A Market Convergence Prediction Framework Based on A Supply Chain Knowledge Graph
The main function of our framework is to facilitate a comprehensive and detailed characterization of the nodes in a knowledge graph. This characterization process involves analyzing each node based on its attributes, relationships, and broader context within the network. This approach allows for a meaningful in-depth analysis of each company’s market position, technological capabilities, and innovation potential. This is particularly important for analyzing a company’s potential technological direction of development. In addition, the framework helps to provide personalized decision support to companies in the CNC machine tool industry. This personalized approach ensures that the recommendations are relevant and actionable, thereby improving the company’s SCR and SSCM, helping the organization make informed decisions, and reconciling economic goals with social outcomes, as technological advances are closely linked to supply chain dynamics.
4.1. Descriptive Analysis
The construction of a convergence network for AI and CNC machine tools involved analyzing diverse sources, including patents and supply chain data. The methodology is initiated with the extraction of pertinent technology terms from the existing literature on AI and CNC machines, leading to the development of a comprehensive set of search queries (see Appendix A). These queries facilitated the retrieval of 141,052 patents in CNC machine tools and 108,117 in AI from the Derwent Innovation Index. Each patent record comprised details such as title, publication number, organization, abstract, citations, and publication year. The latest data in these datasets were recorded up to December 2023.
The development of CNC machine tools technology and AI technology is shown in Figure 6. The timeline shows a relatively steady increase in patents for CNC machine tools technology, with a notable increase that has become more pronounced in recent years. In contrast, AI technology shows a more exponential growth pattern, with a sharp increase in patent applications, particularly in the last few years shown. This surge in AI-related innovation coincides with significant advances in computing power and data analysis capabilities. The intersection of CNC machine tools and AI technology is indicative of a growing interest in integrating intelligent automation into manufacturing processes. The accelerated growth in AI patent applications suggests an increasingly important role for AI in enhancing the precision, efficiency, and capabilities of CNC machine tools [63,64].
The steep upward trajectory of AI also reflects broader trends in digital transformation and Industry 4.0, where AI is a key driver of change across various industries [65,66]. Given the critical role of AI in enabling smart manufacturing, the data from Figure 6 underscores the strategic importance of investing in AI research and development to maintain a competitive advantage in the field of advanced manufacturing.
To extract the corporate entities and technology cooperation relationships in the field of CNC machine tools and AI in China, this paper extracts the names of all the corporate organizations from the “Patentee” field of the patents, with a total of 69,989 entries after de-emphasis, and a total of 292,724 companies in the entire supply chain, as shown in Table 1.
The study focused on the evolutionary trend between the number of companies and the Levenshtein distance threshold. The data matching procedure mentioned in Section 3.2 facilitated this study. Figure 7 illustrates the selection of thresholds, which was critical to ensuring the integrity and reliability of the name matching process, as it directly affected the quality of the data aggregation and subsequent analysis. A key conclusion from the analysis is that there is a significant saturation of matching accuracy when the Levenstein distance threshold reaches 0.8 shown as dashed red line in Figure 7, and above this value, while the match rate is higher, the number of companies matched is lower. Therefore, 0.8 has been chosen as the threshold that provides the most reliable matching performance while minimizing false positives.
A two-layer network model was constructed to extract data related to the list of companies accurately matched in the previous analysis across two key networks (the supply chain network and the patent collaboration network). As shown in Figure 8, the model visualizes the interconnections and collaborative relationships between companies in each industry, which informs the recommendation of technology collaboration companies.
The constructed network model consists of two distinct layers: the top layer represents the technology collaboration and the bottom layer symbolizes the supply chain interaction. In this model, nodes are color coded to clearly distinguish between different sectors. Specifically, blue nodes represent companies in the CNC machine tool industry, illustrating their location and linkages in both the technological and supply chain dimensions. Red nodes represent companies operating in the smart sector, highlighting their role and influence in shaping technological collaboration and supply chain dynamics. Yellow nodes represent companies from other different sectors, highlighting the cross-sectoral interactions that exist in these networks.
This color-coded, two-layer network visualization provides a comprehensive and intuitive picture of the complex interactions between technology collaborations and supply chain relationships. It can be used to provide insights into how companies in different industries, particularly the CNC machine tool and intelligence sectors, interact within and across these networks. Mapping these relationships provides valuable insights into structural patterns and collaboration trends in these industries. Such findings are critical for identifying actors in the networks, potential opportunities for collaboration, and strategic tries.
Figure 9 presents a refined network visualization, focusing exclusively on companies that have established cooperative relationships.
A striking observation from Figure 9 is the apparent concentration and intensity of technology cooperation among companies compared to market cooperation. The density and clustering of nodes in the technology layer visually represents this disparity. The nodes in this layer are more closely knit and numerous, indicating a higher degree of collaboration and interconnectedness in technology. This can be attributed to the increasing importance of technological innovation and development in driving competitive advantage and strategic partnerships in modern industries.
In contrast, while still significant, the supply chain layer shows a comparatively sparse and less clustered arrangement of nodes. This suggests that while market cooperation is prevalent, it tends to be more dispersed and possibly involves a wider variety of less intensive partnerships.
