Industries
HOUSTON, TX
U.S.A. M.F.G.

Digitally
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Optimize
All Operations.

Across the world. In factory assembly lines. At oil fields. Deep in mines. Amongst countless rows of corn. In warehouses and shopping malls in suburban towns. There are humans performing rote tasks. Their brain power, creativity and leadership untapped. Their physical energy sapped, mimicking the motions of machines. And, in some cases, their lives put in dangerous situations.

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We are here to finally fix this. We are Teknoir. And we are prepared to flash-forward everything into full industrial automation enhanced with artificial intelligence.

VOL 3 — INDUSTRIES SECTION

COPYRIGHT ©2024 Teknoir™
Thought Research initiative

The Opportunity

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Consultants have spent the past decade peddling the tech trends of the moment as some revolutionary force that will magically transform entire industries. CxOs are told to act now, or their organizations will become irrelevant. This has led companies to waste billions on cloud, IoT, Industry 4.0, and digital transformation on the promise that ROI will eventually come. It hasn’t. Now, they are being told to ride the newest hype cycle, AI.
1/8
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$1.5T
$1.5T
Unplanned downtime now costs Fortune Global 5000 companies 11% of their yearly turnover, which is almost $1.5 trillion.
This is up over 73% in two years.
According to Siemens’ report (published in 2023), the cost of downtime has significantly increased over the past two years (2021-22), with unplanned downtime now costing Fortune Global 500 companies 11% of their yearly turnover, almost $1.5 trillion, up from $864 billion two years ago (2019-20).
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$8.5B
$8.5B
Global onshore wind spent
57% of O&M costs ($8.5 Billion) on unplanned repairs and correctives caused by component failures in 2015.
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3000M
million metric
3000M
million metric
The EPA estimated 3000 million metric tons of C02 equivalent emissions (almost half of the total U.S. greenhouse emissions) from industry and electric power generation in 2021.
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$112B
$112B
in 2022, U.S. Retail stores lost $112 billion in revenue due to theft. Retail theft is on the rise and forecasted to double by 2025. Internal employee theft accounted for 28.5%.
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$167B
$167B
$167 billion total economic cost of work-related fatalities and injuries in 2021. This is equivalent to 103 million days of work lost. In 2002, there 2.8 million recordable injuries and over 5,000 fatalities.
Private industry employers reported 2.3 million nonfatal workplace injuries in 2022. This increase was driven by the rise in injuries, up 4.5 percent to 2.3 million cases.
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$38M
$38M
38 million tons of plastic waste (95%) was not recycled in the U.S. in 2021. Seven million tons of aluminum are still not recyled each year. in 2019, seven million tons of e-waste discarded.
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Filter
Industries
of Interest.

To learn more about how Teknoir impacts a wide range of industries, select from the topic(s) below.
SEC — 3

Filter
Industries
of Interest.

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1/8
Agriculture
Operational AI for agriculture is not only redefining traditional farming practices but also addressing crucial challenges such as food security, resource management, and environmental sustainability.
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Industry

Agriculture

Our Operational AI for agriculture are not only redefining traditional farming practices but also addressing crucial challenges such as food security, resource management, and environmental sustainability.

The use cases of AI in agriculture are both diverse and profound, offering solutions that are as beneficial to smallholder farmers as they are too large-scale agribusinesses. As we look towards a future where sustainability and efficiency are paramount, the role of AI in agriculture is not just promising; it is essential in arriving at a more resilient and bountiful future.

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Use Cases

1/8

Precision
Agriculture

AI-driven precision agriculture harnesses data analytics to optimize field-level management. By analyzing data from satellite imagery, drones, and sensors, AI enables farmers to make informed decisions about planting, irrigation, and harvesting, tailoring their approaches to micro variations in soil and environmental conditions.

2/8

Crop Health
Monitoring

Utilizing advanced image recognition and remote sensing technologies, AI systems can detect and diagnose plant diseases and pest infestations early. This capability facilitates timely interventions, reducing crop losses and enhancing yield quality.

3/8

Predictive Analytics
for Yield Prediction

AI algorithms are adept at predicting crop yields by analyzing historical data alongside real-time field data. These predictions aid farmers in planning and optimizing their resources, leading to more efficient farming practices.

4/8

Predictive Equipment Maintenance

The ability to analyze multimodal time-series and vision data from equipment to predict maintenance events, failures, and other anomalies before they occur in order to increase the reliability and performance of farm equipment machinery.

