Adoption of Industry 4.0 Technologies in Chilean Mining: A Comparative Analysis Between Sectors

Abstract

Industry 4.0 represents a crucial technological revolution for the modernization and competitiveness of companies, offering tools that enhance the efficiency, productivity, and sustainability of industrial processes. Adopting these technologies is essential, especially in crucial sectors such as mining, where their implementation can radically transform operations. This study investigates the adoption of Industry 4.0 technologies among mining and non-mining companies in Chile, using data from the Survey of Access and Use of Information and Communication Technology in Companies. A Principal Component Analysis (PCA) identified the main variables influencing technological adoption. The results indicate that mining companies are significantly more advanced in integrating technologies such as ERP, SCM, and Big Data, which optimize their operational processes and strengthen their competitiveness. In contrast, non-mining companies show a more dispersed adoption, which could limit their capacity for innovation. These findings underscore the importance of developing differentiated public policies that promote technological adoption in SMEs and less advanced sectors, also encouraging the development of internal capacities and collaboration between businesses and government to accelerate digital transformation.

1. Introduction

Industry 4.0 (4IR) has become a topic of increasing interest in recent years, driven by the rapid development of digital technologies such as artificial intelligence, large-scale data analytics, cybersecurity, and Cloud Computing. These technologies offer opportunities to enhance industrial processes’ efficiency, productivity, and sustainability [1,2]. This technological surge is also impacting the mining industry; the concept of Mining 4.0 is transforming the sector through digital technologies like AI, IoT, and Big Data analytics, improving productivity and reducing environmental impact [3,4]. However, the adoption of these technologies is challenging. Technological adoption is difficult, especially in job design and training, due to a skills shortage [5]. It is concerning that there is a delayed and uneven transformation rate across industries, regardless of their country of origin [6]. This fundament is particularly relevant in Chile, where mining is fundamental to the national economy.

Mining is a significant pillar of Chile’s economy, accounting for approximately 12% of the country’s GDP in 2022 [7]. The sector is crucial not only for national economic stability but also for global mineral markets, particularly in the production of copper and lithium. Chile is the world’s leading copper producer and a major supplier of lithium, which is essential for global industries such as electronics and electric vehicles [8]. The impact of copper mining on Chile’s environmental sustainability and social structures has been significant, influencing policy making and community relations [9]. Meanwhile, implementation is progressing rapidly in developed countries. Canada and Australia are leading nations in adopting Industry 4.0 technologies in mining. Canada has embraced Industry 4.0 technologies in its mining sector, strongly focusing on automation, data analytics, and sustainability. The country’s mining companies are increasingly adopting these technologies to improve efficiency and reduce costs [10]. Canada has also made strides in sustainability by using these technologies to reduce carbon emissions and energy consumption, aligning with global environmental standards [11]. Australia is considered one of the leading countries in adopting Industry 4.0 technologies in the mining sector, driven by industry needs and government support [12].

Industry 4.0 extends its presence and impact across multiple industrial sectors, including mining, manufacturing, construction, and services [13,14,15,16]. However, research on implementing Industry 4.0 technologies among industries is sparse [17]. Understanding adoption patterns across different economic sectors and geographic regions is crucial for rapidly adapting to changes associated with the development of new technologies and the digitalization of the economy and for orienting efforts based on evidence. For example, in Europe, Industry 4.0 is more influenced by industry than country. Thus, tailored public policies are recommended based on the country/industry combination [18].

Thus, it is in the interest of countries, regions, and companies to adapt as quickly as possible to the changes associated with developing new technologies and the digitalization of the economy. This study seeks to fill the gap in the literature by analyzing and comparing the level of implementation of 4IRtechnologies between mining and non-mining companies in Chile, using the Principal Component Analysis (PCA) technique to identify the factors that influence such adoption. This analysis is expected to provide valuable information for decision-makers and business leaders on better taking advantage of the opportunities offered by 4IRin the Chilean context.

