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The mining industry is rapidly evolving as mines embrace the fourth Industrial Revolution (4IR) with increasingly sophisticated equipment and automation.

The connected mine has become a reality, driving increased production, improving safety, optimising equipment utilisation and more.

However, alongside this technological revolution, we are also seeing both leadership and employee competence struggling to keep up.

People, leadership, and human behaviour are not being integrated with 4IR technologies, which can cause numerous challenges.

Sociometric sensors with artificial intelligence (AI) and machine learning (ML) capabilities can help to address this challenge, disrupt the traditional value chain, and constructively improve culture, behaviour and leadership within mines.

Built on technology, underpinned by people

From heavy machinery and working at scale, the mining industry has now progressed to continuous and autonomous production, with an increased emphasis on electrification, renewables, and the reduction of emissions.

Safety remains a priority, and with 4IR technologies that offer increased automation and autonomy, people can be moved away from hazardous areas and farther from danger.

However, despite technological advances, mines still rely heavily on the human component.

As a result, suboptimal human behaviour and leadership have a significant negative impact on production, equipment utilisation, safety, and the environment. People and leadership are obstacles on the road to leveraging technology and achieving increased production output, enhanced equipment utilisation, and better safety.

Mining organisations need to look at human behaviour and leadership solutions that will complement technology, improve people and leadership performance, and improve metrics that matter across the board.

Measure itto manage itSociometric sensors measure things such as eye contact, voice pitch, body orientation and proximity to others, all of which can be analysed and used to add value.

This is done by collecting data that can be used to support leadership in improving production, equipment utilisation and safety. Through machine learning, this data can be further used to predict what behaviour contributes to specific outcomes.

For example, certain sociometric data could suggest that an accident is imminent.

This could function as a prompt to shut down equipment or warn employees.

Sociometric data can also be used to objectively assess different leadership and learning styles and measure performance against key performance indicators, with data as evidence to help drive behaviour change.

This information can also be combined with other data from wearables, such as heart rate, breathing rate, pulse oxygen levels, and more, to deliver enhanced value.

Disruption drivesinnovationA positive impact on human efficiency and leadership can be enormously beneficial, but human behaviour is typically difficult to objectively measure and monitor. Sociometric sensor technology can effectively address this challenge and help mines bridge the gap between machinery advances and human performance, giving mining organisations the ability to measure and monitor metrics around social and emotional intelligence, as well as traditional metrics of leadership and engagement.

Sensor output from machines and the environment, together with sociometric sensors, can be integrated to create a complete picture of what is happening at any point in time in a mine, with live feedback and ML to radically transform every element of the connected mine.

By mapping leadership and personal interactions against real data, mining organisations will be able to understand what behaviour produces certain results and adjust behaviour and leadership styles in real-time.

By integrating disruptive technologies such as mining automation technology, renewable energy technology, and sociometric sensor technology into mine and process design in an innovative way, it is possible to achieve radical performance improvement breakthroughs.

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Sociometric data can disrupt the traditional value chain and ... - Namibian

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CHICAGO, Aug. 2, 2023 /PRNewswire/ -- IoT, AI, and 5G technological breakthroughs will have a major impact on the direction of the market for connected mining. As a result, mining operations will become more automated, operate remotely, and put an emphasis on sustainability and safety. Collaboration ecosystems, edge computing, and cybersecurity will all be crucial in advancing industry innovation and productivity.

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The global Connected Mining Market is expected worth USD 13.3 billion in 2023 and USD 22.7 billion by 2028, growing at CAGR of 11.3% during the forecast period, according to a new report by MarketsandMarkets. The need for real-time data analytics and decision-making in mining operations is fueling the adoption of connected mining technologies.