5/8

Automated
Machinery

Self-driving tractors, drones for crop spraying, and automated harvesters powered by AI are revolutionizing field operations. These technologies not only reduce labor costs but also increase precision in tasks such as planting and harvesting, minimizing waste and improving safety.

6/8

Supply Chain
Optimization

AI plays a pivotal role in streamlining agricultural supply chains. From predictive demand analysis to real-time tracking of goods, AI enhances transparency and efficiency in the journey from farm to fork.

7/8

Genetic Crop Improvement

AI-driven genomic analysis is accelerating the development of crop varieties that are more resilient to pests, diseases, and extreme weather conditions. This is a game-changer for ensuring food security in the face of changing global climates.

8/8

Climate & Environmental Monitoring

The ability to analyze multimodal time-series and vision data from equipment to predict maintenance events, failures, and other anomalies before they occur in order to increase the reliability and performance of farm equipment machinery.
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Metrics

01

Between 20% to 40% of global crop production is lost to pests annually. Each year, plant diseases cost the global economy around $220 billion, and invasive insects around $70 billion, according to the Food and Agriculture Organization of the United Nations.

02

Climate change may affect the production of maize (corn) and wheat as early as 2030 under a high greenhouse gas emissions scenario, according to a new NASA study published in the journal, Nature Food. Maize crop yields are projected to decline 24%, while wheat could potentially see growth of about 17%.

03

For this report, U.S. PIRG Education Fund and National Farmers Union surveyed 53 farmers across 14 states. 
The study found that 53% of the farmers had lost crops because of downtime after a tractor or combine breakdown. Roughly one in three farmers surveyed fear losing their family farm to such a breakdown.

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2/8
Retail
Transformation in the retail industry leading to enhanced efficiency, personalization, customer experience, and loss prevention is now possible with Operational AI.
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Industry

Retail

Operational AI for Retail – Occupancy Analytics

 


In an era where digital transformation is not just a trend but a business imperative, the retail industry stands at the forefront of a complete reinvention of how retailers engage with customers, optimize operations, and stay competitive in a rapidly changing market.

Defect Detection & Counting for Retail DTC Warehouse Operations

 


From personalized shopping experiences to streamlined supply chain management, AI is enhancing efficiencies and opening new avenues for growth and customer satisfaction.

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Use Cases

1/8

Personalized Customer Experiences

AI enables retailers to offer highly personalized shopping experiences through advanced data analytics and machine learning. By analyzing customer data, retailers can provide tailored recommendations, personalized marketing, and customized shopping experiences, enhancing customer satisfaction and loyalty.

2/8

Inventory Mgmt and Demand Forecasting

AI-driven predictive analytics are revolutionizing inventory management. Retailers can accurately forecast demand, optimize stock levels, and reduce waste, ensuring that the right products are available at the right time, thereby maximizing sales and minimizing costs.

3/8

Supply Chain Optimization

AI facilitates smarter supply chain management by providing real-time insights and predictive analytics for demand planning, vendor management, and logistics optimization. This leads to improved efficiency, reduced costs, and a more agile response to market changes.

4/8

In-Store Experience and Operations

Retailers are enhancing the in-store experience through AI-powered tools like smart shelves, interactive kiosks, and automated checkout systems. These innovations improve customer engagement, streamline operations, and reduce labor costs.

5/8

Virtual Assistants and Chatbots

AI-powered chatbots and virtual assistants are transforming customer service in retail. These tools provide instant, round-the-clock assistance to customers, handling inquiries, offering product recommendations, and assisting with purchases, thus elevating the customer service experience.

6/8

Fraud Detection and Prevention

Leveraging AI for security and fraud detection allows retailers to identify and prevent fraudulent transactions and activities, safeguarding both the business and its customers.

7/8

Loss Prevention

AI is pivotal in combating retail shrinkage, a challenge that encompasses theft, fraud, and inventory mismanagement. Through advanced surveillance systems integrated with AI, retailers can detect suspicious activities and potential thefts in real time. AI algorithms can analyze video footage to identify patterns that may indicate shoplifting or internal theft, enabling quick intervention. Incorporating AI into shrink and theft prevention strategies offers retailers a proactive approach to safeguard their assets.

8/8

Market Trend Analysis

AI algorithms can analyze vast amounts of market data, helping retailers identify trends, understand consumer behavior, and make informed strategic decisions.
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Metrics

01

Shrink Accounted for Over $112 Billion in Industry Losses in 2022, According to NRF Report.

