Because a simulation takes ten hours to run, only a handful of the resulting trillions of potential designs can be explored in a week. Companies that rely on experienced engineers to narrow down the most promising designs to test in a series of designed experiments risk leaving
performance on the table. As products have evolved, pushing the boundaries of performance has become increasingly challenging. Industrial companies that can rapidly innovate and bring higher-performing products to market faster are much more likely to gain market
share and win in their market segments. Despite this opportunity, many executives remain unsure where to apply AI solutions to capture real bottom-line impact. The result has been slow rates of adoption, with many companies taking a wait-and-see approach rather than diving in.
- US Steel is building applications using Google Cloud’s generative artificial intelligence technology to drive efficiencies and improve employee experiences in the largest iron ore mine in North America.
- Just like a person might look closely at a car to find any problems, AI looks at the cars with cameras and sensors.
- Electronics manufacturer Philips also operates a factory in the Netherlands that makes electric razors, where a total of nine human members of staff are required on site at any time.
- This group serves as a clear home for the new talent required and is responsible for defining common standards and building a central repository for best practices and knowledge.
- Consider the example of a factory maintenance worker who is intimately familiar with the mechanics of the shop floor but isn’t particularly digitally savvy.
- It helps manufacturers optimize operations by interpreting telemetry from equipment and machines to reduce unplanned downtime, gain operating efficiencies, and maximize utilization.
Freight itself is an industry still reliant on the old, far less reliable world of written ledgers and paper receipts – leading to a wave of startups like Flexport promising to fix these problems. In line with their defined targets, companies should identify specific business domains and value levers that will be their focus. They can then select relevant use cases that allow them to apply these levers. Manufacturing Innovation, the blog of the Manufacturing Extension Partnership (MEP), is a resource for manufacturers, industry experts and the public on key U.S. manufacturing topics. There are articles for those looking to dive into new strategies emerging in manufacturing as well as useful information on tools and opportunities for manufacturers. It’s painful and expensive to migrate once you have all your data in a single cloud provider.
AI Use Cases & Applications: In-Depth Guide for 2023
These use cases help to demonstrate the concrete applications of these solutions as well
as their tangible value. By experimenting with AI applications now, industrial companies can be well positioned to generate a tremendous amount of value in the years ahead. In 2018, we explored the $1 trillion opportunity for artificial intelligence (AI) in industrials.1Michael Chui, Nicolaus Henke, and Mehdi Miremadi, “Most of AI’s business uses will be in two areas,” McKinsey, March 7, 2019. As companies are recovering from the pandemic, research shows that talent, resilience, tech enablement across all areas, and organic growth are their top priorities.2What matters most? Five priorities for CEOs in the next normal, McKinsey, September 2021. Even if players limit the amount of information analyzed, their AI/ML initiatives will still require extensive time and resources, such as sufficient numbers of data engineers on AI/ML teams.
We often recommend choosing one small process to experiment with as a pilot—and scale from there. If your sales team is engaged in cold calling, imagine a use case where you leverage AI to craft those conversations instead. 2023 will likely go down in history as the “breakthrough” year for Artificial Intelligence. To say that the use of AI tools has increased exponentially over the past six months is an understatement. Much like the introduction of the internet in the late 1990s, AI has the potential to disrupt life as we know it. Classify the severity of accidents at industrial sites and identify their root causes.
AI in Manufacturing
Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity. Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.
To understand why, my team and I performed research on over 150 scenarios for applying AI to the industrial manufacturing sector, and here are three of the snags we found. Currently, industrial manufacturing is responsible for nearly 24% of global carbon emissions, and the manufacturing sector as a whole is rife with expensive inefficiencies that make work more difficult for laborers. Still, AI innovations are generally accelerating, creating numerous use cases for generative AI in various industries, including the following five. AI isn’t just giving factories a boost; it’s giving them a whole new look. We’re about to enter a future where things are more remarkable, faster, and can change in the blink of an eye.
