## 4 Results and Discussions
### Managerial Implications  
In this study, the proposed system consists of three parts: the IoT-based sensor, big data processing, and machine learning model. First, the IoT-based sensor device developed in this study is based on Raspberry Pi which is small-size, low-cost, and powerful single-board computer device. Previous studies have shown significant advantages of utilizing Raspberry Pi such as for controlling and monitoring IoT system [83], estimating the roll angle of a vehicle using embedded neural network in real-time [84], hosting and serving the user interface of eHealth care system [59], and monitoring the temperature of lava lake using near infrared thermal camera [85]. Therefore, the proposed IoT-based sensor device developed in this study could be applied to monitor the manufacturing process in real-time. Second, since the number of IoT devices increased, it is necessary to develop new big data processing to effectively handle, process, and store the data without experiencing detectable performance loss. Previous studies revealed that by implementing open source software (OSS), the organizations can achieve some economic gains in terms of software development productivity, product quality, as well as lower cost (i.e., license costs) and availability of external support [86; 87]. In our study, the developed big data processing platform is based on the OSS that is cost-effective  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** \\ \hline Naive Bayes (NB) & 94.1 & 93.6 & 93.567 \\ Logistics Regression (LR) & 98 & 98 & 97.953 \\ Multilayer Perceptron (MLP) & 96.8 & 96.8 & 96.784 \\ Random Forest (RF) & 98.5 & 98.5 & 98.538 \\ DBSCAN + NB & 96.8 & 96.7 & 96.74 \\ DBSCAN + LR & 98.6 & 98.5 & 98.52 \\ DBSCAN + MLP & 98.8 & 98.8 & 98.81 \\ Hybrid Prediction Model (DBSCAN + RF) & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison of several classification models for fault prediction.  
for implementation and integration. Third, machine learning has been used in various processes for monitoring systems in manufacturing and predictive maintenance in different industries [88; 89; 90; 91; 92]. Machine learning has powerful tools for continuous quality improvement in a large and complex process such as semiconductor manufacturing [89; 90; 92]. In our study, the machine learning model is used to detect the fault (abnormal event) during assembly line process in real-time. Thus, it is expected to support the management in improving the decision-making and preventing the unexpected loss caused by faults at an early stage during manufacturing process. Finally, the overall results of the study can be used as a guideline for the industrial practitioner in adopting the IoT, big data, and machine learning for their manufacturing process.  
Previous scholars and practitioners have considered several aspects of big data. Big data is often described in terms of 4 V's, they are volume (the size of data), variety (different type of data), velocity (speed of data generation), and veracity (reliability of data) [61]. However, some scholars are more focused on one or more aspects of the big data concept. [PERSON] et al. focused more on the variety aspect of data sources [93], while some other authors emphasized the storage (volume) and analysis parts when it comes to dealing with big data [94; 95]. The big data processing that efficiently can handle the fast incoming (velocity) and huge amount (volume) of sensor data has been developed in our study. Finally, the integration of an IoT-based sensor, big data processing, and machine learning model can be utilized to effectively monitor the manufacturing process as well as obtain early warning notification when an abnormal event is detected in real-time.