The heavy industry is facing challenges in implementing AI. Find out what they are, what other companies are doing, and how you can overcome them today.
Predicting a fault in your fleet's air pressure system (APS).
Identifying a defective refrigerator compressor before it brings down your system.
These are the problems manufacturers deal with daily. And without the right systems in place, they end up working under a break-fix model.
Not a sustainable option.
With the adoption of AI and machine learning, the heavy industry is positioning itself to predict expensive (and sometimes dangerous) before they occur. But this alone isn't enough to stave off issues right around the corner.
It takes years to gather enough data to properly train AI and ML solutions, which is why we recommend companies leverage their strongest asset—their domain expertise.
But before we dive into that, let's review AI's use in various heavy industries.
Artificial intelligence empowers heavy industry companies to make sense of large datasets and gather insights to innovate and drive business forward.
One report shows that by 2026, AI in global manufacturing will grow to $16.7 billion.
Here's how it’s implemented across various sectors today.
Automobiles. Electronics. Fast-moving consumer goods. Manufacturing plants must build products en mass while still passing inspections and safety regulations. It's a challenging task, but with the right equipment, it's possible.
Many of today's manufacturers use robotics to automate and speed up productivity and improve quality assurance.
"In our plant, every plastic injection machine has a robotic arm, multiple sensors, and programmable controllers. The primary job of the robots is to collect the non-product plastic stick at the mold opening phase so the plastic waste is automatically collected. Some work with a sucker to further absorb freshly made petri dishes & lids, then transport them into packing bags directly. This way, you won't see any fingerprints. Cleanness is strongly preferred by our lab scientist users, so their experiments get higher precision and confidence." — Zhe Song, marketing director of a China manufacturer in the biotechnology industry
According to Deloitte, machine learning improves product quality by up to 35%.
In oil refineries, reducing petroleum coke production is vital. It holds no value, so they use data science during the refining process. This requires ML algorithms and virtual sensors to monitor petroleum coke levels.
Machine learning is also present in semiconductor manufacturing facilities where yield losses can reach as high as 30%. With ML algorithms, companies in the gas industry can improve equipment efficiency and decrease yield loss. It's also common for refineries to use predictive maintenance to prevent machinery breakdowns. This helps minimize annual maintenance and inspection costs.
Here's another industry that relies on optimal uptime for its customers. Power outages are detrimental to both the utility company and its users. Maintaining equipment is a top priority, so it's common to see machine learning algorithms used to predict faults in power plants before they occur.
Highways. Bridges. Skyscrapers. Steel is a critical material used for infrastructures across the world. If it's not developed correctly, it can lead to catastrophic events. And therefore, you'll find modern metal manufacturers using machine learning to test the fatigue strength of steel.
This is an expensive but necessary process to ensure the integrity of steel. With ML, manufacturers can predict fatigue strength without manual measurement involved.
Using innovative technologies to advance the medical field is nothing new. You'll find top pharmaceutical companies using AI and machine learning to develop better drugs.
Machine learning and simulation technologies can detect drug side effects, which means fewer failed medications. In fact, leading pharmaceutical companies like Merck and Pfizer collaborate with or acquire AI technologies.
Today, you'll find more Cloud Pharmaceuticals emerging. As recently as 2020, GSK and Vir Biotechnology partnered for drug discovery for COVID-19 using AI and CRISPR.
The military is no stranger to AI and ML technologies. You'll find it used in drones, computer systems, and robotics. Automation, risk assessment, and other defense applications incorporate artificial intelligence.
For example, drones aren't just used for surveillance—they're also used for search and rescue missions. Drones have even been used to deliver medicine.
Machine learning is an excellent technology for predictive maintenance. Unfortunately, some heavy industry companies struggle to adopt it efficiently. Let's review some of the common hurdles faced today.
"Big data" problems—sometimes, it's a good one to have. However, many heavy industry facilities lack the right data type needed to properly train machine learning models. For instance, gas companies may only have access to two years of data. That's not nearly enough time to build a model capable of predicting future failures.
Then if the data is present, it may not be easy to work with. For instance, data might be stored in different formats, making it difficult to extract relevant variables. Or it may not contain the information needed to create reliable models.
