Eight Conditions That Will Change Mining Work in Mining 4.0

Abstract

The mining industry is undergoing a transformation driven by the adoption of Industry 4.0 technologies, implementing autonomous trucks, drones, positions systems, and similar technologies. This article, drawing on experiences and observations from several studies conducted in the mining industry, explores the impact of these technologies on mining work. It identifies eight key potential changes in working conditions. Firstly, routine and dangerous tasks are increasingly automated, reducing physical strain but potentially leading to job displacement and increased maintenance demands. Secondly, operators and managers are shifting toward handling disturbances and training algorithms, as AI takes over decision-making processes. Thirdly, managers are responsible for more capital with fewer people, potentially altering managerial roles and spans of control. Fourthly, the global connectivity of operations makes the world both larger and smaller, with a universal language blurring boundaries. Fifthly, work becomes location-independent, allowing for remote operation and management. Sixthly, the distinction between work and private life blurs, with increased availability expected from operators and managers. Seventhly, technology expands human senses, providing real-time data and situational awareness. Eighthly and lastly, the pervasive collection and retention of data create a scenario where one’s history is inescapable, raising concerns about data ownership and privacy. These changes necessitate a strategic response from the mining industry to ensure socially sustainable technology development and to attract a future workforce.

1. Introduction

The mining industry is currently experiencing many changes. External factors such as global warming, increased international competition, and growing challenges in recruiting mineworkers are placing new demands on mining operations. Consequently, like many other industries, the mining industry has begun to digitalize its operations, driven by concepts like Industry 4.0 [1].

Industrie 4.0 is a strategy introduced by the German government in 2013 [2]. Known in English as Industry 4.0, it is described as the next industrial revolution. Following the eras of the steam engine, electricity, and electronics, this revolution involves implementing the “Internet of Things”, where the entire production process is integrated into internet-based networks, transforming traditional factories into smart factories. Similar concepts have emerged globally. The German vision presents a promising future for industry, where virtual and physical worlds are interconnected into a powerful “whole”. Through integrating software in product development and production, machines will not only perform “physical work” but also carry out calculations [3,4]. This is described as cyber-physical systems, or even socio-cyber-physical systems, where smart ventilation, smart logistics, smart maintenance, smart machines, and other smart systems continuously exchange information with each other and with humans. In this, the German strategy emphasizes the potential for skill development and a more enriching work life with more challenging tasks.

While Industry 4.0 has a strong technical focus, several commentators have highlighted the importance of social aspects in its development. Kagerman et al. [2], for instance, stressed the need for companies to adopt a sociotechnical approach, where employee participation in job design is central. Without such participation, the desired outcomes of Industry 4.0 may not be realized. Other examples include arguments that Industry 4.0 requires a flat organizational structure with more organizational innovations, learning, enhanced human–machine interaction, and a more human-centered perspective on new technology (cf. [5,6,7]). Simultaneously, the German strategy also notes that the demand for employees will decrease as functions like remote controls and preventive maintenance will inevitably lower labor costs and increase the security of the remaining factory staff [2,7].

In the mining industry, some mines have made significant strides toward digitalization, moving toward Industry 4.0 in mining, known as Mining 4.0. For instance, autonomous loaders and trucks are operational at several sites, and autonomous drones are being tested for inspections. Gradually, the mining industry is approaching the visions of Industry 4.0, with fully automated mines and more technologically advanced ore processing facilities. In Mining 4.0, operators would not have to be confined to control rooms. Instead, real-time process data and machine status could accompany miners as they move around the mine, enabling them to solve problems on the spot by remotely interacting with other operators, experts, suppliers, and customers in multi-skilled teams. Production control could even be managed in a digital model (or “digital twin”) far from the mine.

However, technological development often follows its own path and is challenging to influence. It is difficult to resist new technology, as the mining industry must produce at competitive costs in a global market to survive. If mining companies wish to remain competitive, they must adapt to new technology in some way. At the same time, we understand that technology is not predetermined but is shaped by its users. It is within this paradox that mining companies must create future work and working conditions that allow for the socially sustainable use of these technologies. Companies must both “parry” and “act” to ensure the availability of qualified employees in the future: parry by being adaptive to new technology, and act by utilizing the opportunities technology offers to create attractive workplaces that can attract the qualified employees of tomorrow.