1.1. Industry 4.0

4IR represents the fourth industrial revolution, revolutionizing multiple sectors by integrating advanced technologies that enhance operational efficiency and promote digital transformation, creating a highly interconnected and automated ecosystem [19,20]. Implementing these technologies in Chile’s mining context is crucial to maintaining competitiveness in a dynamic global market. This study focuses on several key 4IR technologies: Enterprise Resource Planning (ERP) software (version 1.1), Customer Relationship Management (CRM) software (version 1.1), electronic exchange of information in the production chain (SCM), Big Data, RFID, Cloud Computing, and cybersecurity.

ERP software facilitates the integration of business processes in finance, manufacturing, and supply chain management, providing a unified view of operations. Its adoption in 4IR supports real-time strategic and operational decision-making, which is crucial for responding to global market dynamics [21]. Within 4IR, CRM software manages customer relationships and optimizes customer service by leveraging real-time data, enhancing customer satisfaction, and boosting sales [22]. SCM integration in 4IR enables efficient supply chain synchronization, from material supply to product delivery, using information technologies to facilitate communication and efficient operation among business partners [23]. Big Data plays a transformative role in 4IR, offering advanced data analysis capabilities that enable significant discoveries and process optimization by analyzing large volumes of data in real time [24,25]. RFID technology automatically tracks assets and products through production and logistics stages, improving operational efficiency and inventory management [26]. Cloud Computing provides a scalable and flexible platform for data storage and processing, facilitating collaboration and access to critical information anytime and anywhere, which is essential for distributed mining operations [27]. Lastly, given the increased connectivity and reliance on digital systems, cybersecurity is fundamental to protect sensitive data and critical operations from internal and external threats, ensuring the integrity and availability of 4IR systems [20].

2. Materials and Methods

2.1. Data

The data are extracted from the Survey of Access and Use of Information and Communication Technology (ICT) in Companies, reference year 2018. This survey was jointly developed by the National Institute of Statistics and the Studies Unit of the Ministry of Economy, Development, and Tourism of Chile. The ICT survey covers 170,297 companies, excluding companies with annual sales equal to or less than 2400 UF. It also has representativeness at the company size level (small, medium, and large, defined according to sales). Figure 1 presents the sample composition of the companies analyzed.

The objective of the survey is to gather qualitative and quantitative information from companies on the access and use of information and communication technologies across various economic activities. It measures variables such as the use and access of ICT and internet services, electronic information management, security, human capital, and e-commerce in line with the guidelines of the Organization for Economic Cooperation and Development (OECD). The aim is to achieve international and national comparability over time.

Data from the 2018 survey are still relevant for the mining industry. The adoption of Industry 4.0 technologies in the mining sector is a gradual process that typically extends over several years due to the scale and complexity of the required changes, both in technological and organizational domains [28]. Many mining companies are still in the early or intermediate stages of adopting these technologies [2].

2.2. Variables

The variables to be analyzed include management information systems, Big Data, Radio Frequency Identification (RFID), Cloud Computing, security and privacy, and company size (see Appendix A for more details). These variables are analyzed considering the entire sample, then segmented by mining and non-mining companies, using the ISIC (International Standard Industrial Classification) definition. Principal Component Analysis is applied to these variables.

The variables selected for this study—management information systems (specifically ERP and CRM), Big Data, Radio Frequency Identification (RFID), Cloud Computing, and cybersecurity measures—are fundamental components of Industry 4.0, as outlined in Section 1.1. These technologies collectively enable the development of highly interconnected, automated, and data-driven industrial ecosystems characteristic of the fourth industrial revolution [20,29,30,31].

A data cleaning and preparation process was carried out before the analysis using the Principal Component Analysis (PCA) technique. This process included eliminating observations with missing values and excluding previously answered questions, whether or not the response was considered. Finally, the possible outliers were reviewed, and it was decided to keep those that reflect real situations of the business context, contributing to a more accurate and representative analysis of the use of technologies in Chilean companies.

2.3. Principal Component Analysis

Principal Component Analysis (PCA) aims to reduce the dimensionality of data. Given n observations of p variables, PCA examines whether it can adequately represent this information with a smaller number of variables constructed as linear combinations of the originals. This method optimally represents observations in a smaller-dimensional space from a general p-dimensional space. Additionally, PCA transforms the original, generally correlated variables into new, uncorrelated variables, facilitating data interpretation [32,33].

