## 4 Discussions and Conclusions  
In the evolutionary context of Industry 4.0, studies regarding the application of enabling technologies and, in particular, the IoT are useful elements in order to obtain a greater understanding of monitoring the performance of production processes [11]. The OEE indicator is recognized in the scientific literature as a useful KPI to monitor the performance of production processes [13; 14]. Not all the information required to calculate OEE is easy to obtain through an automatic IoT data collection system, so the integration of automated collected data and manually collected data is required; furthermore, this field requires more investigation [18]. The objective of this work was to answer the following research questions: How could plastic injection molding manufacturers collect manual data on machine downtime events, ensuring a high level of data quality? How can modern technologies be used to improve manual data collection? Through an in-depth analysis of the downtime management process in an injection molding manufacturing context, the AS-IS downtime management process, which allows for the description of the information flow as a first step for the application of the BPMN method, was identified. Subsequently, through applying the BPMN method, the characteristics required by the architectural model and, in turn, the TO-BE process were defined. The architectural model and the TO-BE process have been described to improve the completeness and accuracy of data that cannot be collected automatically. The IoT system made up of the telemetry collected by the injection molding machine allows for the calculation of the OEE, but it is not possible to obtain information on the causes of machine downtime and information on the operator's intervention. The architecture for the effective collection of manual data is composed of an intervention request system, an intervention tracking system, a human-system interface, and a monitoring dashboard. Through the intervention request system, it is possible to help the line operator request the intervention of an operator in case of a problem on the production line. In addition, intervention requests are automatically saved from the architectural model, so information about the intervention is not lost. With the traceability system of the interventions, it is possible to identify the operator who intervenes on the machine; in this way, the machine stop time obtained automatically from the telemetry is connected to an operator who perfectly knows the cause of the machine stoppage. It is important to make it mandatory for the operator to log in to the system when they start their intervention (if possible, by locking that machine panel without logging in). The human-system interface is connected with all the data available in the architectural model in real-time; therefore, the system requests an intervention from the operator who is logged in to a unit to enter the cause of  
Figure 9: Dashboard—analysis panel of the causes of stoppages regarding the production unit. the block. The system suggests the most probable cause based on the information available (type of operator, duration of downtime, type of production unit). In this way, the operation was facilitated by inserting the cause, and the outcome of the operation was simple and fast. After manual and automated data collection, the architecture calculates the KPI and displays it in the dashboard, and the dashboard also shows the aggregated time for downtime caused in the selected time range. Combining the different elements of the proposed architectural model (machine telemetry, intervention request system, intervention tracking system, and human-system interface) can enable more effective data collection.