# The Human Performance Impact on OEE in the Adoption of New Production Technologies
## 2 Literature Review
### Human Factors in the Initial Adoption Phase of New Production Technologies
While switching from low volume to high volume production, workers are assumed to have improved their knowledge about the production process that was not initially well understood [1], leading to increased productivity over time due to repetitive execution of activities by employees, familiarization with the production process and organizational-technological changes [3]. [PERSON] and [PERSON] (2001) [1] model the learning process in the form of experiments performed on the system during the ramp-up phase, collecting specific information on resource behavior and highlighting the criticality of learning. Moreover, Industry 4.0 provides help for training operations using virtual reality (VR) [17]. By using virtual simulation and training (VISTRA), representing a system that simulates the manufacturing environment, it was observed that operators can speed up the ramp-up period by reducing the time spent in practical training in the real environment and showing the most critical operations in the learning phase. [PERSON] et al. (2017) [23] show that the effect of virtual training during a virtual assembly experiment guarantees, on average, 72% of the learning effect by performing all activities for the first time in the real context. The effect of the actual training was a reduction in assembly time by 27%, while virtual training induces a 20% improvement. Several scientific contributions have also described and modeled the training progress of workers using learning curves. In the production and operations management field, the learning curves describe the improvement in worker performance due to repetition of activities or experience, and, therefore, they are of fundamental importance in the managerial decision-making process. The phenomenon of human training is relevant in labor-intensive manufacturing companies, especially where the labor cost is expensive [5]. Learning curves can be applied to define more accurate job standards, track realistic production goals, forecast the available working time of a process, and forecast production output and compliant units. Learning by doing is a natural phenomenon that improves human performance, and it leads to a reduction in the time required to perform an activity for each repetition [24]. In 1936, [PERSON] noted that when doubling the produced volumes, the unit production time was reduced by a constant percentage [25]. Based on this consideration, he defined the learning curve that can be used in operations management as a supporting function in the planning of the volumes to be processed. The curve can be used to derive the time spent by employees to learn a new activity and, consequently, plan the workforce during full production. In the same context, [PERSON] et al. (2011) [24] affirm that the opposite phenomenon to learning, namely the tendency to forget, must also be considered in formation. In the field of industrial engineering, the common assumption is that if the worker interrupts the execution of activities for a certain period, then the performance of the worker deteriorates up to when a new production session begins [24]. The time elapsed between the training of the operators and their insertion in the process must be minimal to avoid reducing the performance of the employee. Neglecting this key factor in human resource planning could lead to under sizing the required staff to produce a certain target volume. In addition, some scientific contributions have attempted to determine performance indicators for the assessment of industrial operators. For instance, [PERSON] et al. (2012) [26] assess human factor performances during the operator training period through the \"Analytic Hierarchy Process\" (AHP) methodology. Other research works discuss the evolution of operations roles with the impact of Industry 4.0 by focusing on the interconnection between new technologies and humans, and on the increase in compatibility, effectiveness, safety, human well-being and quality of life [27; 28].