###### Abstract
[PERSON] and [PERSON] [13] analyzed different types of data collection strategies and underlined that in calculating OEE, it is important to consider machines operating in a linked and complex environment. [PERSON] et al. [14] proposed a revision of the OEE indicator for multi-product production system contexts; they proposed the multiproduct production system effectiveness (MSPE) indicator. [PERSON] and [PERSON] [15] analyzed the ISO22400 OEE indicators and proposed a classification of equipment states that grant the standard consistency with the established OEE expression. In monitoring performance through using the OEE indicator, it is essential to pay due attention to the precise analysis of the causes of plant shutdowns [16]. The effectiveness of the investment efforts to improve the efficiency of the production plants is linked to the monitoring of the use of shutdowns of the same; therefore, the collection of precise information relating to the causes of the shutdowns of the plants demonstrates strong potential to maintain business quality within limited expenditure and time periods [17]. Further investigations on this topic are required [18]. For this purpose, [PERSON] [PERSON] [PERSON] [19] proposed a human-machine interface for manual data collection based on a form. [PERSON] et al. [20] designed a Standardized Supervisory Control and Data Acquisition (SCADA) for real-time OEE indicator calculation and manual plant shutdown data collection. [PERSON] et al. [21] designed an application to collect data on disruptions in the manual assembly of complex production and system (CoPS) and investigated the operator's intention to use this software in a single case study. Furthermore, they [22] suggest that an efficient human-machine interaction design favors effective disruption management. [PERSON] et al. [23] highlighted that it is a further research opportunity to develop methodologies and approaches to evaluate and measure the consequences and impacts of human performance on production systems. These impacts should be considered in terms of system availability and reliability (for example, frequency of maintenance interventions or duration of intervention times). The information related to the interventions of the operators also falls within the topic of manual data collection. Within the sector of plastic manufacturing, injection molding is one of the most important manufacturing methods. About one-third of all thermoplastic materials are injection-molded [24]. The large diffusion of injection molding continuously requires the development and implementation of new solutions to improve efficiency and reduce the economic and environmental costs of this manufacturing process. Calculating the OEE in injection molding production contexts has already been proposed in the literature in the past. [PERSON] et al. [25] estimated the total production downtime for each plastic production line per shift and developed an algorithm for uptime maximization; moreover, they used the OEE indicator to highlight the effectiveness of the algorithm but did not study any ways to improve the OEE accuracy estimation. Despite the widespread use of OEE in the literature to evaluate the performance of production plants in various production sectors, including injection molding production lines, little attention has been paid to the source of the data collected for the calculation of the indicator and therefore to the quality of the data collected, particularly in the case of semi-automated processes for which the collection of data for the calculation of the OEE and for the causes of downtime must be retrieved manually. For these cases, mechanisms to improve the manual data quality would be useful. The present study aims to follow the recent academic advances discussed so far to propose a solution that improves the quality of important process data that cannot be obtained automatically. The study will focus on an application solution that allows data to be collected in a plastic injection molding process.