Scaling AI in the sector that enables it: Lessons for semiconductor-device makers
Many original equipment manufacturers are pushing requirements down their supply chain and the smaller manufacturers are in a bind. You have this pressure but don’t have the resources to implement the technologies. Between the MEP Centers in every state and Puerto Rico and our 1,400 trusted advisors, the MEP National Network offers assistance within a two-hour drive of every U.S. manufacturer.
Notably, these companies have made significant investments in AI/ML talent, as well as the data infrastructure, technology, and other enablers, and have already fully scaled up their initial use cases. The other respondents—about 70 percent—are still in the pilot phase with AI/ML and their progress has stalled. Recommendation engines, for example, are used by e-commerce, social media and news organizations to suggest content based on a customer’s past behavior. Machine learning algorithms and machine vision are a critical component of self-driving cars, helping them navigate the roads safely. In healthcare, machine learning is used to diagnose and suggest treatment plans.
Why is AI important in the manufacturing industry?
Network experts can help de-risk your company’s adoption of AI and other advanced technologies via hands-on technical assistance, as well as connecting you with grants, awards and other funding sources. MEP Center staff can facilitate introductions to trusted subject matter experts. For areas like AI, where not all MEP Centers have the expertise on staff, they can locate and vet potential third-party service providers. Center staff help make sure the third-party experts brought to you have a track record of implementing successful, impactful solutions and that they are comfortable working with smaller firms.
It has almost become shorthand for any application of cutting-edge technology, obscuring its true definition and purpose. Therefore, it’s helpful to clearly define AI and its uses for industrial companies. Rather than endlessly contemplate possible applications, executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve specific business problems and create tangible value. As a first step, industrial leaders could gain a better understanding of AI technology and how it can be used to solve specific business problems.
What are the advantages and disadvantages of machine learning?
While a significant portion of this value will inevitably be passed on to customers, the competitive advantage of capturing it, particularly for early movers, will be impossible to ignore. People often use the terms AI and machine learning interchangeably, but they’re two very different things. Machine learning puts data from different sources together and helps you understand how the data is acting, why, and which data correlates with other data. It helps you solve a particular problem by taking historic evidence in the data to tell you the probabilities between various choices and which choice clearly worked better in the past. It tells you the relevance of all this, the probabilities of certain outcomes and the future likelihood of these outcomes.
The face of the industry is changing, following the global trends of digitalization and sustainability. Industrial manufacturers have been reluctant to make the shift, but since change is inevitable, it’s better to embrace AI now rather than get left behind. Transformer architectures learn context and, thus, meaning, by tracking relationships in sequential data.
Supply Chains Are Still in Shock from COVID-19
Setting clear business targets will also help companies measure the benefits of each use case over time. AI/ML use cases can help semiconductor companies optimize their portfolios and improve efficiency during the research and chip-design phase. By eliminating defects and out-of-tolerance process steps, companies can avoid time-consuming iterations, accelerate yield ramp-up, and decrease the costs required to maintain yield. They may also automate the time-consuming processes related to physical-layout design and the verification process. Manufacturers can use automated visual inspection tools to search for defects on production lines. Visual inspection equipment — such as machine vision cameras — is able to detect faults in real time, often more quickly and accurately than the human eye.
Boost Your Manufacturing Business with Matellio’s AI Solutions
With its unique ability to process and understand vast amounts of data, gen AI can be used across a wide array of applications — not just to improve productivity or efficiency. Here are five use cases that put gen AI to what is AI in manufacturing work in transforming the manufacturing industry. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.
When defining steps in process recipes, semiconductor companies typically specify one constant time frame for each one. But the time frame required for some individual wafers may show statistical or systematic fluctuations, so a process could keep running after it has produced the desired outcome (for instance, a particular etch depth). Industry-wide, manufacturing will accrue the most value from AI/ML (Exhibit 4). This is not a surprise, given the capital expenditures, operating expenditures, and material costs involved in semiconductor fabrication.