Let u₁ and u₂ be the eigenvectors of the covariance matrix (SX) of the variable X. The first principal component (Y1) and the second principal component (Y2) are defined as the linear combination of the variables (Y1 = Xu₁ and Y2 = Xu₂). In this case, the sum of the variances of Y1 = Xu₁ and Y2 = Xu₂ must be maximized, and u₁ and u₂ define the projection plane of the variables X. The objective function will be

$$\varnothing =u_1^{\prime }{S}_X{u}_1+u_2^{\prime }{S}_X{u}_2-{\lambda }_1(u_1^{´}{u}_1-1)-{\lambda }_2(u_2^{´}{u}_2-1)$$

with $u_i^{\prime }{u}_i=1$, for $i=\mathrm{1,2}$.

This technique does not have constraints regarding the types of variables analyzed; they can be continuous, ordinal, binary (0/1), or a mix of these. The primary objective of PCA—to summarize the most significant portion of the “variation” present in the original set of variables using a smaller number of derived variables—can be achieved regardless of the nature of the original variables [34]. Thus, this study uses normalized PCA with orthogonal Varimax rotation [35,36]. This approach maximizes the principal components’ interpretability by making them as statistically independent from each other as possible.

3. Results

3.1. Descriptive Statistics

This section presents the results obtained from the data analysis, starting with the descriptive statistics. These statistics provide an overview of the fundamental characteristics of the studied variables.

Table 1. Descriptive Statistics.

		Total Sample	Mining Companies	Non-Mining Companies
Management Information Tools	X1	55.59	42.11	56.15
	X2	17.7	6.02	18.19
	X3	8.07	10.53	7.97
	X4	7.89	6.77	7.94
Big Data	X5	7.36	9.02	7.29
	X6	4.16	7.52	4.02
	X7	2.21	6.02	2.06
	X8	1.67	0.75	1.71
Radio Frequency Identification	X9	14.92	6.02	15.29
	X10	4.01	5.26	3.96
	X11	1.2	2.26	1.15
Cloud Computing	X12	42.02	29.32	42.54
Cybersecurity	X13	22.34	18.05	22.52
Size	Large	51.53	2.1	97.9
	Medium	17.34	5.2	94.8
	Small	31.13	6.3	93.7
Total	N	3344	133	3211
	%	100	3.98	96.02

shows the average values obtained for the entire sample and the specific averages for companies in the mining industry and those that do not. This breakdown allows us to compare the characteristics of different groups within the sample and to analyze possible differences between the mining and non-mining sectors. The details of the variables are presented in Appendix A.

Use of Management Information Tools: A total of 55.59% of companies have used Enterprise Resource Planning (ERP) software to integrate process and information management (X1). In the mining sector, 42.11% of companies report using ERP software, compared to 56.15% in non-mining companies. Furthermore, 17.7% of the total companies have implemented Customer Relationship Management (CRM) software to manage information related to their customers (X2). In the mining sector, only 6.02% use CRM software, while in non-mining companies, this percentage is 18.19%.

Electronic Information Exchange: A total of 8.07% of firms share information with suppliers using Supply Chain Management (SCM) systems (X3). This practice is more common in mining companies (10.53%) than in non-mining firms (7.97%). Additionally, 7.89% of firms electronically share production chain information with their customers through SCM systems (X4). In the mining sector, 6.77% of firms engage in this practice, whereas the figure is 7.94% in the non-mining sector.

Big Data Analysis: Only 7.36% of firms perform Big Data analysis (X5). This percentage is slightly higher in the mining sector (9.02%) compared to the non-mining sector (7.29%). Furthermore, 4.16% of firms analyze large volumes of data from sensors or smart devices (X6). This percentage rises to 7.52% in mining companies, whereas in non-mining companies, it stands at 4.02%.

Big Data from Geolocation and Social Media: A total of 2.21% of firms utilize massive data obtained through the geolocation of portable devices (X7). This practice is more common in the mining sector (6.02%) compared to the non-mining sector (2.06%). Additionally, 1.67% of firms analyze massive data generated from social media for Big Data purposes (X8). Only 0.75% use these data in the mining sector, whereas the figure is 1.71% in the non-mining sector.

