## 4 Discussion
### Application Papers  
In general, the application papers presented real case studies that best represent the theoretical models and enrich the methodological description. For example, a physics simulation was proposed by [PERSON] et al. (2019) [38] to train the operators and avoid mistakes at multiple positions and measure the subsequent configuration efficiency. A case study in the field of industrial manufacturing was also presented. In this context, a physics simulation helped to compare various scenarios before implementation to reduce costs and achieve global sustainability within the company. Furthermore, the principle of the Internet of Things and its transfer to the industrial sector, in order to identifynot only new managerial models, but to also optimize the efficiency of manufacturing systems, are being developed by several authors. In particular, [PERSON] et. [39], proposed a multi-stage model in the context of industrial service based on IoT useful to generate added value in platform-based ecosystems. An interesting point of view, in the context of manufacturing systems, was presented by [PERSON] and [PERSON] [40], who proposed an optimized IoT based Job Shop Scheduler Monitoring System. The system aims to track the tasks being performed by the machines, enabling automatic detection of the completion time of a job and based on that, dynamic rescheduling. Meanwhile, [PERSON] et al. [41] applied IoT to develop a Smart Behavioral Filter (SBF) for the Programmable Logic Controllers with the aim to secure the PLC itself against logic attacks. Always in the manufacturing context, [PERSON] et al. [42] proposed an IoT standard to monitor an injection moding machine. The proposed system was based on Mobius, developed by the Korea Electronics Technology Institute (KETI). A different approach was analyzed by [PERSON] and [PERSON] [43] and by [PERSON] and [PERSON] [44]. In the first study, they proved the efficiency of the \"Lean Six Sigma 4.0\" in the context of a HealthCare environment. The second study showed how smart labels can provide identification, tracking, sensing, event detection, and interaction to human-centered Industry 4.0 applications. Emerging research topics include machine learning algorithms to identify technical defects in the manufacturing sector (such as to identify relationships that can be harnessed to preempt electrical defects at downline inspection stations) [45], genetic algorithms to predict customer needs [46], and robot actions using a multi-platform software application [47]. A very nice application was proposed by [PERSON] et al. [48]. In particular, a SCARA robot's system and software implementation were used to detect defects in the product. A compliance rate was calculated using an efficient algorithm. Nice applications based on a CPS were proposed by [PERSON] et al. (2017) [49] and [PERSON] et al. (2017) [50]. Respectively, the first was a framework of CPS as a 5C architecture including connection level, conversion level, cyber level, cognition level, and the second was a smart manufacturing platform based on CPS. Always on CPS, [PERSON] et al. (2016) [51] proposed the application of the IEEE1588 protocol to new generation CPSs in order to achieve sub-microsecond synchronization. The analysis of the papers also highlighted the confirmed interest in technologies such as additive manufacturing as a tool to reduce the need for energy- and resource-intensive manufacturing processes [52].  
The analysis of selected papers highlighted some research gaps and weaknesses, as summarized in Table 5.