To understand the impact on mining work, one must begin with an analysis of new technology. It is the technology that sets the framework for the design of work. While there are reports on how new technology will affect work (e.g., [8,9,10])—including both utopian and dystopian descriptions [1]—this article documents and reports on some of the ways work and work organization in mining are beginning to change. The present article explores how the new technology associated with Industry/Mining 4.0 alters the conditions for work in a modern mining industry; we aim to identify areas likely to be affected by new technology so that mining organizations can act and parry in relation to these changes.

2. Method

This article draws on observations and experiences from multiple field studies and research projects conducted with mining companies, technology developers, and equipment providers. It includes interviews and workshops with managers (such as work environment managers, first-line managers, and section managers), engineers within these organizations, and operators at mining companies. Our aim is to make analytical and theoretical points concerning the changes mineworkers and their managers face when new technology is introduced, rather than to draw general conclusions through empirical generalization (cf. [11]). Therefore, the presentation of the observations prioritizes illustrating phenomena over descriptive depth. The material is not a representative sample but seeks to provide conceptual insights (cf. [12])—insights that highlight the evolving working conditions of mineworkers and their managers.

We draw our observations from four sources. The first source is a comprehensive research project on the organizational and social work environment of managers conducted at a large Swedish mining company. The original research employed both qualitative and quantitative methods, including 20 interviews with managers (one series involved ten managers mapping their weekly activities, and the other involved ten young managers, focusing on their roles and work situations; see [13,14]). In the present study, we use this material to gain further insight into the effects of new technology on managers’ work environments.

Sources two to four are a series of research projects conducted in collaboration with the European mining industry, where we (the authors) were tasked with addressing work environment and work organization issues. These projects, funded by the European Union (EU), focused on developing new technology for the mining industry. The first project was titled Innovative Technologies and Concepts for the Intelligent Deep Mine of the Future (I²mine; 2011–2016), the second was Sustainable Intelligent Mining Systems (SIMS; 2017–2020), and the third was Next Generation Sustainable Intelligent Mining Systems (NEXGEN-SIMS; 2021–2024). The material that we draw on from these sources comes from workshops and interviews with mineworkers, managers, technology developers, salespeople, and similar roles. These sources also include many field studies in European mines.

Drawing from experiences in these two types of projects—one focusing on managers’ experiences in the mining industry and the others on the rapid introduction of new technology in mines—we discuss the impact of technology on the future of work in the mining industry. Our main insights are summarized as eight changes to working conditions that the mining industry must address in some manner (See Section 3.2).

3. Results

The results of this article are divided into two parts. First, we describe the technologies that have recently been introduced in the mining industry or have seen increased and broader applications. Then, we report on some phenomena regarding the effects of these new technologies and their usage.

3.1. The Technologies That Open Up the World

3.1.1. Automation and Robotics

Automation aims to replace or reduce human efforts in production, both physically and cognitively. Robotics is essential for automation, as it can take over physical tasks like lifting, drilling, or welding. Robots can be equipped with various sensors, allowing them to be integrated into automated systems. These systems can then be connected to larger networks, often referred to as the Internet of Things. In mining, this is evident with automated drill rigs, chargers, and even semi-autonomous loaders. There is also significant automation in decision support and decision-making, both in daily production control and personnel management.

Modern internet-based production systems often have global nodes, making it challenging to see and understand the entire system. This is often described as being “in the cloud”. Of course, it is all actually stored in computers located somewhere on Earth, but the manner of speech serves to illustrate how technological development blurs boundaries and challenges past ways of thinking in terms of technology.

3.1.2. Big Data

Most production activities, including human activity, are recorded and stored in various databases, often referred to as big data. This vast amount of data can be collected in numerous ways and includes texts, social media, sensor data, sounds, images, and videos. Essentially, anything that can be digitized can become part of big data for further analysis. If the data are well structured, it can be relatively easy to find and compile the needed information. However, the diversity and size of data sets often make it challenging to extract meaningful information using conventional methods. AI and machine learning can then be used to find patterns and structures in the data set, a process sometimes called Data Mining. This term, not specific to mining, refers to the (data) extraction process in general. Algorithms are developed to discover patterns and relationships, extracting information or forming hypotheses about how a phenomenon should be interpreted.

In mining, many modern pieces of equipment continuously collect and store data. Some new technologies gather data related to equipment movement. Algorithms analyze these data to classify equipment activities, such as tramming, drilling, or idling. This information feeds into production planning algorithms, which may automatically allocate additional resources to underperforming areas.

