# A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing
## 3 Results
### Data-Driven Decision Making  
The big data collected by IoT can be leveraged for different tasks such as scheduling optimization [87], minimizing new process validation time [88] or intelligent manufacturing equipment that allows for visualization of machine information [89]. Various ML and AI solutions are key drivers of data-driven decision making are discussed. For example, big data analysis can be adopted for equipment reliability analysis and predictive maintenance, as discussed by several articles within the SLR process including [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al. [45; 47; 48; 74; 90; 91]. ML algorithms can be used for predicting machine failures or abnormalities in advance, leading to better maintenance planning possibilities and cost reduction [45]. For instance, sensors collect data on motor vibration and reduce unexpected downtime as a result, as in the article by [PERSON] et al. [45]. Moreover, [PERSON] et al. discuss that a TensorFlow-enabled deep neural network (DNN) is shown to be more accurate than PCA and HMM for equipment reliability analysis based on IoT data [90]. A goal of human-machine interaction is the optimization of tasks performed by humans [18]. For this, learning to recognize different tasks performed is crucial. One way of monitoring the workers in real-time is by using cameras positioned over their workstations. The assembly task performed by worker can then be classified using the pictures taken by a camera as an input data for transfer learning. The current task can be identified in near real-time by using fog computing, as the computationally heavy tasks are performed close to data source [18]. Moreover, segmentation can be used for image-based quality control. By first identifying areas of interest a lot of computation can be spared by applying detection only on those areas [92]. However, as big data analysis can be computationally costly, ways to solve that are required. For instance, the \"inspection by exception\" method by [PERSON] et al., that focuses on inspection of only the parts which quality is considered uncertain [47]. In fact, using unsupervised Fuzzy C-Means clustering algorithm and PCA reduced the amount of inspections by 82% and PCA-based supervised ANN by 93% [47]. The amount of data collected during manufacturing processes has increased drastically; however, only a small part of it is used for optimization purposes. For this reason, [93] studied the use of multiplayer perceptron with two hidden layers to reduce data and extract meaningful information, by measuring electrical currents and machine temperature to classify operating states allowing for further studies in indication of errors during production or wear of machinery [93]. Fault diagnosis can be a difficult task due to the amount of data that smart factories produce. However, the abnormalities can be classified into production-threatening and not categories, narrowing down the data that needs to be analyzed [48]. Vision systems are seldom employed in the quality control of medical device manufacturing due to their relatively high error rate caused by sensitivity to light and setup [94]. Hence, these kinds of setups often require expert validation and are considered unreliable. However, a study conducted by [94] studied medical device classification in different settings using CNNs and identified Single Shot Multibox (SSD) model, as preferred classifier in medical device production. Furthermore, real time quality inspection can take place by collecting acoustic, visual, and haptic signals from wearable smart devices for CNN model which qualifies the task action as successful or not [95]. Smart wearables are considered from both quality of experience and quality of information viewpoints [96].