# The Human Performance Impact on OEE in the Adoption of New Production Technologies
## 1 Introduction  
The ramp-up is defined as the period between the end of product development and full capacity production [1]. Following this definition, the ramp-up phase can be more precisely defined as the process of bringing a production system up to its required operational characteristics after it has been designed and built and before it is taken into full operation [2]. In this phase, the production process is often not fully understood and subject to errors, requiring adjustments that are costly for the company and which could lead to delays in introducing the product to the market [2]. Problems encountered during this phase include disturbances in process and product quality, a lack of reliable planning, unplanned capacity losses, and poor performance of suppliers [3]. Furthermore,the unpredictability of the system behavior generates process instability, since most of the operating conditions are unknown and are being applied for the first time. In this context, decisions concerning the planning and organization of the ramp-up become essentially complicated [4]. The goal of the ramp-up is to increase both the yield and the production rate in the shortest possible time [1]. However, at any time, there is a trade-off between the two mentioned parameters given that as production speed increases, the probability of encountering defects increases as well. Consequently, an increase in the number of reworks and of the work in process (WIP) is faced. In addition, according to [PERSON]'s law, the average waiting time also increases, as well as the time between the occurrence of the problems upstream of the process and their detection in the downstream verification stations: as soon as problems are identified, there is a faultier WIP in circulation to be reworked [1]. Hence, this trade-off should be considered carefully to achieve effective management of the ramp-up period. Indeed, during the production start-up, several unanticipated problems may occur, which should be managed carefully so as not to impact either other business processes or the customer experience. Process errors should be reduced through constant attention to the learning and observation of the interaction between the elements of the production system. The production ramp-up phase can be considered a learning process that could be reflected in better usage of the equipment, in the continuous improvement of product quality, and in the reduction of labor requirements [5]. The length of this period can be measured either as \"ramp-up time\" or as \"time to volume\", and it is characterized by a gradual increase in the output production rate as well as an improvement in the recorded quality rate [6]. Moreover, the adoption phase of new production technologies includes a learning phase for both production managers and operators [7]. However, most of the complexity is managed by the operators, so the effectiveness of the ramp-up largely depends on their experience and knowledge [2]. Hence, human performance represents an additional source of variability that dramatically impacts the ramp-up phase and that should be properly assessed. Though the key role of the human factor is recognized in the scientific literature, it seems that there is a lack of contributions including this element in the ramp-up phase evaluation, often due to the absence of available data for its assessment. In this context, digitalization and Industry 4.0 can offer support during the adoption phase of new production technologies, since they enable companies to analyze large amounts of real-time data, improving strategic and operational decision-making activities [8]. The key element that characterizes this new industrial phase is the profound change in the connectivity of production systems due to the integration of information and communications technology (ICT), Internet of Things (IoT) and machinery in cyber-physical systems (CPS).