Artificial Intelligence: Friend or Foe?
84% of global business organizations believe that AI will give them a competitive advantage. But the question companies face today is where and how to leverage Artificial Intelligence, and the big data that drives it, to capture as much value as possible. Where can companies get started and how do they ensure their adoption of new technology doesn’t get stuck in pilot purgatory and erase potential gains?
Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics (BDA) solutions will reach $260 billion in 2022 with a compound annual growth rate (CAGR) of 11.9% over the 2017-2022 forecast period. McKinsey estimates that neural networks alone will drive a 2-5% annual increase in gross revenue for AVEVA-focused industries such as Consumer Packaged Goods and Oil and Gas, resulting in over $1 Trillion per year in growth. A recent PwC report estimates that AI will contribute $15.7 trillion to the global economy by 2030. It’s clear that AI will make products and services better and has the potential to improve mankind’s way of life in almost infinite ways. But how does this relate to the adoption of artificial intelligence in modern industrial applications?
State-of-the-art artificial intelligence technologies improve industrial processes, proactively detect and solve problems, and provide guidance for risk-based decisions resulting in significant cost savings and improved competitiveness for the enterprise. Artificial Intelligence is disrupting industrial markets and forcing enterprises to reevaluate how typical work is performed including:
- Workforce training
- Process engineering
- Maintenance and repair
- Operations forecasting and scheduling
AVEVA’s industry leading cloud-based artificial intelligence technologies focus on four key areas to mitigate business and operational risk, improve workforce safety and efficiency, and forge a more reliable and secure enterprise.
With AVEVA’s Predictive Asset Analytics technology, you can spot anomalies in how your processes, equipment and assets are performing with advanced pattern recognition powered by machine learning. Early detection and warning of equipment failure, process inefficiencies, and errors within engineering, operations and maintenance improves worker safety, limits operational risk and saves $100s of Millions in averted asset failure.
AVEVA’s AI-driven simulation software combines first principles analysis and machine learning to optimize processes for improved yield and operational efficiency. Industry and asset specific algorithms are combined with advanced modeling techniques to identify process and asset anomalies with accelerated resolution times.
AVEVA’s prescriptive solutions bridge the gap between AI and the human workforce. Root cause analysis, optimised solutions, and risk-based decision support guide you to the most efficient decision for the business. Gain the insight needed to identify the right course of action with the highest probability of success in achieving your goals of improved efficiency and profitability.
By forecasting future events, schedules, and operational scenarios, AVEVA’s prognostic AI solutions allow customers to manage risk, maximize profitability and minimize cost impacts. Neural networks, deep-learning, and reinforcement learning AI technologies provide valuable insight into operations and maintenance strategy, identifying specific areas for improvement.
Putting AI into Action
To learn more about how companies are using artificial intelligence to improve their business operations view the webinars below or download the Predictive Asset Analytics infographic.
Your machines don’t have to fail. See how Covestro is reducing unscheduled downtime and maintenance costs with AVEVA's Predictive Asset Analytics. Webinar: Implementing Predictive Maintenance
Learn how Air Liquide lowers maintenance costs by preventing equipment failure. Webinar: Improve Asset Reliability by Eliminating Equipment Failures Using Predictive Asset Analytics
Jim Chappell oversees AVEVA’s overall Artificial Intelligence (AI) strategy and implementation across all business sectors. In addition, he is in charge of the Asset Performance Management (APM) suite of products and related engineering/analytics services. This encompasses Industrial Big Data (data historians), predictive/prescriptive/prognostic analytics, business intelligence, and enterprise asset management. His responsibilities include both on-premises and cloud based (SaaS) offers. Previously, Jim was a founding partner and managing officer of InStep Software, a global leader in predictive analytics and enterprise data historian software. He oversaw InStep’s operations and services for nearly 20 years and helped grow the company from startup to a global leader in its space, ultimately being acquired by Schneider Electric in 2014. His responsibilities included mission-critical system integration, enterprise architecture, cutting-edge analytics, value-added consulting services, customer support, quality assurance, and training. Early in his career, Jim was a U.S. Naval Officer where he was trained to command and control nuclear power plants, including reactor safety operations and nuclear emergencies. Jim holds a B.S. in Nuclear Engineering from Rensselaer Polytechnic Institute (RPI) in Troy, NY, a M.S. in Nuclear Engineering from the Naval Nuclear Power School in Orlando, FL, and a M.B.A. from Chaminade University in Honolulu, Hawaii. In addition, he graduated from the Civil Engineer Corps Officer's School (CECOS) in Port Hueneme, CA.