## 4 Performance Evaluation
### Results and Discussion
#### 4.3.2 ALCOA+ Value Prediction  
Regarding the prediction of the ALCOA+ values, we tested our model and compared its performance against an LSTM and a GRU model on the same datasets. For this evaluation, we ordered data batches in a chronologically descending manner creating a batch data vector and fed it to the models, batch by batch. The output of the model following the last training batch data forward step was considered the output of the model at batch step 0. This result then was appended to the start of the batch data vector (becoming the most recent data sample) and re-fed to the models. The output of this operation was the predicted ALCOA+ values at batch step 1. This output was then again appended to the start of the previous obtained vector, and so on. This process was repeated until batch step 50, because after that moment average errors started to rise producing nonsensical results. The comparison results clearly show that, on average, the ALCOAi model surpasses the performance of the GRU and LSTM models (Figure 5). In particular, as shown in Tables 5 and 6, the ALCOAi model consistently achieves lower MAE values, averaged across all ALCOA+ principles, when compared against the other two models on both production line datasets. However, all of the models demonstrated a similar exponential trend in the calculated MAE with increasing batch numbers, meaning that while we are able to model ALCOA+ value trends in the near future with some accuracy, predicting such values further in time becomes inconsistent and unreliable. The exact threshold that defines whether an MAE value is acceptable or not depends on individual business policies. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**ALCOA+ Principle** & **1600** & \multicolumn{4}{c}{**I1000**} \\ \hline  & **ALCOAi** & **GRU** & **LSTM** & **ALCOAi** & **GRU** & **LSTM** \\ \hline Attributable & 1.24 & 1.88 & 1.96 & 1.44 & 2.05 & 1.68 \\ \hline Legible & 2.37 & 2.66 & 2.64 & 4.61 & 4.73 & 5.30 \\ \hline Contemporaneous & 2.38 & 3.79 & 4.00 & 3.46 & 4.88 & 5.73 \\ \hline Accurate & 2.83 & 3.07 & 3.07 & 3.04 & 3.69 & 3.61 \\ \hline Complete & 1.99 & 2.22 & 2.12 & 2.40 & 2.48 & 2.54 \\ \hline Average & **2.16** & 2.72 & 2.76 & **2.99** & 3.57 & 3.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean absolute error (_MAE_) between real values and predicted ones for ALCOA+ attribute regression. The ALCOA+ principles that were not used during the training sessions are omitted. The values reported refer to the performance of the models obtained on the test set. The best performance is written in bold. ufacturing lines. This model was trained on two separate real datasets stemming from two production lines, achieving a high accuracy level and demonstrating the effectiveness of using neural networks for this task. In fact, the conducted experiments indicate that predicting ALCOA+ values is possible and the predictions made can contribute to decision making to deal with data integrity issues according to the business policies of the pharmaceutical manufacturers. The experiments also shown that transformer-based networks perform better when compared to traditional time-series processing neural networks such as the LSTM and GRU, and the performance gap increases as predictions are made further forward in time. As future work, we aim to integrate explainable AI methods (such as [32]) to weight and highlight the data fields that contribute the most in the pharmaceutical manufacturing data integrity assessment based on the ALCOA+ principles. I.K.Writing, programming, performance evaluation, experiment definition, review; E.L.Writing, literature analysis; H.C.L. : Team management, writing, review; G.O. : Programming, writing, performance evaluation; D.K. : Programming, writing, performance evaluation; V.C.G. : Writing, review; A.K. : Team management, experiment definition, funding, review, writing, proof-reading. All authors have read and agreed to the published version of the manuscript.