02

10 AI Customer Experience Statistics You Should Know About

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3/8
Waste & Recycling
Operational AI addresses key challenges in waste management, including improving recycling rates, reducing contamination, optimizing operations, and moving towards more circular economy models.
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Industry

Waste & Recycling

The Waste & Recycling industry, a crucial component in the pursuit of a sustainable future, is undergoing a significant transformation. In this era where efficiency, environmental responsibility, and innovation intersect, AI emerges as a pivotal tool in revolutionizing waste management and recycling processes.

As our technologies continue to evolve, they hold the promise of significantly mitigating the environmental impact of waste and propelling the industry towards a greener, more circular economy.

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Use Cases

1/8

Waste Sorting and Management

AI-powered robots and systems are increasingly being employed for sorting waste more accurately and rapidly than traditional methods. These systems use machine learning algorithms and computer vision to differentiate between various types of waste materials, ensuring proper segregation and enhancing recycling rates.
Sustainability

2/8

Predictive Analytics for Collection Routes

AI algorithms analyze vast amounts of data to optimize waste collection routes and schedules. This not only improves operational efficiency but also reduces the carbon footprint of collection vehicles, contributing to environmentally friendly practices.

3/8

Material Recovery Facilities Optimization

AI helps in the automation of MRFs, leading to more efficient separation of recyclables, reduced contamination, and lower human labor requirements. Improved sorting accuracy ensures a higher quality of recyclable materials, which is crucial for the recycling industry.

4/8

Waste Volume and Composition Analysis

Through AI, waste management companies can predict the generation of waste in terms of volume and composition. This foresight aids in better planning and management of resources required for waste handling.

5/8

Enhanced Customer Engagement

AI-driven platforms enable better customer engagement by providing insights into recycling practices, waste reduction tips, and personalized information regarding waste disposal and collection schedules.

6/8

Smart Bins and IoT Integration

AI integrated with the Internet of Things (IoT) leads to the development of smart bins that can monitor waste levels, sort recyclables, and even compact waste. These bins can communicate data to waste management systems for efficient collection and processing.

7/8

Illegal Dumping Detection

AI-powered surveillance and monitoring systems can identify and track instances of illegal dumping, enabling quicker response and aiding in enforcing regulations.

8/8

Lifecycle Analysis and Reporting

AI tools facilitate comprehensive lifecycle analysis of products and materials, aiding businesses and policymakers in making informed decisions about materials usage and waste management strategies.
SEC — 2

Metrics

01

Plastic consumption has quadrupled in the past 30 years, and is expected to triple in the next 30. Meanwhile, global plastic recycling rates have failed to reach two digits. Less than 10% of all the plastic ever produced has been recycled.

02

On average, Americans throw away about 1,200 pounds of organic garbage each year. We only recycle about 67.2 million tons of a possible 267.8 million tons – less than a quarter of total MSW. The EPA estimates that around 75% of all waste is recyclable. Each American produces about 4.51 pounds of trash in a single day.

03

1.5 million tons of trash is illegally dumped each year in the US (source: EPA) This equates to approximately 4,109 tons of trash per day 100 million tons of trash is illegally dumped per year worldwide Illegal dumping statistics do not include cigarette butts and plastic bags Illegal dumping costs $600 per ton on average to clean up Large cities spend millions on cleanup each year.

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4/8
Manufacturing
Operational AI is advancing the manufacturing industry through applications that enhance efficiency, quality, safety, and productivity. These systems are used to anticipate equipment failures and reduce downtime, detect defects more accurately than humans, optimize energy usage, and enhances workplace safety.
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Industry

Manufacturing

In the ever-evolving landscape of the manufacturing industry, the integration of Artificial Intelligence (AI) stands as a pivotal turning point, heralding an era of unprecedented efficiency, innovation, and competitiveness. AI, with its vast array of capabilities, is not just an add-on but a fundamental paradigm shift, reshaping how manufacturing operates from the drawing board to the final product – providing increased productivity, quality, sustainability, and customization.

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Use Cases

1/7

Predictive Maintenance

AI-driven predictive maintenance systems analyze data from machinery sensors to forecast potential failures before they occur. This proactive approach minimizes downtime, extends equipment life, and optimizes maintenance schedules, significantly reducing costs and enhancing operational reliability.

2/7

Quality Control / Defect Detection

Advanced image recognition and machine learning algorithms are revolutionizing quality control processes. These systems can identify defects or deviations from the norm with higher accuracy and speed than human inspectors, ensuring consistent product quality and reducing waste.