The visual comparison of these two layers in Figure 9 provides critical insights into the dynamics of corporate cooperation. It highlights the current trend where technology-driven partnerships are becoming more central to business strategies than traditional market collaborations. This tendency underscores the evolving nature of corporate relationships in the digital age, where technological prowess and innovation are key drivers of business success and industry leadership.
4.2. Graph Conversion
Data from the supply chain layer are integrated into the technology layer through graph transformation rules. As shown in Figure 10, this process is a critical step in understanding the potential intersections and synergies between technological partnerships and supply chain collaborations.
The underlying premise of the approach is based on the hypothesis that supply partnerships between firms can indicate potential technological cooperation opportunities. This assumption is rooted in the observation that companies engaged in technological collaborations often share compatible goals, resources, and capabilities, which could be leveraged in supply chain contexts. Graph transformation rules were applied to operationalize this concept, enabling the inference of potential supply chain relationships from existing technological partnerships.
Figure 10 demonstrates the integration’s manifestation within the network structure. When two firms are connected in the supply chain network, this relationship is transposed into the technology network, suggesting a potential avenue for technology cooperation. This methodology enables us to create a more enriched and interconnected network model, where the lines between technology and supply chain interactions are blurred, reflecting the multidimensional nature of modern business relationships.
This approach has significant implications for strategic planning and decision-making in businesses. Through identifying potential technological cooperation opportunities from supply partnerships, companies can explore new avenues for collaboration, enhance SCR, optimize their SCM, and potentially gain a competitive advantage. It also provides insights into the evolving nature of business ecosystems, where technology and supply chains are increasingly interlinked, driving innovation and efficiency.
4.3. Graph Representation Learning
In preparation for the link prediction task within the study, the GATNE model was set as the default parameter setting. This approach was instrumental in converting the nodes of the graph into 200-dimensional vectors. The selection of 200 dimensions for the vector representations was based on a balance between computational efficiency and the capacity to capture the complex relationships and attributes inherent in the network’s nodes.
The representation learning models are divided into four groups. The inputs for each model were created from the network in a suitable form. Subsequently, the embedding results were collected, and the classification performance of each model was evaluated using a logistic regression classifier. For the DeepWalk and Node2vec models, the walk length was set to 40, the number of walks to 10, and the window size to 5. The number of training epochs for the neural network models was 200, with a learning rate of 0.001. The output dimension for all models was established at 128. The DeepWalk, LINE, and Node2vec models were built using the TensorFlow 1.14 framework, the GATNE model was constructed using PyTorch, and the metrics were developed using the sklearn package in PyCharm. The performance of each model on each dataset is described in Table 2.
A fine-grained breakdown of the performance metrics of the various models within the aggregated network framework is provided in Table 2, with particular emphasis placed on comparative analysis. The empirical results derived from the research clearly show that the GATNE model exhibits a superior performance in terms of both accuracy and efficiency. This finding is crucial as it validates the effectiveness of the proposed framework, especially in the characterization of nodes within the network. The GATNE model, with its advanced architecture, excels in capturing the complex relationships and attributes of the nodes, thus providing a more detailed and accurate characterization of the network. This ability is attributed to its ability to incorporate node- and edge-specific information for enhanced learning dynamically.
4.4. Link Prediction
To improve the company’s SCR and SSCM, we use link prediction to identify defining characteristics, anticipate market convergence, and provide actionable recommendations. Its purpose is to evaluate the effectiveness of a proposed framework that involves training on a residual graph by concealing a set of edges/non-edges. This is achieved by passing through the original network. The dataset is divided into training, testing, and validation sets based on 75%, 15%, and 15%, respectively. The training set randomly selects 5% of positive edges, while the testing set selects 10%. An equal number of opposing edges are chosen randomly for each edge type. The validation set is utilized to fine-tune the hyperparameters and for early stopping. The test set is used for performance evaluation and only runs once with the tuned hyperparameters. The framework’s link prediction accuracy is 98%.
Based on the predictive analysis of the link prediction model, we have identified several prospective technological partnerships between firms, as shown in Table 3. These link predictions highlight the possibility of enhanced technological synergies and reflect the dynamic nature of industry partnerships in a rapidly evolving corporate landscape.
The results of link prediction were evaluated by calculating the network resilience, as demonstrated in Figure 11. This is a conventional measure of network toughness. In Figure 11, the x-axis represents the number of removed nodes, and the y-axis represents the maximum number of connections. The results demonstrate that the inclusion of predicted links enhances network resilience, which is a crucial discovery for network analysis.
Building on the insights gained from the link prediction results, it can offer informed recommendations to organizations aiming to enhance their SCR and SSCM. These recommendations are predicted on the understanding that the strategic insertion of predicted links can substantially reinforce the supply chain network, thereby mitigating risks and vulnerabilities. This approach is particularly relevant in an era where supply chains are increasingly complex and interdependent. By leveraging the findings from our framework, organizations can identify potential weak links in their supply chains and proactively strengthen these areas, ultimately leading to more resilient and sustainable supply chain practices. Moreover, our analysis underscores the importance of adopting advanced analytical tools in SCM, enabling organizations to navigate the challenges of today’s dynamic business environment more effectively.
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