Heavy industries adopting AI often suffer from high implementation costs. This makes it hard for them to justify adopting machine learning solutions. The reason? It takes time to implement and maintain them.
Plus, implementing a solution requires additional resources. For example, engineers taking time from their day job to learn the software. And the money spent on hardware upgrades.
There's also the issue with finding the right AI solution. Most don't come preloaded with the features necessary for your specific problem. So you'll need to purchase multiple systems or program it from scratch.
This eats away at productivity and slows the process of ML modeling.
Perfect predictions don't always mean better decisions. For example, industrial companies with ML platforms that predict faults in areas that are low risk. Rather than detecting higher risk issues, they waste money replacing small parts with little impact.
It's critical to prioritize risk assessments and fault predictions so your engineers can make the most effective repairs.
Heavy industries require large amounts of data to deploy machine learning solutions. This is because most of the training data come from past events. If you don't have enough historical data, you won't be able to predict future failures accurately.
And once you collect the data, you still need to analyze it. This means you need to hire people who know how to code. You also need to pay for storage and compute power. These things add up quickly.
Not to mention, the time it takes to implement. It can take several years to deploy an AI solution.
Fortunately, these four problems can be overcome using an innovative approach: Knowledge-first AI.
Heavy industry companies face a common problem when implementing machine learning and predictive modeling: the solution revolves around anomaly detection.
Finding something wrong in a system or machinery is only one-third of the problem. You also need to find the root cause. And then determine whether the detection was accurate. This requires human knowledge from industry experts.
"Anomaly detection is useful, but it doesn't complete the job. Anomaly Detection only says, Hey, I've now learned the patterns of the last 10 years. Now I see something different. That's it, the rest is your problem. But to build a full solution, you must sit down with the expert. And it turns out the procedure for the expert isn't that complicated. The diagnosis expressed to the company isn't available to us in rows of data. It's available to us as knowledge." — Christopher Nguyen, CEO of Aitomatic
Here's a look at several examples showing the importance of the knowledge-first approach.
Airlines lose millions of dollars each day planes are grounded. But finding mechanical issues early isn't just about money—it's critical to prevent lethal in-air malfunctions.
“When Google gets something wrong, maybe you click on the wrong ad. When we get things wrong, people get hurt.There's a lot of industry and legislative requirements regarding auditability, reliability, actually inability and so on. Any solution we build must address these things.” — Christopher Nguyen, CEO of Aitomatic
For Boeing, integrating machine learning predictive models was an easy decision. And although the company gathered terabytes of data each day, it wasn't enough to train machine learning to pinpoint faults. It can detect an anomaly in machinery, but identifying the sensor and anomaly before a breakdown was problematic.
The missing component was human experts with 30 years' worth of domain knowledge. They were paramount to going in, looking at the anomalies, and tying them to specific sensors and components. With this input, ML became advanced within a short period.
Fishermen today rely on technology to simplify daily tasks. For instance, scouring the ocean for key locations to place fixed nets to capture generous amounts of fish. It's helpful, but without prediction capabilities, fishermen wasted hours daily driving to the boat to find smaller than expected volumes.
They needed real-time detection to see when the net is near capacity (the longer fish are in the net, the higher the odds of them getting out). This would enable the fishermen to leave at the right time to gather optimal yields. The company in question adopted an AI solution to achieve this.
But there was another problem: there was no way to accurately detect fish species. This was critical to quickly identify high-value fish and capture them before escaping the net. Unfortunately, AI comes with limitations and can't determine exact matches 100% of the time.
At least, not without human intervention. This required someone with expert domain knowledge to teach the system species classification.
A downed refrigeration system in a grocery store leads to hundreds of thousands of dollars in lost product and even more in revenue. It's a problem no retailer wants to have and many avoid it using machine learning.
The only problem is that it uses anomaly detection, which requires analyzing past data to identify issues before they emerge. In most cases, there aren't enough failures that occurred over the past year to train ML models efficiently.
Then when new data points come in, it can't adequately determine if it's normal or abnormal. So it leads to false positives and false negatives. In other words, wasted time and resources on inspecting potential issues that weren't business-relevant.
The solution: using human expert fuzzy logic models. One popular Japanese retailer did this across thousands of its stores. Experts came in, codified the rules, and deployed it within six months (vs. the three years it typically takes). The outcome: meaningful gains in market share.
