## 3 Materials and Methods
### Dataset Description  
Data included 29,115 records. A set of 20,381 (70% of 29,115) was used for training. Each of them consists of:  
1. Six inputs: * Event classifier 1 (class1), e.g., categories: packaging, bonding, sealing, etc. ; * Event classifier 2 (class2), e.g., categories: controller, belt, head, blade, etc. ; * Event classifier 3 (class3), e.g., categories: failure, damage, adjustment, etc. ; * Production department (department), e.g., categories: WP1, WP2, WP3; * Production line (line), e.g., categories: LP01, LP02, LP03, etc. ;Segment online (segment), e.g., categories: mill, gluemachine1, segment1;
2. Three output data: * Repair description (description), e.g., categories: replacement, adjustment, both of them or external service; * Repair type (rtype), with repair type to determine where the repair can be routed, e.g., categories: electrical, mechanical, electrical and mechanical, etc. ; * Component (component), e.g., categories: belt, rollers, nozzle, etc. Each of the aforementioned categories is assigned a numerical value. The range of input and output variables in the ANN model is, for each variable, normalized to the range 0-1 and is a 1-z-n code created separately for each input or output, depending on the number of categories per input or output. Due to the use of a sigmoid (non-linear) transition function, we had to choose the bias appropriately, which is critical here for creating an effective learning model. A correctly chosen bias value allows the activation function to be shifted left or right and helps to produce a more accurate fit to the data (a more accurate prediction function at the output). In the study, we focused on multiplication-type bias rather than addition/subtraction-type bias because multiplication makes the function steeper and better at transferring differences when there are a large number of categories. However, it should be noted that, in the study, our model was learned on specific industry data, which may weaken its ability to generalize to a more diverse dataset and this will need to be modified. When looking at complex industry data, we relied on previous approaches presented in, amongst others in [58; 59]. In addition, for reasons of commercial confidentiality, the name of the department and the name of the line had to be coded. The number of observations in each category varies across the dataset, ranging from a few dozen (lowest is 35) to several hundred (highest is 183). We believe that this limits the accuracy of ANN because usually equinumerous sets in each category, not less than 20 in each category, are recommended for network training. Back-propagation (BP) is often used because it is simple, popular and quick. In subsequent studies, we will check the impact of category balancing, but it should be remembered that these are real, industrial data, and during normal operation it will be difficult to ensure equivalence when training ANN. In the study, we relied on an approach in which the MLP requires all inputs corresponding to numerical variables continuous in value and described by a continuous function. Hence, usually categorical values are replaced by numerical values and treated as if they were from a continuous set of values. An alternative solution is to encode the categorical inputs using 1-out-of-n encoding and use them as inputs. Previous research shows that the approach is more efficient and faster than other approaches; also for a dataset with several categorical and several continuous variables [60; 61]. ROC curve analysis was checked as an alternative approach. Since each output variable has more than two labels, we had to consider each label as an ANN output (yes/no), train the ANN from scratch and then perform a ROC analysis for each output. Hence, we have:  
6 inputs--a total of 80 neurons:  
* class1: 12 neurons;
* class2: 24 neurons;
* class3: 5 neurons;
* department: 4 neurons;
* line: 17 neurons;
* segment: 18 neurons. 3 outputs--a total of 65 neurons:  
* description: 5 neurons;
* rtype: 5 neurons;
* component: 55 neurons.