## 3 MSP Algorithms and Master Data in Production Planning System
### Forecasting Variable Master Data
Using the training data, a predictive model is generated that could predict the correct output for a new input. The predictive model learns the relationship between a given input and output to generate a decision boundary [28]. A typical flowchart of a supervised learning algorithm in machine learning is shown in Figure 4. Based on this flowchart, here is how the study was conducted. 1. Data collection: The input data required for training and the correct answers (labels) are collected. The data required for training for both yield and tact times are the master data consisting of existing baseline information. The correct answer data are calculated based on the performance. 2. Data preprocessing: The collected data are processed into an analyzable form, and the necessary preprocessing tasks are performed. These methods include data cleaning, attribute scaling, and outlier handling. The statistical program R is used to organize the horizontal database data into vertical data. This is the means by which the features are represented as columns in a model, wherein each row represents a data point. When converted into vertical data, the features are placed in columns corresponding to the model input matrix. This makes it suitable for the model to process the data. The obtained yield and tact-time training data are organized into vertical data by type. In machine learning, the processing of vertical data has the advantage of standardizing the data structure. This facilitates their application to various models and libraries. 3. Select/extract attributes: The attributes required for training are selected or extracted. In this step, one can analyze the characteristics of the data and select important features or extract new features. In this study, information such as ITEM and RESOURCE is extracted from the machine learning calculation because these are not directly related to the target data calculation. Moreover, the data to be included in the calculation can be selected. 4. Select a model: The best model for the given problem is selected. The M5P algorithm provided by Weka (a Java machine learning library) is used in this study. Figure 3: Traditional IT compared to Analytics IT. 5. Train the model: The selected model is trained using the training data. During the training phase, the model learns the relationship between the input data and correct answers (labels) for that data so that it can make predictions. 6. Evaluate the model: The performance of the trained model is evaluated. This is accomplished by examining the model's prediction results using test data and calculating the evaluation metrics. The main evaluation metrics are the accuracy, precision. 7. Apply the model: The trained model is applied to new data to make predictions. The results of the predictions for new data are applied to accomplish the objective. In this study, the data predicted by machine learning are incorporated into the master data to generate a new production plan. Figure 4: Machine Learning Supervise Process [29]. This study calculates two target master datasets by analyzing the performance information and predicting their values using a supervised learning algorithm, in conjunction with their corresponding target values.