The AI revolution: Artificial intelligence is changing how manufacturers engage with data
Artificial intelligence has created a new frontier of manufacturing innovation
June 24, 2020 | By Leonardo Vieira
The world of manufacturing has long been associated with cogs and gears: the clang of metal and machines, and the sweat and steam of mechanized industry.
But that outdated stereotype no longer applies to the vast majority of production environments, where decades of incremental innovation and the accelerating adoption of new technologies in recent years have created a very different kind of manufacturing landscape.
Today, sophisticated new tools and technologies are reshaping the world of manufacturing in profound and exciting ways. They are already making manufacturing safer, more efficient and more cost-effective, and have made it possible for manufacturers to achieve new levels of quality and consistency – all while affording them more control than ever before over their parts and processes.
The AI revolution
Among the most powerful and promising of these advances is the category of new tools and technologies powered by artificial intelligence (AI).
Artificial intelligence enables systems and software to process vast amounts of data, perform herculean tasks of computational processing, to reach conclusions that would otherwise be impossible or impractical for human counterparts, and to “learn” and improve over time.
The AI revolution in manufacturing is made possible by the growing power of the Industrial Internet of Things (IoT), enhanced connectivity in factories and facilities, and the game-changing utility of big data and analytics.
With industrial and production environments now more connected, more transparent, and more flexible than at any other time in history, there is enormous potential for AI-powered solutions to continue to fuel big new advances in manufacturing.
Appreciating the impact that AI is already having on manufacturing and understanding the future potential of AI-based technologies begins with recognizing the challenges manufacturers face in implementing AI tools, and grasping the best practices associated with integrating AI into a manufacturing environment.
AI is a broad term that encompasses a fairly wide category of tools and technologies.
The AI umbrella includes “smart” tools that provide automated functionality ideal for the precision and repetitive nature of manufacturing tasks. AI not only helps perform those repetitive tasks and optimizes automated processes, it delivers newly sophisticated monitoring and maintenance capabilities.
Combined with the capacity to collect and parse vast volumes of data to identify key metrics and hidden patterns – and AI solutions can use that analytical horsepower to unlock truly predictive capabilities.
Predictive modelling allows manufacturers to benefit from early warnings for failures, end-user alerts for potential issues, and even preventative measures and “self-healing” functionality.
In other words, the best AI tools not only allow manufacturers to readily visualize variables, but to use that information to make more informed and strategic decisions.
One groundbreaking and perhaps under-appreciated facet of the AI revolution is the fact that AI can fundamentally change the way people interact with tools. AI-powered tech is often more accessible, intuitive and engaging, specifically designed to enhance the way operators and analysts engage with information.
Challenges of AI
Despite the extraordinary potential of AI technology, unlocking AI-powered tools and making the most of AI functionality isn’t as simple as flipping a switch. The following all-too-common obstacles and challenges must be overcome for any manufacturer looking to make AI solutions part of their operation:
Insufficient infrastructure. If a manufacturer’s existing technical architecture is insufficient or incompatible, AI tools cannot be implemented—or will deliver limited functionality.
Cultural reluctance. There’s no denying the fact that manufacturing has traditionally been slow to embrace high-tech change. While that mindset is (happily) shifting, enthusiasm for new tools and tech needs to be ensured, with decision-makers bolstering the digital transformation process with appropriate change management and ongoing training.
Context and customization. AI doesn’t come in a box. It is highly dependent on the specific context/environment in which it is applied to fully realize its potential and deliver maximum ROI for new tools and tech. Identifying the right tools—and the right way to use those tools—can be harder than it seems.
Connecting AI with business objectives. Finally, and perhaps most importantly, business leaders accustomed to making informed decisions may find themselves at a loss when confronted with not just a new technology, but an entirely new paradigm. Understanding what a data-driven organization means in that new context (and how/why it is important) is a critical first step that must be resolved before implementing AI technology.
Overcoming the challenges outlined above begins with understanding and adopting established best practices for AI implementation and optimization. Foremost among those are the following:
1. Prepare for success
Manufacturers need the right data collection capabilities and the right technical infrastructure to get the most out of their AI initiatives. It is essential to have the right tools and connectivity capacity in place to collect/measure essential data and convey that data to a central location.
Data gathered at individual workstations has to be funnelled through a digital architecture that connects previously distinct pieces of the larger manufacturing ecosystem.
The information that powers AI solutions, and the processing potential through which AI-powered insights are conveyed, are entirely dependent on a sufficiently connected manufacturing environment.
2. Pick the right tools
There are a lot of AI tools out there—and the quality and capability of those tools varies considerably. Make sure that your AI solution is flexible and backwards-compatible, capable of not just meeting your needs today, but evolving and growing with your operation as it changes over time.
Prioritize solutions that can integrate with most business platforms and enterprise applications, and pay close attention to any specific connected smart tools or systems that are a part of your existing processes.
Finally, make sure your AI tech is optimized by pairing it with the right analytics platforms to make data more accessible and comprehensible.
3. Pick the right partner
Today, AI is virtually synonymous with innovation—and you can’t have innovation without experimentation. Customers need to embrace innovation. AI gives you the opportunity to make big changes, but only if the right attitude is there. You have to be willing to take the plunge.
The right professional partner is critical for helping manufacturers who are ready to take that plunge make smart and informed decisions about the right solutions and applications to get the most out of their AI tools and tech.
A bright future
Despite worries about job-stealing automation, AI is proving to be anything but a replacement for workers. The best AI tech is an optimization tool, making manufacturing professionals better at what they do. In other words, it’s not a job taker, but a job enhancer.
AI not only allows manufacturers to collect more data – but to do more with that data, monitoring quality and production trends in real time and making smarter, data-driven decisions.
The result is an enormous step closer to the holy grail of manufacturers everywhere: real-time optimization of people, processes and systems—not only in production environments, but across the enterprise.
What is even more exciting is that these game-changing new AI technologies are not only extraordinarily powerful, but are also more affordable and accessible than ever before. Which means that AI isn’t just making a big impact today – but prospects for an even brighter future are anything but artificial.
Leonardo Vieira is a digital industry director with Stefanini, a global technology company specializing in digital solutions. In his role, Leonardo facilitates partnerships with clients to boost operational efficiency, improve industrial processes and implement high-end technology.
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