## 4 Performance Analysis
### Model Comparison
#### 4.4.2 SHAP Values and Prediction  
The idea of the Shapley value was derived from game theory, as described above. This paper was used to interpret the prediction results of the proposed AI model using the Shapley value, and positive results were obtained to induce collaboration between workers and AI. The model proposed in this paper preprocesses the event log data using a transition system and predicts the manufacturing process time by learning the data. Features can affect the results predicted by the model. The data we used is from the metal processing process of the real world, and the features of the data learned by the AI in this paper refer to several manufacturing machines in the manufacturing process. The contribution of these features is called the SHAP value. That is, since the Y vector in this paper is the remaining time of the manufacturing process, the degree of influence that each manufacturing machine has on the manufacturing process can be known through the SHAP value. The data learned in this paper is a metal processing process, and Figure 15 shows the SHAP value of one of the cases that produce a cable head. The red arrow in the figure indicates the factors that affect the increase in the prediction result value, while in contrast, the blue arrow explains the factors that affect the decrease in the prediction result value. The length of the arrow quantitatively expresses the extent to which the corresponding feature contributed to the prediction result and corresponds to the SHAP value. Because the result that this model learned and predicted is the remaining time of the manufacturing process, Machine 11--Grinding, Machine 5--Turning and Milling had an impact on increasing the manufacturing process time, and Work Order Qty, Machine 4--Turning and Milling. It had the effect of reducing the manufacturing process time. Part Num was excluded from the analysis of XAI because it is a number used to classify manufactured products. Figure 16 shows the SHAP value of one of the cases producing spur gear generated in the metal manufacturing process. As described above, the red arrow in the figure shows the features that have an effect on increasing the manufacturing process time, while the blue arrow shows the features that have an effect on reducing the manufacturing process time. The length of the arrow is a quantitative expression of the degree of contribution and means a relative value for the case that is being described. Through the figure, we show that Packing, Machine 10--Grinding had a positive effect on increasing the manufacturing process time, and Work Order Qty, Quality Check 1 had a positive effect on reducing the manufacturing process time in the case of generating Spur Gear. The Part Num of the graph was also excluded from the analysis of XAI because it is a random number to distinguish the manufactured products. Figure 15: SHAP values of cable head. Figure 17 explains the SHAP value of one of the cases producing Ball Nuts. In this case, Work Order Qty had a significant effect on delaying the manufacturing process time, while Machine 11--Grinding and Quality Check1 had an effect on shortening the manufacturing time. Likewise, Part Num was excluded from the analysis because it is a number mapped to distinguish production products. With the local accuracy property of the SHAP value, it is possible to provide a meaningful estimate of the contribution of the feature to the variation in the predicted time of the manufacturing process time of the case. In addition, the estimate can help field experts in the manufacturing process determine the appropriateness of direct learning without interpretation by AI experts and provide an appropriate indicator to increase the accuracy of AI models.