4.1. Results
The network visualization of IPC codes per year is derived via UCINET. These networks show the relationship among various technologies in patent data for that particular year. It also highlights the major technologies which are playing a key role in networks. Technological convergence can also be seen and explored from the resulting networks. These networks can demonstrate the intersection of some specific areas of technologies over the years. The IPC codes associated with each year’s patents were derived, and then the networks were created for each year. Some years have limited patents and IPC codes, so the authors derived networks from 1993 to 2019 in this study. In the results section, the authors presented networks for the last ten years because of space limitation. Here, the authors used these IPC code networks to analyze the technological convergence of the smart factory.
Table 5 shows the IPC code networks for the respective years.
The last two years, 2018 and 2019, contain the highest number of patents included in this case study. Therefore, their networks are complex too, as shown in
Table 4. Both networks have clusters of many dominant technologies.
Moreover, centrality analysis has also been carried out for the extracted IPC codes from 1993 to 2019. Graphs of three different indexes of centrality are shown. First, the degree of centrality graph is shown in
Figure 5. This graph shows how the degree centrality of some of the IPC codes increased rapidly during the last couple of years. Codes such as H01L, G06Q, and H04L are among the top IPC codes concerning degree increment. These IPC codes represent the technologies of semiconductor devices, data processing systems, and the transformation of data information.
As the actors with high degrees are considered central nodes in a network, major nodes are regarded as the most advantaged and most influential position in a network. So, the degree centrality graph illustrates that those codes with top degree centrality in recent years can highly influence the other technological nodes. Moreover, this graph shows that their linkages and relationships with other technologies have increased significantly in recent years.
Figure 5,
Figure 6 and
Figure 7 illustrate the need for and importance of technological convergence in a way that shows how technologies, associated with data orientation, have gained importance and have been continuously converged with other technologies for the smart factory.
Figure 6 shows the closeness centrality of IPC codes from 1993 to 2019. IPC codes and their relevant closeness centrality are shown in the graph.
Figure 7 shows the betweenness centrality of the IPC codes from 1993 to 2019. This graph illustrates that the betweenness of some top IPC codes has increased dramatically over the past few years. Mainly, the top IPC codes in this graph are G06F, H04N, and G06K. These IPC codes represent the technologies of electrical digital data processing, pictorial communication, and recognition of data.
4.2. Discussion
This article addresses the technological convergence of the smart factory, which is a unique concept of Industry 4.0. So, a production facility such as a smart factory may lead to high efficiency, increased productivity, lower maintenance costs, customized production, etc. Hence, this article addresses issues related to the economic aspects of sustainable development. Further, it involves the evolution and investigation of technological convergence in the smart factory. It illustrates how the various technological convergence related to the smart factory has occurred during recent years. The technological evolution in recent decades can be easily understood with the help of technological convergence assessment.
The smart factory is a hot topic nowadays, and many researchers have studied it to investigate the implementation of various strategies and technologies in production systems. For example, Nascimento et al. explored smart factory technologies to implement the circular economy (CE) in a smart factory. And as a result, they found some positive effects of CE regarding waste reduction, reducing the negative environmental effects, and some positive social effects [
1]. On the other hand, definitions, challenges, and hurdles in smart factory implementation and its enabling technologies have been discussed by [
7]. In addition, RFID-enabled smart factories and IIoT-based smart factories have been investigated by [
12,
14]. Chen et al. discussed the key technologies and proposed the three-layer architecture for the smart factory, consisting of the physical layer, network, and data application layer. They conducted a case study and found some valuable findings regarding their proposed methodology for smart factory implementation [
2]. Similarly, Won and Park explored the decision factors to introduce the smart factory concept in small and medium enterprises (SMEs) [
50]. Resman et al. explained the procedure to implement the smart factory after reviewing the literature related to the smart factory [
51].
So, from the previous findings, the authors had the idea that technological analysis of the smart factory has limited available literature. Therefore, a technological analysis was needed to provide valuable information about the technological convergence of the smart factory. In this study, the authors find that, with each passing year, the number of patents associated with smart factories increased. As a result, the technological networks have become more and more complex. Especially from 2016 onward, there is an increase in the number of related patents, showing the significance of smart factories in recent years. IPC code networks from the years 2016 to 2019 show the resulting complexity due to the higher number of nodes (codes) and more connections. Many innovations and technologies are being developed related to digital manufacturing. Our case study shows that, over time, technologies related to data analysis have gained significant importance. From the above networks, G06F, H04L, and H04W are the top three codes dominating the networks. G06F represents the technology of electrical digital data processing, according to the World Intellectual Property Organization (WIPO). H04L represents the transmission of data information technology, and H04W is related to wireless communication networks. We found that data-oriented technologies come as the major output. Many of the data-oriented technology convergence related to digital data transformation, data recognition, and data analysis technologies have appeared as appealing in smart factory scope. All of these codes are related to data analysis technology. Data has a vital role in smart factory development and operations. In the smart factory, machines are interconnected and share data. Indeed, the uniqueness of the smart factory lies in its ability to process real-time data and use it for decision-making processes. Better and on-time decisions are made based on data collected on the floor. Therefore, these technologies associated with data transmission, data processing, and communication networks truly represent the smart factory. Besides these technologies, there are some other technologies in the networks which also represent the technologies related to data processing and semiconductor devices. In short, the top IPC codes in the complex networks of recent years represent the technologies related to data processing and the transmission of information, which are the key aspects of the smart factory.
From the centrality analysis aspect, the authors obtained the top IPC codes related to the smart factory. These IPC codes also represent data-based technologies. After analyzing degree centrality, the authors identified G06F, H04L, H04W, and G06Q as the top IPC codes. These codes were obtained by analyzing their degrees in recent years. So, these codes have the highest degree in the last couple of years, showing their dominance in the degree centrality category.
Table 6 reveals the top five degree centrality IPC codes of the last three years, from 2017 to 2019. It illustrates the dominance of some specific technological IPC codes, which are mostly related to data processing technologies.
Meanwhile, closeness centrality shows how close a node is to other nodes. In the case of the smart factory, the authors identified G06F, H04L, and G06K as the main codes for closeness centrality. These codes have the highest closeness centrality for the given networks in recent years. Further, betweenness centrality highlights the most influential nodes in a network between different clusters of the network. Here, the authors identified H04L, G06F, and H01L as the most important nodes for betweenness centrality.
Since the number of smart factory patents increased significantly from 2016 to 2019, the number of classification codes in each year increased. As a result, IPC code networks have become complex and more saturated as they include more nodes. Hence, the degree centrality of nodes increased in this manner. Due to an increment in the degree of centrality, some of the major nodes in the network gained more promising positions, which impacted their connections. They are playing a key role in the networks, and show that many technologies are dependent or connected with the more central node. In our case study, these nodes are mostly related to data-oriented technologies, which shows how these technologies have gained significant importance to the smart factory in recent years. Many nodes (technologies) are connected to these nodes (data-oriented technologies), showing the possible technological convergence of data-oriented technologies for the smart factory. The research results indicate how technological convergence evolution has taken place over the past few decades. The authors found some prominent technologies which gained significant importance over this time. The authors found that major IPC codes are associated with data processing and information transmission by analyzing both network and centrality aspects. So, it is clear from the above results that the future of production facilities and smart factories is based upon data-related technologies. The smart factory, where machines will be interconnected and share information, will rely on these data-oriented technologies.