3.1.3. Internet of Things and Advanced Sensors

The Internet of Things is a framework where devices connected to the Internet or other networks can communicate and exchange information. These devices can be equipped with various sensors to measure and collect data about the production system’s state. Devices can be connected locally or as part of a larger, often cloud-based, processing system distributed across multiple locations.

In recent years, many mines have seen significant development in this area. Not long ago, mines were among the few places with “non-connectivity”, but today, it is not uncommon to use a phone underground (social media use in breakrooms, for instance, is very common). In mining, positioning systems now work by analyzing which access point a device (e.g., a smartphone) is connected to.

3.1.4. Virtual and Augmented Reality

VR stands for “Virtual Reality”, and AR stands for “Augmented Reality”. With VR, one is fully immersed in a virtual world, seeing a digital simulation of, for example, a production system, usually using a VR helmet or glasses. With good VR equipment, one can explore and interact with the virtual environment as if it were real. VR technology has long been used for educational and training purposes in mining.

With AR technology, the operator visually remains in the physical environment, combining it with digital elements projected into their field of vision through smart glasses. It is also possible to visualize the environment directly in contact lenses, making the technology feel almost like a part of the human body.

These new technologies expand employees’ senses, allowing them to take in new types of information. With VR, one can simulate events, foresee future actions, and even see around corners. A fully VR-represented production system allows control to be independent of location, following the operator. Managers become less dependent on location while coordinating a process that can be globally distributed.

3.1.5. AI and Machine Learning

Artificial Intelligence (AI) involves creating intelligent systems, while machine learning (ML) is a technique within AI that focuses on training computers to learn from data. ML is a tool for building intelligent systems and applications. If a system or machine can regulate itself without human intervention, it is considered autonomous. Generative AI, which can create something new, has recently impacted many contexts, including mining. Unlike previous AI forms based on algorithms designed to control processes, generative AI is built on language models that predict events based on typical human actions in similar situations. It is not based on pre-constructed algorithms but on how questions are linguistically formulated and the defined context, which can be text-based or image-based.

Many systems use AI, often without our awareness, including in mining. The use of generative AI is still relatively uncommon, but new applications are continuously emerging, such as using generative AI for safety training.

3.2. Working in the New Digitized Context

In this section, we describe how work in the mining industry has begun to change with the advent of the technologies described above. We categorize these results as phenomena illustrating how technology is transforming mining work.

3.2.1. Routine and Dangerous Work Are Automated and Disappear, but with Some Downsides

Automation offers many benefits, primarily by moving operators from hazardous production environments to well-designed control rooms or centers, either underground or aboveground. Physical strains from heavy work, dangerous gases, and harmful noise are eliminated. Historically, remote-controlled trains, loaders, and mine trucks, as well as semi-automatic drilling, have existed since the 1980s. Our recent studies show this trend continuing, with developments like autonomous charging and hard-rock roadheaders enabling further remote control and automation. The latest advancement is autonomous drones for tunnel and shaft inspections with minimal human interaction.

For maintenance staff, the development can be less positive. For instance, more systems and components can fail. Autonomous systems require extensive infrastructure, mainly for connectivity (through access points, for example), and automatic systems need more predictable environments, such as smoother roads. Direct repairs are becoming less common, partly because new mining technology often comes in digital form. We have seen examples where black boxes containing sensors, edge computing, and connectivity components are simply installed onto mine trucks. In other cases, infrastructure components are treated almost as consumables: access points damaged by blasting are replaced with new ones. This challenges the notion of automation enhancing safety; for example, consider a situation where an access point must be installed for an autonomous machine to perform dangerous work.

Finally, there is a growing concern that jobs will disappear and that individual skills may no longer be in demand.

3.2.2. Operators and Management Focus on Handling Disturbances and Training Algorithms

With AI developments in the mining industry and beyond, operators may find their decision-making gradually transferred to machines, initially as advice and eventually built into systems. AI’s decision-making is based on vast amounts of data and how operators have previously acted in similar situations. For example, AI can analyze images of freshly blasted muck piles to identify ore boundaries or detect cracks in tunnel roofs (perhaps taken by a drone). For this to be possible, humans must first train the AI by identifying muck pile boundaries or cracks in tunnel roofs, either explicitly or implicitly. Then, the AI system can autonomously make classifications. However, an operator must still assess the AI’s judgment or classification as correct or not. Even if AI often “knows” better than the operator (as it can base decisions on more data), AI can still make mistakes, and someone must detect these errors. This can lead to operators increasingly being controlled by machine-generated algorithms, potentially described as algorithmic leadership.