Browse in-depth TOC on "Connected Mining Market"

306 - Tables 48 - Figures264 - Pages

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Scope of the Report

Report Metrics

Details

Market size available for years

2023-2028

Base year considered

2022

Forecast period

2023-2028

Forecast units

Value (USD) Million/Billion

Segments Covered

By Component, Solutions, Services, Mining Type, deployment mode, Application, Regions

Region covered

North America, Europe, Asia Pacific, Middle East and Africa, and Latin America

Companies covered

ABB (Switzerland), IBM (US), SAP (Germany), Cisco (US), Schneider Electric (France), Komatsu (Japan), Hexagon (Sweden), Caterpillar (US), Rockwell Automation (US), Trimble (US), Siemens (Germany), Howden (Scotland), Accenture (Ireland), PTC (US), Hitachi (Japan), Eurotech Communication (Israel), Wipro (India), MST Global (US), GE Digital (US), Symboticware (Canada), Getac (Taiwan), IntelliSense.io (UK), Zyfra (Finland), Axora (UK), GroundHog (US), SmartMining SpA (Chile), and Applied Vehicle Analysis (Africa).

The Solution segment to record the higher market share during the forecast period

During the forecast period, the Solution segment is anticipated to achieve a higher market share in the Connected Mining Market. The demand for integrated and customized solutions is expected to grow significantly as the mining industry increasingly focuses on digital transformation and operational efficiency. The Solution segment is poised to dominate the market by providing mining companies with the tools and capabilities to address their specific challenges and achieve sustainable growth. With continuous technological advancements and increasing capabilities of connected mining solutions, the Solution segment is well-positioned to lead the market during the forecast period.

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By Mining type, the Surface segment is expected to hold a larger market share during the forecast period

By mining type, the Surface segment is expected to hold a larger market share during the forecast period in the Connected Mining Market. Surface mining refers to the extraction of minerals and resources from the Earth's surface, such as open-pit mining and strip mining. This type of mining is widely used for coal, iron ore, copper, and other minerals closer to the surface. Connected mining solutions offer significant benefits in surface mining operations, enabling real-time equipment monitoring, efficient fleet management, and optimized resource utilization. The ability to track and manage surface mining operations in real-time enhances safety measures, reduces operational costs, and improves overall productivity fueling market growth.

Asia Pacific to hold the larger market size during the forecast period

During the forecast period, the Asia Pacific region is expected to hold a larger market size in the Connected Mining Market. The Asia Pacific region is rich in mineral resources and has witnessed significant growth in mining activities in recent years. The increasing demand for metals, minerals, and resources from various industries, coupled with rapid urbanization and infrastructure development in the region, drives the need for efficient mining operations. As a result, mining companies in the Asia Pacific are increasingly adopting connected mining solutions to improve operational efficiency, safety, and sustainability.

Top Key Companies in Connected Mining Market:

The major players in the Connected Mining Market are ABB (Switzerland), IBM (US), SAP (Germany), Cisco (US), Schneider Electric (France), Komatsu (Japan), Hexagon (Sweden), Caterpillar (US), Rockwell Automation (US), Trimble (US), Siemens (Germany), Howden (Scotland), Accenture (Ireland), PTC (US), Hitachi (Japan), Eurotech Communication (Israel), Wipro (India), MST Global (US), GE Digital (US), Symboticware (Canada), Getac (Taiwan), IntelliSense.io (UK), Zyfra (Finland), Axora (UK), GroundHog (US), SmartMining SpA (Chile), and Applied Vehicle Analysis (Africa).

Recent Developments

June 2022 - Metso Outotec and Dynamox worked together to implement the condition monitoring platform of Dynamox in mining and aggregating operations. The solution is being provided by Metso Outotec to the installed equipment base of the business. Dynamox is a new member of the Metso Outotec partner ecosystem and aids in creating and offering customers simple-to-use digital solutions. The corporation is leveraging analytics and AI to further advance its mining operations.

November 2021 - Hexagon AB, a market pioneer in virtual reality solutions, introduced the HxGN MineEnterprise Platform in November 2021. This new product is intended to increase real-time data management and analytics for mining operations.

August 2021 - A cooperation between Caterpillar and BHP, a global mining, oil, and metals corporation, was established. In order to reduce greenhouse gas (GHG) emissions at BHP's mining locations around the world, the alliance planned to build and execute zero-emissions mining vehicles. In order to enable future mine sites and emission-free machinery, the alliance would help shape the processes, infrastructure, and technology needed.