3/7

Supply Chain Optimization

AI algorithms excel in managing complex supply chains. They predict demand, optimize inventory levels, and suggest the most efficient routes and methods for logistics, adapting in real time to changing conditions and minimizing disruptions.

4/7

Smart Manufacturing

The integration of AI in manufacturing processes (often referred to as Industry 4.0) allows for smarter, more flexible production lines. AI systems can adjust operations dynamically in response to new information or requirements, paving the way for customized production and faster turnaround times.

5/7

Energy Management

AI enables more efficient use of resources, particularly energy. By analyzing usage patterns and operational data, AI can optimize energy consumption, reducing costs and the environmental footprint of manufacturing activities.

6/7

Worker Safety

AI-enhanced monitoring systems ensure worker safety by detecting hazardous situations or ergonomic issues in real time. They also contribute to designing safer, more efficient workplace layouts.

7/7

Process Optimization

Machine learning models continually analyze production processes, identifying inefficiencies and suggesting improvements. This continuous improvement cycle leads to enhanced productivity and product quality.
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Metrics

01

The global artificial intelligence (AI) in manufacturing market size was reached at USD 3.8 billion in 2022 and it is expected to hit around USD 68.36 billion by 2032, growing at a CAGR of 33.5% over the forecast period 2023 to 2032.

02

In the private manufacturing industry during 2020, there were 373,300 total recordable cases (TRC) of nonfatal injuries and illnesses. Of those, 135,900 had days away from work (DAFW), 108,800 had days of job transfer or restriction (DJTR), and 128,700 had other recordable cases (ORC). The total recordable case incidence rate per 100 full-time equivalent (FTE) workers was 3.1.

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5/8
Mining
Operational AI for mining enhances efficiency, safety, and productivity across various stages of operations. In operations, this technology is deployed for predictive maintenance, reducing equipment downtime by forecasting failures. Overall, AI is enabling mining companies to make better, faster decisions, ultimately leading to cost savings and increased productivity.
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Industry

Mining

The mining industry, historically characterized by its intensive labor and high operational costs, stands on the cusp of a technological revolution with the integration of Artificial Intelligence (AI). The advent of AI in mining heralds a new era of efficiency, safety, and sustainability, transforming conventional practices into innovative, data-driven processes

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Use Cases

1/7

Predictive Maintenance

AI algorithms excel in predicting equipment failure, enabling proactive maintenance. By analyzing data from sensors and machinery, these systems can forecast breakdowns before they occur, minimizing downtime and extending equipment lifespan.

2/7

Resource Optimization

AI-driven analytics facilitate more precise mineral exploration and resource estimation. Utilizing data from geological surveys, AI models can more accurately identify potential mineral deposits and assess the feasibility of mining operations, thus reducing exploratory costs and improving yield.

3/7

Automated Operations

Autonomous vehicles and drilling systems, guided by AI, are increasingly prevalent in mining. These innovations not only enhance operational efficiency but also significantly reduce the risks associated with human-operated machinery in hazardous environments.

4/7

Environmental Monitoring and Compliance

AI assists in real time monitoring of environmental parameters, ensuring compliance with regulatory standards. By analyzing data from various sensors, AI can predict environmental impacts, aiding in sustainable mining practices.

5/7

Safety and Risk Management

The application of AI in monitoring and analyzing data from mining sites substantially elevates safety standards. AI systems can predict and mitigate risks, such as equipment failure or structural collapses, ensuring the safety of the workforce. AI worker safety applications ensure personal protective equipment (PPE) adherence, hazardous area entry detection, worker down, and dozens of other HSE use cases for saving lives, preventing injuries, and protecting the environment.

6/7

Supply Chain and Logistics Optimization

AI enhances the efficiency of the supply chain in mining by predicting demand, optimizing routes for transportation, and managing inventory more effectively, thus reducing operational costs and improving delivery times.

7/7

Data-Driven Decision-Making

By harnessing vast amounts of data, AI empowers decision-makers with deeper insights and predictive analytics, facilitating more informed and strategic decisions in mining operations.
SEC — 2

Metrics

01

The number of fatal injuries in the mining, quarrying, and oil and gas extraction industry rose from 78 in 2020 to 95 in 2021, a 21.8-percent increase. Fatal injuries in the industry were above 100 in the three years prior to 2020: 112 fatalities in 2017, 130 fatalities in 2018, and 127 fatalities in 2019.

02

Mining, metals, and other heavy-industrial companies lose 23 hours/month, equating to 1.2 million hours a year across the sector. At $187,500/hour, this adds up to $225 billion annually.

