###### Abstract  
Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human-system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact. overall equipment effectiveness (OEE); production downtime; internet of things; manual data collection 202313313  
input source, the main effects of Industry 4.0 on the improvement of business processes are as follows: dynamic production lines; the interconnection between machinery, information systems, and people; a networked environment, massive data usage; and machine communications to manage system dynamics [4; 5]. These elements favor the improvement of operational efficiency and productivity, asset utilization, and reductions in downtime [6]. Various works in the literature have thoroughly described how an IoT network is structured [7; 8; 9; 10]. The authors of [6] described in detail the different possible levels for organizing the stages of collection, transmission, processing, and presentation of information. The enabling technologies of Industry 4.0 and, in particular, the IoT have turned out to be useful elements for monitoring the performance of production processes [11]. In the context of monitoring the performance of production plants, many publications in the scientific literature mention the Overall Equipment Effectiveness indicator. [PERSON] et al., 2020 [12] conducted a systematic literature review on the OEE indicator, highlighting that OEE is an emerging topic that can be used as input information for decision making in business; moreover, they underlined that the evolution in the method of calculating the indicators applied to different contexts and the different needs listed in the literature. After analyzing different OEE approaches, they also noted that the OEE indicator is adaptable to different domains by measuring the effectiveness of production equipment and material, economic, and human resources. They underlined that an in-depth study of the process is required to determine the losses, variables, and factors to be included in other OEE approaches.