August 2021 - To improve its position in the Connected Mining Market, Komatsu Ltd., a top manufacturer of mining equipment, said in August 2021 that it had acquired Immersive Technologies, a supplier of training solutions for the mining sector.

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Connected Mining Market Advantages:

Real-time monitoring of machinery, the environment, and employees is made possible by connected mining technology like IoT sensors and wearables. As a result, there are fewer chances of accidents occurring and possible hazards are given early warnings.

Connected Mining improves workflows, equipment use, and maintenance schedules by integrating data from diverse mining processes. As a result, operational effectiveness is improved, downtime is decreased, and productivity is raised overall.

Connected Mining provides predictive maintenance of mining equipment using data analytics and machine learning. Preventing unforeseen malfunctions, lowering repair costs, and extending equipment lifespan all result from proactive maintenance needs assessment.

With connected mining, mining operations may be remotely monitored and managed from centralised control points. This makes it possible to make decisions and changes in real-time to increase efficiency, especially in difficult-to-reach places.

Connected Mining supports resource management optimisation by utilising data-driven insights to reduce resource utilisation such as water and electricity use. This lowers operating expenses and lessens the impact of mining operations on the environment.

Connected Mining promotes more environmentally friendly mining techniques by streamlining operations and resource use. This entails cutting back on carbon emissions, water use, and the mining industry's total ecological imprint.

Real-time data analytics and reporting are offered by connected mining platforms, which enable stakeholders to learn useful information about various elements of mining operations. Better strategic planning and decision-making are made possible by this data-driven methodology.

Better communication and cooperation amongst various stakeholders, including miners, engineers, and management, are encouraged by connected mining. With better coordination as a result of this improved connectedness, operations become more effective.

Regulations pertaining to safety, environmental protection, and working conditions can be met with the help of connected mining solutions. This guarantees that mining firms operate in accordance with pertinent laws and norms.

Report Objectives

To define, describe, and forecast the Connected Mining Market based on segments based on offering, solutions, services, mining type, deployment mode, and application, with regions covered.

To forecast the size of the market segments with respect to five regions: North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America.

To provide detailed information on the major factors (drivers, opportunities, threats, and challenges) influencing the growth of the Connected Mining Market.

To analyze each submarket with respect to individual growth trends, prospects, and contributions to the global Connected Mining Market.

To analyze opportunities in the market for stakeholders by identifying high-growth segments of the global Connected Mining Market.

To profile the key market players, such as top and emerging vendors; provide a comparative analysis based on their business overviews, product offerings, and business strategies; and illustrate the market's competitive landscape.

To track and analyze competitive developments in the market, such as new product launches, product enhancements, partnerships, acquisitions, and agreements and collaborations.

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Connected Mining Market worth $22.7 billion by 2028 - Exclusive Report by MarketsandMarkets - Yahoo Finance

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The Prescriptive and Predictive Analytics research report offers in-depth market research and provides strategic solutions for businesses based on their specific needs. This report helps businesses gain clarity on current market trends and expected future developments. By leveraging global industry expertise, the report assists clients in making strategic decisions and achieving their growth objectives.

The Market Insights Reports (MIR) team focuses on key areas crucial for customer success in the market. The research employs a data triangulation method, including data mining, analysis of data factors, and primary validation, to provide accurate and reliable information to assist industries in making informed judgments and planning effective advertising and sales promotion strategies.

The Prescriptive and Predictive Analytics market is segmented based on product, customer, and distribution channels. This segmented approach enables businesses to analyze growth opportunities and make strategic decisions regarding core market applications.

The report includes various market segments such as Collection Analytics, Marketing Analytics, Supply-Chain Analytics, Behavioral Analytics, and Talent Analytics. Additionally, it covers applications in Finance & Credit, Banking & Investment, Retail, Healthcare & Pharmaceutical, Insurance, and others.