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6/8
Building & Property Management
Building and property managers are implementing Operational AI to improve efficiency, sustainability, security, and tenant satisfaction.
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Industry

Building & Property Management

By integrating our Operative AI technologies, the building management industry is not only enhancing operational efficiency but also setting new benchmarks in sustainability, safety, and occupant comfort.

More than a trend, the integration of AI in building management is a significant shift towards smarter, more efficient, and sustainable building operations for owners, managers, and occupants.

SEC — 1

Use Cases

1/6

Energy Optimization

AI algorithms are crucial in analyzing energy consumption patterns, enabling predictive maintenance, and automating HVAC systems for optimal energy usage. This not only reduces energy bills but also contributes to a building’s sustainability goals.

2/6

Predictive Maintenance

AI-powered systems can predict equipment failures and maintenance needs, reducing downtime and extending the lifespan of building infrastructure. By analyzing data from sensors and historical maintenance records, these systems can forecast when a piece of equipment is likely to fail or need servicing.

3/6

Enhanced Security

AI enhances building security through advanced surveillance systems that can recognize faces, detect anomalies, and alert authorities in real time. It goes beyond traditional security measures by providing a proactive approach to threat detection and management.

4/6

Intelligent Building Automation

AI is pivotal in creating smart buildings where lighting, heating, cooling, and other systems are optimized for efficiency and occupant comfort. These systems learn from occupant behavior and adjust environments accordingly.

5/6

Occupant Experience and Space Optimization

AI-driven analytics help in understanding how spaces are utilized, leading to more efficient space management. This not only maximizes the utility of available space but also enhances the occupant experience through personalized environmental settings.

6/6

Disaster Response and Management

AI systems play a vital role in disaster preparedness and response. By analyzing data from various sources, including weather systems and structural sensors, AI can predict potential issues and automate responses to mitigate damage.
SEC — 2

Metrics

01

METRICS TO INCLUDE: The Importance of Energy Optimization Buildings account for a whopping 40% of global energy consumption. The energy used in buildings contributes to nearly 33% of global greenhouse gas emissions. Just a 10% reduction in energy consumption could result in over $40 billion annual savings in the United States alone.

02

Predictive maintenance is highly cost effective, saving roughly 8% to 12% over preventive maintenance, and up to 40% over reactive maintenance (according to the U.S. Department of Energy).

03

American news reports often cite claims data from the National Insurance Crime Bureau, which recorded 64,000 catalytic converter thefts in 2022.

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7/8
Renewable Energy
Operational AI is being used in renewable energy to optimize the generation, distribution, and consumption of power by improving supply and demand forecasting, enhancing grid management, and enabling predictive maintenance of renewable energy infrastructure.
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Industry

Renewable Energy

In the renewable energy sector, AI stands as a beacon of innovation, driving efficiency and sustainability and revolutionizing how we harness, manage, and distribute renewable resources.

The intersection of AI and renewable energy heralds a future of cleaner, more efficient, and more sustainable energy systems. This isn’t just a predictable technological advancement, but a crucial step towards a greener, more sustainable future for all.

SEC — 1

Use Cases

1/4

AI-Driven Forecasting and Efficiency Optimization

One of the most pivotal roles of AI in renewable energy is in the domain of predictive analytics. Machine learning algorithms, a subset of AI, are adept at forecasting energy production from sources like solar and wind. These predictions are crucial for grid management, allowing for better integration of renewable sources and reducing dependency on fossil fuels. Moreover, AI enhances energy efficiency by optimizing operations in real-time, reducing wastage, and maximizing output.

2/4

Smart Grid Management and Energy Storage

AI is integral in the development and management of smart grids. These intelligent networks leverage AI to balance supply and demand, incorporate renewable energy sources more effectively, and improve the reliability and efficiency of the power supply. Additionally, AI plays a crucial role in advancing energy storage technologies. By predicting storage needs and managing charge/discharge cycles, AI aids in smoothing out the intermittency issues commonly associated with renewable energy sources.

3/4

Predictive Maintenance and Operational Efficiency

The maintenance of renewable energy infrastructure, such as wind turbines and solar panels, is another area where AI makes a significant impact. Predictive maintenance powered by AI algorithms can anticipate equipment failures before they occur, reducing downtime and extending the lifespan of renewable energy assets. This proactive approach ensures continuous energy generation and reduces maintenance costs.

