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Leveraging Big Data, Artificial Intelligence, and Machine Learning in the Coatings Industry - American Coatings Association

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Digitalization is occurring across all manufacturing industries, and the coatings sector is no exception. The quantity of data that can be leveraged to improve all business activities--from new product development to production to customer service--is increasing dramatically. The challenge is to determine where and how to apply technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) and how to make the data on hand relevant to the problem or question of interest. These questions and others were considered by members of the coatings value chain and their insights are presented below. What types of Big Data can be leveraged by the coatings industry to facilitate research, development, and innovation in general? Sapper, Cal Poly: We need to be asking three questions when it comes to data needs in our industry. What data do we have? What data do we need? And what questions are we trying to answer? A lot of valuable data already exists, but it is tied up in reports, published literature, or subject matter expertise. The data is there, but not collected in a way that allows helpful artificial intelligence and machine learning projects to be performed. Understanding what type of data is needed for a particular project is the first step in identifying where that data might already exist.


Winning with AI is a state of mind

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Executives have seen that the move from running artificial intelligence (AI) experiments and proofs of concept to capturing lasting value at scale requires an investment in strong foundations. These include aligning AI with core areas of the business; embracing important cultural and organizational shifts; and investing in new kinds of technology, training, and processes for building AI. More and more organizations are adopting these basic practices, and those that do tend to report the highest bottom-line impact from AI. But successful organizations don't just behave differently; our experience in thousands of client engagements around analytics and AI over the past five years shows that they also think differently about AI. At these companies, AI is etched in the collective mindset ("We are AI enabled"), rather than simply applied opportunistically ("Here's a use case where AI can add value").


Fueling utility innovation through analytics

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Utilities around the world are making big investments in advanced analytics. Getting the full value, however, requires rethinking their strategy, culture, and organization. Advanced analytics can deliver enormous value for utilities and drive organizations to new frontiers of efficiency-- but only with the right approach. There's little to be gained from just bolting on a software solution. The real value comes from embedding data analytics as a core capability in the organization and using it to detect pain points, design solutions, and enable decision making.


Is your data ready for AI? Part 1

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Artificial intelligence is a major driver of value for the enterprise. According to a recent AI study from IBM, 82 percent of organizations are now at least considering AI adoption, and the number of companies that are beyond the AI implementation stage is 33 percent higher than it was in 2016. What's more, by pairing AI with other exponential technologies such as automation, blockchain and the Internet of Things (IoT), companies are redefining their business architectures. The IBM "Cognitive Enterprise" report highlights how these technologies represent the next inflection point for the enterprise comparable in scale and scope to the introduction of the Internet and mobile technology. The cognitive enterprise is a framework for companies to define and pursue a bold vision to realize new sources of value and restructure their industries, missions, and business models.


How airlines should manage IT failures and security breaches to improve operational stability

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More than four billion airline passengers will book flights this year, and they expect to reach their destination on time, safely, and securely. Airlines rely ever more on digital technology to manage everything from booking and in-flight entertainment to aircraft maintenance. Technology outages and cyberincidents around the world have demonstrated that even some of the largest airlines need to upgrade their IT and operational technology systems, including their technology architecture and underlying infrastructure, to reduce risk and build resiliency into their heavily digitized operating model. In 2019, for example, the United Kingdom imposed a $230 million fine on a European airline for a 2018 breach caused by security vulnerabilities in its website. Multiple airlines have had problems with their ticketing systems and an important aviation-infrastructure system, causing delayed flights and passenger check-ins.