RFID Technology Usage: A total of 14.92% of firms use Radio Frequency Identification (RFID) technology to identify individuals or control access (X9). This usage is 6.02% in the mining sector, whereas in the non-mining sector, it is 15.29%. Additionally, 4.01% of firms use RFID in production or product delivery (X10), with 5.26% in mining companies and 3.96% in non-mining companies. Moreover, 1.2% of firms use RFID to identify products after production (X11). In the mining sector, the usage is 2.26%, compared to 1.15% in the non-mining sector.

Cloud Computing Services Usage: In 2018, 42.02% of firms used paid Cloud Computing services (X12). In the mining sector, this percentage is 29.32%, while in the non-mining sector, it is 42.54%.

Cybersecurity: A total of 22.34% of firms have a dedicated area or role for IT security (X13). In the mining sector, this percentage is 18.05%, while in the non-mining sector, it is 22.52%.

Figure 2, Figure 3 and Figure 4 show the acceptance of management information tools, Big Data, and RFID, respectively.

3.2. Analysis

The normalized Principal Component Analysis (PCA) results for all firms presented in

Table 2. Eigenvalues of the correlation matrix and variance: all firms.

	Eigenvalue	Proportion of Variance	Cumulative Variance
1	3.8001	23.7506	23.7506
2	1.8706	11.6912	35.4418

reveal that the first two principal components explain 35.44% of the total variance in the data. When focusing exclusively on mining companies (

Table 3. Eigenvalues of the correlation matrix and variance: mining companies.

	Eigenvalue	Proportion of Variance	Cumulative Variance
1	5.0145	31.3406	31.3406
2	1.8931	11.8319	43.1725

), this percentage increases to 43.17%, indicating a more significant proportion of variance explained by the first components in this sector. In contrast, for non-mining companies (

Table 4. Eigenvalues of the correlation matrix and variance: non-mining companies.

	Eigenvalue	Proportion of Variance	Cumulative Variance
1	3.7555	23.4719	23.4719
2	1.8723	11.7019	35.1738

), the variance explained by the first two components slightly decreases to 35.17%. These results suggest that mining companies exhibit a higher concentration of their characteristics, reflecting less variance dispersion than non-mining companies.

Figure 5 shows the circle of correlations between the variables with the first and second principal components. In this graph, the relationship of the variables with the axes is observed by having the same variables in a particular quadrant. Thus, if two variables are strongly correlated with each other and the axis, they are in the same quadrant. Given this, the first principal component (X-axis) of the analysis of the clustering variables of the mining companies is related to the adoption of integrated management tools and information sharing within the production chain. This component includes the use of enterprise resource planning (ERP) software (X1), customer relationship management (CRM) software (X2), and electronic information exchange with suppliers (X3) and customers (X4) through supply chain management (SCM) systems. The strong association between these variables suggests that mining companies prioritize integration and efficiency in their operational and management processes, highlighting the importance of these technologies in optimizing internal and external coordination within the sector.

The second main component (Y-axis) groups variables that reflect the advanced use of data analytics and product tracking technologies. This component includes Big Data analysis (X5), the use of massive data obtained from sensors or smart devices (X6), the use of geolocated data (X7), and the use of Radio Frequency Identification (RFID) systems for post-production product tracking (X11). This indicates that mining companies are focused on improving operational accuracy and efficiency through cutting-edge technologies, enabling them to manage and analyze large volumes of information to optimize production processes.

In the mining industry, operational efficiency, management resources, and supply-chain coordination are critical to competitiveness [37]. The technologies included in the first and second principal components are useful tools for achieving such purposes in complex and large-scale mining operations. Mining firms possess the resources and technical experience to perceive a relatively easy use of such technologies.

Figure 6 shows the correlations between the variables and the principal components for non-mining companies. It is obtained in the same way as Figure 5, only for the non-mining sample. The analysis reveals that, for non-mining companies, the first principal component (X-axis) clusters variables related to the use of management tools and company size. Specifically, this component includes the use of Enterprise Resource Planning (ERP) software (X1), Customer Relationship Management (CRM) software (X2), and the company’s large size. The association of these variables suggests that larger companies adopt these integrated management technologies, allowing them to manage their operations and internal processes more efficiently.

