# The Human Performance Impact on OEE in the Adoption of New Production Technologies
## 3 Framework Proposal
### Criteria for Target OEE Calculation  
Production and workforce capacity planning is usually performed ahead of the end of the ramp-up period to anticipate the needs of the fully operational state of the technology. Moreover, organizations should be able to estimate and determine the conditions under which the transition from the ramp-up phase to the fully operational state is obtained. In this context, the OEE measurement could be employed to forecast the expected processable quantity for the fully operational state of the production technology, since this measurement is suitable for automatic and semi-automatic production processes [43]. However, the OEE calculation should also consider the impact of the human aspects (e.g., training) that influence the duration of the ramp-up phase. For this reason, starting from the proposed OEE measurement methodologies, a set of criteria is here introduced to evaluate and perform proper production planning and to establish the moment in which the ramp-up state is over. The following criteria are proposed for the estimation of the target OEE to be obtained in the fully operational state of the production technology:  
1. Compute the actual OEE value of the machinery (\(\text{OEE}_{\text{mac}}\)): it is required to compute the OEE value that filters the components due to human and process configuration errors occurring during the adoption phase of new production technologies to obtain the actual machinery OEE value. For this calculation, refer to Section 3.2. It is hence assumed that the human-process losses do not occur in a fully operational state, or that they occur in a less systematic manner;
2. Evaluate the average OEE during the ramp-up phase;
3. Evaluate the percentage of OEE which increases due to completion of trainings: this value can be computed by considering the percentage of OEE increase obtained \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{3}{c}{**OEE Process Calculation**} & \multicolumn{2}{c}{**OEE Machinery Calculation**} \\ \hline Downtime (D) & \(\text{D}_{\text{proc}}=\text{D}\) & (11) & \(\text{D}_{\text{mac}}\)\(=\text{D}_{\text{proc}}-\text{F}_{\text{proc}}\) & (12) \\ Operating & \(\text{A}_{\text{proc}}=\frac{\text{U}}{\text{U}+\text{D}_{\text{proc}}}\) & (13) & \(\text{A}_{\text{mac}}=\frac{\text{U}}{\text{U}+\text{D}_{\text{mac}}}\) & (14) \\ rate/Availability (A) & \(\text{Performance efficiency}\) & \(\text{PE}_{\text{proc}}=\frac{\text{P}_{\text{c}}+\text{P}_{\text{acc}}}{ \text{P}_{\text{target}}}\) & (15) & \(\text{PE}_{\text{mac}}=\frac{\text{P}_{\text{c}}+\text{P}_{\text{sys}}+\text{P} _{\text{post}}}{\text{P}_{\text{target}}}\) & (16) \\ Quality rate (QR) & \(\text{QR}_{\text{proc}}=\frac{\text{P}_{\text{c}}+\text{P}_{\text{acc}}}{ \text{P}_{\text{acc}}+\text{P}_{\text{rec}}}\) & (17) & \(\text{QR}_{\text{mac}}=\frac{\text{P}_{\text{c}}+\text{P}_{\text{post}}+\text{P} _{\text{post}}}{\text{P}_{\text{rec}}+\text{P}_{\text{post}}}\) & (18) \\ OEE & \(\text{OEE}_{\text{proc}}\)\(=\text{A}_{\text{proc}}\cdot\text{PE}_{\text{proc}}\cdot\text{QR}_{\text{proc}}\) & (19) & \(\text{OEE}_{\text{mac}}\)\(=\text{A}_{\text{mac}}\cdot\text{PE}_{\text{mac}}\cdot\text{QR}_{\text{mac}}\) & (20) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Representation of the OEE measurement methodologies, distinguishing between human and technical factors. between the end and the beginning of the training. It is therefore possible to indicate the value as in Equation (21):  
\[\Delta\text{OEE}_{\text{training}}=\frac{\text{OEE}_{\text{post-training}}}{ \text{OEE}_{\text{average, ante-post}}}-1 \tag{21}\]  
where \(\text{OEE}_{\text{post-training}}\) is the OEE value registered at the end of the training, while \(\text{OEE}_{\text{average, ante-post}}\) represents the average OEE value in the whole training period.