Managers in mining are also affected by these developments. Similar to the reasoning above, administrative tasks can be “learned” by AI. We already see this today, in simple forms, where a manager’s email is automatically sorted as prioritized or non-prioritized. While this development is not unique to mining, it may play a more central role as other developments (see below) make communication through email and similar services more common.

3.2.3. Managers Become Responsible for More Capital Distributed Among Fewer People

Successful new technology increases productivity, meaning fewer people are needed to do the same amount of work. In positive cases, this is managed through natural attrition, such as retirement and retraining. In some mining organizations that we studied, many first-line managers have (too) many subordinates, making it difficult to manage all tasks. Thus, we see a likely development of decreasing managerial control spans. However, with a more technology-reliant workplace, managers become responsible for more capital. Such developments may conflict with what attracts many new managers to the role: working with people.

Conversely, digital technology could be used to increase the number of subordinates reporting to a manager. This could involve relying on AI for administrative tasks and communication with subordinates, using distance-bridging technology to manage remote workplaces and utilizing big data to gather information typically obtained through subordinate interaction.

3.2.4. The World Grows Larger While Becoming Smaller, and a Universal Language Blurs Boundaries

The technology enabling highly automated and autonomous mining operations also allows these operations to be controlled from far away. Prototypes of autonomous LHDs show that their wireless connection to a mine-wide network allows for monitoring and occasional control from outside the mine area. Thus, the world grows smaller as the operator’s work location becomes less significant. Previously, large distances in the mine posed communication challenges between underground and aboveground workers or even between levels. The world grows larger as the previously closed world of the mine opens to the outside. For example, maintenance support can be sought from experts anywhere in the company or the world. Another example is equipment providers taking responsibility for maintenance rather than the mining company, introducing more actors into the workplace and expanding the world.

In one Swedish mining company that we worked with, English is spoken in many white-collar settings, with production units being the last “bastions” of Swedish. To address labor shortages, international recruitment for these positions has been suggested. The technological transition allows for communication in other forms, such as chat or text messages. Here, LLMs could enable each miner to communicate in their native language. In other cases, technological interfaces replace much of the previous verbal communication. An example is systems that automatically classify machine activity, eliminating the need for operators to report their status to a control center.

3.2.5. Work Becomes Independent of Location

As noted earlier, technological development allows work to be conducted from almost anywhere in the world. This trend has been ongoing in the mining industry for some time. What has received less attention is that this also applies to mining managers. Some of our studies on managers were conducted during the COVID-19 pandemic, where we saw managers working almost entirely remotely, holding meetings and even workplace tours digitally. There was doubt about the feasibility of this, but pandemic experiences show it is possible and can even be positive, though there are negative effects to guard against (see the next section). This development is not specific to mining, but its possibility may be less obvious; we want to highlight that it seems a likely development.

3.2.6. Operators and Managers Become Available Around the Clock, Every Day of the Year

If work is no longer location-dependent and distances matter less, the separation between work and free time will start to blur. In other words, operators and, perhaps especially, managers will become available around the clock, every day of the year. Many mines already use shift work to keep operations running continuously. With Mining 4.0 technology, specialists and managers—roles previously spared from shift work—can be called in at a moment’s notice to address technical issues or resolve personnel crises. Indeed, this may be necessary for semi-autonomous mining systems to perform at a high level; none of the systems we have seen can function completely without human oversight and occasional intervention. Thus, to justify the costs of autonomous systems, specialist roles may be available on call rather than on site.

Today, increased availability is facilitated by communication platforms like Microsoft Teams. Chat messages make it easier to communicate outside work hours, and a lot of information is always accessible to managers and workers through the system. More sophisticated systems allow managers and specialists to see the status and operations of, for example, a plant in real time. Technological development in this area is likely to continue growing.

3.2.7. Workers and Managers Are Provided with Expanded Senses (Memory, Vision) and Can See Around Corners and Be Present Everywhere at Any Time

Much of the development in Mining 4.0 focuses on safety by providing workers with expanded senses, allowing them to essentially look around corners and be aware of potential dangers. These developments are important for improving safety in mining work. A less common but still important development is the expansion of senses to most areas of mining work. The technology allows operators to immediately see where a particular truck or piece of equipment is located and even receive directions on how best to get there. The technology can also provide operators with the direct status of other operations in the value flow, allowing them to adapt their work accordingly. Instructions for procedures or risk assessments can also be “pulled up” as needed.