Key Benefits of the Prescriptive and Predictive Analytics Market Report:

Thorough and dynamic research methodology Complete picture of the competitive scenario in the market Information about the latest technology and product developments Analysis of the impact of industry improvements on future growth Essential historical data and analysis included in the report Easily understandable insights with visual representations

The report provides insights on market expansion, product creation and innovation, competitive analysis, market development, and market diversification. It also answers key questions about market growth, driving factors, major vendors, market scope, trends, and challenges.

In conclusion, the Prescriptive and Predictive Analytics Market report offers comprehensive market intelligence and valuable insights for businesses in various industries. Customized report information is also available to meet specific business needs. MarketInsightsReports is a trusted source for syndicated market research across multiple industry verticals.

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Prescriptive and Predictive Analytics: A Comprehensive Market ... - Fagen wasanni

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Based on data, machine learning can quickly and efficiently analyze large amounts of information to provide suggestions and help make decisions. For example, phones and computers expose us to machine learning technologies such as voice recognition, personalized shopping suggestions, targeted advertisements and email filtering.

Dr. Na Zou

Texas A&M Engineering

Machine learning impacts extensive applications across diverse sectors of the economy, including health care, public services, education and employment opportunities. However, it also brings challenges related to bias in the data it uses, potentially leading to discrimination against specific individuals or groups.

To combat this problem, Dr. Na Zou, an assistant professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, aims to develop a data-centric fairness framework. To support her research, Zou received the National Science Foundations Faculty Early Career Development Program (CAREER) Award.

She will focus on developing a framework from different aspects of common data mining practices that can eliminate or reduce bias, promote data quality and improve modeling processes for machine learning.

Machine learning models are becoming pervasive in real-world applications and have been increasingly deployed in high-stakes decision-making processes, such as loan management, job applications and criminal justice, Zou said. Fair machine learning has the potential to reduce or eliminate bias from the decision-making process, avoid making unwarranted implicit associations or amplifying societal stereotypes about people.

According to Zou, fairness in machine learning refers to the methods or algorithms used to solve the phenomenon that machine learning algorithms naturally inherit or even amplify the bias in the data.

For example, in health care, fair machine learning can help reduce health disparities and improve health outcomes, Zou said. By avoiding biased decision making, medical diagnoses, treatment plans and resource allocations can be more equitable and effective for diverse patient populations.

Additionally, users of machine learning systems can enhance their experiences across various applications by mitigating bias. For instance, fair algorithms can incorporate individual preferences in recommendation systems or personalized services without perpetuating stereotypes or excluding certain groups.

To develop unbiased machine learning technologies, Zou will investigate data-centric algorithms capable of systemically modifying datasets to improve model performance. She will also look at theories that facilitate fairness through improving data quality, while incorporating insights from previous research in implicit fairness modeling.

The challenge of developing a fairness framework lies in problems within the original data used in machine learning technologies. In some instances, the data may lack quality, leading to missing values, incorrect labels and anomalies. In addition, when the trained algorithms are deployed in real-world systems, they usually face problems of deteriorated performance due to data distribution shifts, such as a covariate or concept shift. Although the data can be incomplete, it is used to make impactful decisions throughout various fields.

For example, the trained models on images from sketches and paintings may not achieve satisfactory performance when used in natural images or photos, Zou said. Thus, the data quality and distribution shift issues make detecting and mitigating models discriminative behavior much more difficult.

If successful, Zou believes the outcome of this project will lead to advances in facilitating fairness in computing. The project will produce effective and efficient algorithms to explore fair data characteristics from different perspectives and enhance generalizability and trust in the machine learning field. This research is expected to impact the broad utilization of machine learning algorithms in essential applications, enabling non-discrimination decision-making processes and prompting a more transparent platform for future information systems.

Receiving this award will help me achieve my short-term and long-term goals, Zou said. My short-term goal is to develop fair machine learning algorithms through mitigating fairness issues from computational challenges and broadening the impact through disseminating research outcomes and a comprehensive educational toolkit. The long-term goal is to extend the efforts to all aspects of society to deploy fairness-aware information systems and enhance society-level fair decision-making through intensive collaborations with industries.

Excerpt from:

Preventing Bias In Machine Learning - Texas A&M Today - Texas A&M University Today

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