The second principal component (Y-axis) clusters variables associated with information sharing and advanced data analysis. This component includes electronic information exchange of the production chain with customers through specific systems (X4), Big Data analysis (X5), and the use of various sources of massive data for such analysis: large volumes of data obtained from sensors or smart devices (X6), massive data from geolocation via portable devices (X7), and data generated from social media (X8). This component focuses on analyzing and managing large amounts of data to enhance decision-making and operational efficiency within companies. Details are presented in 6.

It is important to highlight that the results obtained through Principal Component Analysis (PCA) provide a statistical perspective on technology adoption and carry practical implications for both the mining and non-mining sectors. The concentration of the first two principal components around technologies such as ERP software, SCM, and Big Data analytics in the mining sector suggests an integrated approach to process optimization and efficient resource management. This indicates that mining companies are well positioned to address complex operational challenges and capitalize on opportunities in a globalized market, thereby enhancing their competitiveness through advanced technological adoption.

In contrast, the results for the non-mining sector show a more dispersed adoption of these technologies, presenting additional challenges related to integration and operational efficiency. The practical implications for this sector include building internal capacity and offering more targeted support, such as incentives for adopting integrated management technologies and data analytics systems. These findings suggest that public policies should be tailored to encourage technology adoption in larger companies and provide targeted support to non-mining SMEs to close the digital divide and foster innovation.

4. Discussion

The results highlight significant differences in the adoption and use of technologies between mining and non-mining companies in Chile, directly affecting innovation efficiency and business performance. The analysis revealed that mining companies are more concentrated on using integrated management tools and advanced data analysis technologies. This is reflected in the higher variance explained by the first principal components in the mining sector compared to the non-mining sector. Specifically, the adoption of Enterprise Resource Planning (ERP) software and Supply Chain Management (SCM) systems is more prevalent among mining companies, suggesting a focus on integration and operational efficiency.

Adopting technologies such as ERP software, CRM software, and SCM systems is critical for improving internal and external coordination of operations in mining. These systems enable better management of resources and processes, optimizing the flow of information between suppliers and customers, which is essential in the complex and demanding industrial environment of mining. On the other hand, Big Data analysis and using technologies like RFID for tracking post-production products emphasize precision and operational efficiency, which is crucial for maintaining competitiveness in a globalized market.

While mining companies appear to be more advanced in adopting integrated technologies and data analysis, non-mining companies show a lower degree of adoption, limiting their ability to innovate and improve performance. The lower variance explained in non-mining companies indicates greater dispersion in technology adoption, which may be related to less integration of these technologies into their operational processes.

From a theoretical perspective, it is of interest to extend this analysis to technology acceptance models, such as the Theory of Reasoned Action (TRA) and the Technology Acceptance Model (TAM), as well as the role of technology in organizational change, such as the Adaptive Structuration Theory (AST) [38].

To improve innovation performance, especially in non-mining companies, it is essential to promote the adoption of integrated management technologies and advanced data analysis tools. This would optimize operational efficiency and enhance the ability to respond to market demands and competitive pressures. Furthermore, fostering cross-sectoral collaboration and shared use of technologies such as Big Data and RFID could bridge the gap between sectors, allowing non-mining companies to benefit from the same efficiencies and competitive advantages observed in the mining sector. From this perspective, it is interesting to deepen the theoretical analysis considering the digital capabilities of different economic sectors [39], and systemic approaches to innovation, such as technological innovation systems, the quadruple helix, and open innovation, among others [40,41,42,43].

5. Conclusions

This study has identified key differences in adopting Industry 4.0 technologies between mining and non-mining companies in Chile. The findings reveal that mining companies exhibit a higher integration of advanced management tools, such as ERP software and SCM, and more significant use of data analysis technologies, suggesting they are better positioned to leverage the opportunities of Industry 4.0. In contrast, non-mining companies show lower adoption of these technologies, which could limit their ability to innovate and enhance their operational efficiency.