Managers are often overlooked in this context, but they too can have their senses expanded. One possibility is for managers to see the location of all staff. Depending on implementation, such an overview could include health status, such as signs of stress, fatigue, or exposure to excessive noise or vibrations. Combined with personnel files and LLMs, managers could receive support in the social dimensions of leadership, helping them remember past conversations or follow up on issues.

3.2.8. One’s History Becomes Inescapable—Big Brother Not Only Sees You; He Remembers Too

George Orwell’s classic expression Big Brother is watching you [15] becomes more like big data is watching everything—and remembers everything. Big data can be seen as an expansion of an employee’s memory, with almost limitless access to data that can be analyzed more sophisticatedly. The basis for decisions will become larger and broader, potentially leading to better decisions based on so many facts that they are difficult to absorb. Already, we see signs of information overload, which can lead to increased stress and burnout. Moreover, the computer competence of many users of this information struggles to keep up with current systems. A challenge is to sort and evaluate the data so that only relevant information reaches decision-makers. This also involves ensuring that only appropriate data reach the right person, which we have found to be an important factor for the acceptance of technological systems like these. A consequence of the extended memory is that Big Brother does not forget anything, even when one might reasonably expect something to be forgotten. It will be possible to reconstruct events and decision chains far back in time. One can learn from experiences that would otherwise be forgotten, but one can also be held accountable for past and forgotten decisions.

An important and related question facing the mining industry, which we often encounter, is who owns and controls the collected data: the mining companies or the machine and system suppliers? In other words, who has the power and competence to further develop these systems?

4. Summary, Discussion, and Conclusions

In summary, in the new context described above, work organization will feature fewer people, as large parts of production become automated and most unit operations, such as drilling, loading, and scrapping, become autonomous. Operators coordinating production will work from comfortable control centers above ground, far from hazardous environments. They will be supported by advanced AI trained to handle disturbances. There will be direct communication between operators and the system, bypassing traditional management levels. The system learns from operators, making it crucial for management to oversee this learning process to ensure alignment with the company’s strategic goals.

Production system components will gradually be interconnected, making larger production flows autonomous. Operators will rarely need to visit the mine, as sensors and cameras will monitor most activities. If necessary, drones can be directed to inspect specific areas. The most challenging aspect to integrate into an autonomous system is repair and maintenance. Initially, there will be a shift toward standardized components that are replaced rather than repaired on site. Many disturbances can be resolved remotely, as control systems are cloud-based rather than machine-based. For advanced on-site repairs, external expertise can support repairers via AR, allowing for joint analysis and problem-solving.

It might thus seem that new technology primarily impacts mine workers, leaving managers’ work largely unchanged. However, we believe this is a misconception. Managers’ work is affected in at least two significant ways. Firstly, new administrative and management systems directly impact their roles. Simpler decisions are automated, and administrative support is replaced with computerized routines. Secondly, managers face a completely new mining context shaped by the technologies described above, collectively referred to as Mining 4.0—or perhaps Mining 5.0, aligning with the EU’s new Industry 5.0 terminology [15,16], which emphasizes the role of humans and the green transition.

For future managers, this development means working in a context with fewer workers and more machines. Much of the traditional production control will be integrated into autonomous systems, and the remaining personnel will be well educated, with high expectations for their work. Maintenance staff may take longer to reach this level. Managers will have access to sophisticated technology (e.g., VR) that provides a comprehensive overview of production and detailed information when needed. This overview extends beyond the present, allowing for forward-looking scenario simulations and backward-looking historical reconstructions through big data. The design and implementation of technology will require careful consideration. Many managers are already overwhelmed by numerous technical systems and software. Digital meetings and a constant stream of emails mean any break is spent managing correspondence. Future managers will not be as location-dependent as before. The mine follows them wherever they go, potentially leading to a situation where they can never truly leave the mine and must always be available, 24/7.

In conclusion, new technology will impact the future work of both miners and managers, though the pace of change will be slower for managers than for miners. How these change will unfold is uncertain. What should be clear is that both managers and workers will be affected and have their roles to play. Many positive outcomes are possible, but they depend on negative outcomes being handled well. The challenge remains for the mining industry to address the full spectrum of issues and possibilities that new technology represents; ultimately, it is not about technical determinism but human choices.