time tracking: These would be placed on pallets, forklifts, bicycles, and potentially on workers (privacy concerns to be addressed) to capture location data. These sensors should be supported by IoT antennas to connect the wireless device to communications networks. * Proximity sensors: These could be used to detect proximity between objects or people, triggering alerts for potential collisions. This is particularly relevant in the case of promiscuity paths between forklifts and bicycles to reduce the risk of blind spots for the forklift operators. * AI algorithms:
* Search algorithms: To enable efficient search and location functionality for packages and materials. * Routing algorithms: To optimize routes for forklifts and other vehicles, reducing travel time and improving efficiency. * Workload balancing algorithms: To automate task allocation and ensure efficient distribution of work among warehouse operators. * Data analysis and visualization tools: To process and display real-time data from IoT sensors in a meaningful way, allowing for quick decision-making. * Digital twin platform:
* Three-dimensional layout visualization: To provide a visual representation of the warehouse layout, allowing for better spatial understanding and planning. * Integration BIM data with IoT data: To overlay real-time data from IoT sensors onto the BIM model and geolocation, providing a comprehensive view of warehouse operations. * Real-time location tracking display: To show the current location of pallets, vehicles, and personnel within the warehouse. * Task management system: To assign, track, and prioritize tasks for warehouse operators. The solution should be supported for the production line as well as for the forklift utilization. * Communication and collaboration tools: To enable real-time communication and information sharing between workers. * Safety alerts and notifications: To provide audible alerts (and/or visual for forklift operator) for potential hazards, such as proximity to forklifts or unauthorized areas. * Digital twin business intelligence:
* Dashboard visualizations: To display KPIs in an easy-to-understand format. Dashboards must be developed specifically for office users (desktop) and for warehouse workers (mobile and table) to support tasks and data deployments. * Reporting tools: To generate reports on warehouse efficiency, safety, and other relevant metrics. * Predictive analytics: To forecast future warehouse occupancy and potential issues in production based on real-time data and production planning. An analysis of KPIs with the users helps to categorize two main topics of interest to be addressed in warehouse improvement:  
* Efficiency and Optimization KPIs:
* Vehicle turnaround time: The time it takes for a truck to enter the warehouse, unload/load, and exit. * Warehouse utilization rate: The percentage and/or sqm of available warehouse space being effectively utilized. * Production line downtime: The amount of time that production lines are not productive due to lack of materials or components. Inventory accuracy: The degree to which the recorded inventory levels match the actual physical inventory. Non-compliance records (NCRs) due to loss: The number of incidents where materials or components are lost or misplaced. Picking time and cost: The time and resources required to locate and retrieve items from the warehouse. Task allocation and completion time: The time it takes to assign and complete various warehouse tasks. Operator efficiency: A measure of how effectively warehouse operators are performing their assigned tasks.