# Lean Manufacturing Soft Sensors for Automotive Industries
## 4 Conclusions  
Lean manufacturing attainment is quintessential for the auto component firms to survive and thrive in modern competitive markets. Effective management of manufacturing flexibilities leads to improvement in the lean manufacturing level of an enterprise. Due to the complex interplay of various flexibilities, it is a challenging task to predict their combined influence on lean manufacturing. The present study applied a machine learning approach to develop an effective lean manufacturing soft sensor, to predict/classify the lean attainment of an auto component firm based on the existing levels of its manufacturing flexibilities. As many as 29 models were explored, belonging to seven different machine learning architectures, viz. ANN, KNN, SVM, decision trees, discriminants, Naive Bayes, and ensembles. The trilayered neural network LM soft sensor attained the highest testing accuracy of 80%. The fine, medium, and coarse trees, the narrow and wide neural networks, the quadratic and cubic SVMs and the ensemble RUSBoosted tree--all these models scored 60% testing accuracies. The medium and bilayered neural networks, the Gaussian, and Kernel Naive Bayes, the medium, coarse, cosine, cubic and weighted KNNs, the linear, fine, medium, and coarse Gaussian SVMs, the linear discriminant, and the ensemble boosted, bagged and subspace discriminant models attained only 40% testing accuracies. The fine KNN and the ensemble subspace KNN could achieve only 20% testing accuracy, whereas the quadratic discriminant was unable to model the flexibilities-LM dataset used in this study. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. The scatter parallel coordinate plots showed  
Figure 22: Validation Predictions: Trilayered Neural Network LM Soft Sensor. that the correctly predicted high LM class firms had higher flexibilities than their medium LM class counterpart, except in case of the product flexibility. The confusion matrices and the ROC curves revealed the exact class-wise predictive performance of the trilayered neural network soft sensor for validation and testing datasets. Moreover, the precision, recall and F1 scores obtained from the testing confusion matrix as well as the ROC curves confirmed that the trilayered neural network soft sensor was insensitive/robust to the relatively unbalanced dataset employed in the present study. Hence, this work establishes that machine learning based soft sensors can be developed to suitably classify the lean manufacturing level of an auto component manufacturing firm based on its manufacturing flexibilities. The successful lean manufacturing level classification of auto component manufacturing firms based on their flexibilities using machine learning soft sensors holds great importance in today's scenario of competitive and dynamic markets, supply chains, and consumer preferences. Manufacturing firms can frequently measure their lean manufacturing levels by deploying machine learning-based soft sensors and take suitable actions for improvement based on their flexibility data/levels. In the present study, exploration of multiple architectures within the same model group was achieved by varying few hyperparameters. The remaining hyperparameters were kept/assumed constant. Moreover, the present work did not include an in-depth algorithm-level analysis of why some machine learning models performed better than others for the given dataset. In a future work, all hyperparameters will be varied/optimized, coupled with a methodical analysis of machine learning models' performances to overcome these limitations of the present study to further improve/maximize the LM classification accuracy of the machine learning soft sensors.