Data Analytics Applied to the Mining Industry

Ali Soofastaei

• Editorial: CRC Press
• Año: 2020
• ISBN:
  9780429781766
• Edición: 1
• Categoría: Computer Science
• Categoría: Databases

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book:

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• Explains how to implement advanced data analytics through case studies and examples in mining engineering

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• Provides approaches and methods to improve data-driven decision making

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• Explains a concise overview of the state of the art for Mining Executives and Managers

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• Highlights and describes critical opportunity areas for mining optimization

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• Brings experience and learning in digital transformation from adjacent sectors

1

Digital Transformation of Mining

Ali Soofastaei

Introduction

Adapting the mining industry with the technological changes is an exciting research subject [1,2]. Studied research about sociotechnical theory in an Australian mine site shows one of the first experiences to transition from hand-got mining to longwall methods. As a practical experience, this study illustrates a successful transition from a traditional mining method to an advanced process when socio-psychological and production influences grew over this technology transition [1].

The fourth industrial revolution was happening when the world is facing with the digital decade [3]. In mining, a massive amount of data is collected from many equipment and machines working in the sites that are much more than ever before [4]. These data can potentially make excellent opportunities for mining innovation to find new solutions for business problems through digital transformation (DT) in this industry [5]. The main goal of DT programs in mining has come to describe how companies become accustomed to digital modifications [6,7,8,9,10,9,10,11,12,13]. Moreover, there is not the same definition of digital mining transformation [13]. Figure 1.1 demonstrates a technology-driven process consists of three main components of DT: data, connectivity, and decision-making [9].

FIGURE 1.1
DT components.

A successful DT plan can increase the digital capabilities and develop the sociotechnical capacity in a mining company [2,14]. DT can also change all aspects of the business to improve the mining operation and maintenance [15]. However, mining companies are struggling to start the DT plans based on their technical and management challenges they are practically facing.

The pressure on mining companies to adopt themselves with digital technologies is on both sides: supply and demand. In general, the trouble starts on the side of the consumers. Some examples of this pressure are explained as follows:

Influential factors on demand:

• Consumers are more connected and are more significant decision-makers.
  The digital economy produced a cultural transformation that has set a higher level of expectation and user experiences from consumers. This change redirected the decision-making from the mining companies to the final consumers.
• Consumers are more focused on user experience than with the possession of the property itself.
  New business models developed by the digital economy lead a transformation in the consumers’ preferences mainly among the young generation, migrating the focus more and more from owning to using.
• Liquid expectations.
  The more developed a digital economy is, the more consumers extrapolate the consuming experience of a determined category of mining product to other markets, thus significantly amplifying what the market traditionally defines as “competitor.” Currently, competitors are not necessarily inside the mining industry.
• Faster adoption cycles of new ideas and technologies have made markets quickly disappear.
  The classic curve of mining innovation diffusion is facing a significant change. The process of transmission that once slowly flowed between the social systems participants nowadays quickly converges between the winner solutions.

Influential factors on supply:

• They are unbundling phenomena by the start-ups.
  The entire process of a productive chain, which was executed before for a big mining company, currently can be achieved by hundreds of small companies that perform each one of the small steps of the whole process in a more efficient way.
• Exponential cost reduction of the technological process.
  This pattern, which has been observed since the end of the fifty decades, has become economically feasible in a series of projects that previously did not leave the drawing board.
• New competitors being created every day.
  It is essential to plan a DT plan to predict the effect of market conditions on the mine value chain. The companies that do not review their operational models and especially their business models will not have space in this dynamic competitive environment. This DT plan can be reached through three strategical drivers:
  • Digital Business Transformation
    Attending the new demands of business models. The primary investment area to implement this strategical approach is a junction of the technological parks with the relevant set of new and existing data to foster the use of machine learning and artificial intelligence (AI). This approach can help to identify new trends and market demands.
  • Digital Clients Transformation
    Revision of the client experience B2C or B2B. The integration of different platforms to guarantee clients information unification, jointly with the DT of the marketing function, is the necessary condition to implement this strategical driver. The application of AI unitedly with mobile technologies and social media is essential to customizing the offerings to guarantee higher client engagement.
  • Digital Company Transformation
    Operational excellence of production process and technological park. Each productive process automation is required to implement this part of the strategy, which ranges from the operation itself to the system for decision-making. The use of IoT, robotics, and AI are some of the elements that allow automation and identification of opportunities for an improved efficiency.

To maximize the return of investment in digital is required to focus on some leveraged strategies; on the contrary, the train of DT takes the risk of stopping at proof of concepts and the first results never turning out to be sustainable.

• Agile leadership
  Strategic view and fast-paced in the decision-making process.
• Workforce focused on innovation
  Digital min...