The dataset used in this study, derived from the Survey on Access and Use of Information and Communication Technology in Enterprises, provides a robust foundation for assessing the penetration of key Industry 4.0 technologies across different sectors. Using Principal Component Analysis (PCA) allowed for identifying the main variables influencing technological adoption, highlighting areas where companies must focus their efforts to remain competitive in an increasingly digitalized industrial environment. This methodological approach has proven effective in capturing the complexity of digital transformation in the industry and offering valuable insights for policy formulation and business strategies in the era of Industry 4.0.

The results of this study have direct implications for formulating public policies aimed at incorporating Industry 4.0 technologies into businesses. These policies must consider the significant differences in technological adoption between large enterprises and SMEs, as well as between sectors such as mining and non-mining.

Due to their resources and capabilities, policies should recognize that large enterprises are generally better equipped to adopt advanced technologies such as ERP software, SCM, and Big Data. However, SMEs often need more resources and a more tailored support approach. This may include tax incentives, subsidies for technology implementation, and specific training programs that enable them to integrate into the Industry 4.0 era fully. Promoting collaboration between large enterprises and SMEs could also facilitate the transfer of knowledge and resources, accelerating technological adoption in the SME sector.

Including I4.0 technologies should not be limited to acquiring software and hardware; focusing on developing people at all company levels is crucial. This involves creating continuous training programs that enhance employees’ digital and technical skills, from operators to top executives. For example, training in ERP and CRM systems for administrative staff, education in data analysis and Big Data for analysts and managers, and cybersecurity workshops for IT teams. Additionally, operators could receive training in IoT technologies and industrial process automation. Developing internal capabilities ensures that technologies are adopted and used effectively to drive innovation and improve business performance.

The government plays a fundamental role in promoting technological inclusion, especially among those companies with fewer capabilities to adopt Industry 4.0. Public policies should include designing technical and financial support programs for SMEs, facilitating access to key technologies, and reducing the digital divide between large and small companies. Additionally, it is essential to foster a collaborative environment between the public sector, businesses, and educational institutions to create robust innovation ecosystems. This can be achieved through innovation consortia, co-development programs, and support networks that allow companies to share knowledge, experiences, and resources.

Countries such as Germany, South Korea, and France have implemented public policies that have significantly accelerated the adoption of Industry 4.0 technologies. In Germany, the ‘Industry 4.0’ strategy has promoted a comprehensive transformation of its industry through collaboration between the public and private sectors, facilitating the adoption of technologies such as IoT, cybersecurity, and Big Data analysis in the manufacturing sector [44]. This approach has been key to maintaining the global competitiveness of German companies, combining economic incentives with a technological innovation ecosystem. In South Korea, tax incentives and collaborative research and development (R&D) programs have a fundamental role in adopting advanced technologies. The government has incentivized the implementation of Big Data and automation solutions through strategic partnerships between companies and research centers, which has improved operational efficiency and fostered innovation within the industrial sector [44]. These programs have benefited large companies and helped SMEs integrate into Industry 4.0. For its part, France, through the ‘Industrie du Futur’ program, has implemented tax incentives and promoted the creation of technology clusters to accelerate the adoption of automation and digitalization technologies, especially among small and medium-sized enterprises. This strategy has enabled greater collaboration between companies, universities, and research centers, facilitating the development of advanced technological projects and improving the competitiveness of the French manufacturing sector [45]. These examples can serve as useful models for designing policies in Chile that promote technological adoption in less advanced sectors, fostering both competitiveness and industrial innovation.

These practical implications underscore the need for inclusive and differentiated policies that drive digital transformation across all sectors and levels of the economy. These policies should ensure that the benefits of Industry 4.0 are accessible to all companies, regardless of their size or sector.

While Principal Component Analysis (PCA) is a powerful tool for reducing data dimensionality, this approach also has limitations. For instance, PCA assumes that the original variables are linearly related, which may not fully capture the complexity of relationships among variables in real-world business contexts. Additionally, this study does not incorporate cultural, organizational, or managerial factors that could influence the adoption of Industry 4.0 technologies. These factors could play a crucial role in companies adopting and utilizing these technologies, especially in specific contexts such as mining in Chile. Therefore, future research could explore other methods or techniques to include these diverse factors.