4/4

Enhancing Renewable Energy Adoption and Access

AI also facilitates the wider adoption of renewable energy through various applications. From optimizing the design and layout of solar farms and wind turbines to evaluating the best locations for new installations based on historical and geographical data, AI assists in maximizing the potential of renewable energy projects. Furthermore, AI-enabled platforms can democratize access to renewable energy, allowing for more efficient peer-to-peer energy trading and empowering consumers to make informed decisions about their energy usage.
SEC — 2

Metrics

01

Solar has fared particularly badly, with a 95% increase in average downtime days, much more than the renewable average due to supply chain issues.

02

About 25% of wind turbine faults caused 95% of all downtime. Wind turbine reliability has improved in recent years to 98% availability. But wind turbines still fail at least once annually, on average. Larger turbines fail more often than smaller ones. The average downtime is 1.6 hours for U.S. wind turbines.

03

More than 36% of wind turbines fail less than three times; however, most wind turbines suffered five to nine failures during observation. The above conclusions may support wind farm maintenance scheduling and supplier management.

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8/8
Oil & Gas
Oil & Gas producers and service providers are benefiting from Operational AI to drive operational efficiency, optimize exploration and production, predict equipment maintenance needs, and improve safety by analyzing vast datasets and automating complex processes.
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Industry

Oil & Gas

AI has the ability to transform the Oil & Gas industry by introducing unprecedented efficiencies, enhancing exploration, optimizing production, and mitigating safety risks.

Over the past few decades, the industry has invested significantly in instrumentation, industrial automation, and SCADA for process control and safety. While these systems excel at what they were designed for, they lack the predictive capabilities and other intelligent capabilities of AI like object and anomaly detection.

SEC — 1

Use Cases

1/7

Exploration and Reservoir Characterization

AI algorithms analyze geological data to predict the location of oil and gas reserves with higher accuracy. Machine learning models interpret seismic and subsurface data, reducing the risk and cost associated with exploration. Enhanced reservoir characterization enables more precise modeling of oil fields, facilitating optimal resource extraction.

2/7

Drilling and Completion Optimization

AI-powered tools assist in designing well placement and drilling paths, optimizing drilling operations, and reducing non-productive time. They predict equipment failure enabling proactive maintenance, and automate real-time decision-making during drilling to improve safety and efficiency.

3/7

Production Optimization

AI systems analyze production data to maximize oil recovery, using predictive models to manage field production and automate control systems. They enable smart field management through the use of digital twins, creating virtual models of oil fields for simulation and analysis.

4/7

Equipment Maintenance and Reliability

Predictive maintenance powered by AI enhances equipment reliability and longevity. By monitoring machinery conditions and predicting failures before they occur, AI helps avoid costly downtime and improves overall equipment effectiveness.

5/7

Supply Chain and Logistics Optimization

AI optimizes the Oil & Gas supply chain, from forecasting demand to automating inventory management. By analyzing market trends and logistics data, AI ensures a more responsive and cost-effective supply chain.

6/7

Health, Safety, and Environmental Protection

AI contributes to safer work environments by monitoring operational parameters and predicting hazardous situations. This goes far beyond detecting personal protective equipment (PPE) adherence, where the AI can differentiate the type of PPE required depending on a defined area’s hazards. It also detects specific hazardous worker behaviors using human pose estimation to accomplish everything from unsafe lift practices to worker down. It aids in environmental monitoring, detecting spills, and ensuring compliance with environmental regulations.

7/7

Energy Efficiency and Emissions Reduction

AI systems optimize energy use and reduce greenhouse gas emissions by monitoring and controlling energy consumption across operations. This is crucial for the industry's move towards more sustainable practices.
SEC — 2

Metrics

01

The Global AI in Oil and Gas Market size is expected to grow from USD 2.67 billion in 2023 to USD 4.63 billion by 2028, at a CAGR of 11.68% during the forecast period.

02

In 2021, the rate of job-related injuries and illnesses for the oil and gas industry in the United States was 1.6 per 100 workers. This figure has been consistently decreasing during the period in consideration, aside from the most recent figure. Technology has played a major role in this reduction, and AI has the potential to decrease it even further.

03

High production or machine downtime is a growing scourge in the global oil and gas industry. An Aberdeen research found that the cost of unplanned downtimes worked out to around $260,000 every hour in 2016. When focusing on the causes of this downtime, a recent study conducted by Vanson Bourne for Service Max found that human error was the leading cause at 23%, while 70% of the surveyed companies had a complete lack of knowledge about their equipment condition.

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