There's an overwhelming amount of evidence proving the value knowledge-first AI brings to the ML table. Without it, you're left with anomaly detection that takes years to learn patterns meaningful enough to impact your bottom line.
Each industry has domain experts on standby (or even retired) that can come in and assist with model training. Using this resource is invaluable to launching credible AI solutions in months instead of years.
Not leveraging human knowledge reduces the reliability of AI and ML in critical industries like manufacturing, pharmaceuticals, supply chain, and utilities can lead to disastrous scenarios.
Predicting a fault in your fleet's air pressure system (APS).
Identifying a defective refrigerator compressor before it brings down your system.
These are the problems manufacturers deal with daily. And without the right systems in place, they end up working under a break-fix model.
Not a sustainable option.
With the adoption of AI and machine learning, the heavy industry is positioning itself to predict expensive (and sometimes dangerous) before they occur. But this alone isn't enough to stave off issues right around the corner.
It takes years to gather enough data to properly train AI and ML solutions, which is why we recommend companies leverage their strongest asset—their domain expertise.
But before we dive into that, let's review AI's use in various heavy industries.
Artificial intelligence empowers heavy industry companies to make sense of large datasets and gather insights to innovate and drive business forward.
One report shows that by 2026, AI in global manufacturing will grow to $16.7 billion.
Here's how it’s implemented across various sectors today.
Automobiles. Electronics. Fast-moving consumer goods. Manufacturing plants must build products en mass while still passing inspections and safety regulations. It's a challenging task, but with the right equipment, it's possible.
Many of today's manufacturers use robotics to automate and speed up productivity and improve quality assurance.
"In our plant, every plastic injection machine has a robotic arm, multiple sensors, and programmable controllers. The primary job of the robots is to collect the non-product plastic stick at the mold opening phase so the plastic waste is automatically collected. Some work with a sucker to further absorb freshly made petri dishes & lids, then transport them into packing bags directly. This way, you won't see any fingerprints. Cleanness is strongly preferred by our lab scientist users, so their experiments get higher precision and confidence." — Zhe Song, marketing director of a China manufacturer in the biotechnology industry
According to Deloitte, machine learning improves product quality by up to 35%.
In oil refineries, reducing petroleum coke production is vital. It holds no value, so they use data science during the refining process. This requires ML algorithms and virtual sensors to monitor petroleum coke levels.
Machine learning is also present in semiconductor manufacturing facilities where yield losses can reach as high as 30%. With ML algorithms, companies in the gas industry can improve equipment efficiency and decrease yield loss. It's also common for refineries to use predictive maintenance to prevent machinery breakdowns. This helps minimize annual maintenance and inspection costs.
Here's another industry that relies on optimal uptime for its customers. Power outages are detrimental to both the utility company and its users. Maintaining equipment is a top priority, so it's common to see machine learning algorithms used to predict faults in power plants before they occur.
Highways. Bridges. Skyscrapers. Steel is a critical material used for infrastructures across the world. If it's not developed correctly, it can lead to catastrophic events. And therefore, you'll find modern metal manufacturers using machine learning to test the fatigue strength of steel.
This is an expensive but necessary process to ensure the integrity of steel. With ML, manufacturers can predict fatigue strength without manual measurement involved.
Using innovative technologies to advance the medical field is nothing new. You'll find top pharmaceutical companies using AI and machine learning to develop better drugs.
Machine learning and simulation technologies can detect drug side effects, which means fewer failed medications. In fact, leading pharmaceutical companies like Merck and Pfizer collaborate with or acquire AI technologies.
Today, you'll find more Cloud Pharmaceuticals emerging. As recently as 2020, GSK and Vir Biotechnology partnered for drug discovery for COVID-19 using AI and CRISPR.
The military is no stranger to AI and ML technologies. You'll find it used in drones, computer systems, and robotics. Automation, risk assessment, and other defense applications incorporate artificial intelligence.
For example, drones aren't just used for surveillance—they're also used for search and rescue missions. Drones have even been used to deliver medicine.
Machine learning is an excellent technology for predictive maintenance. Unfortunately, some heavy industry companies struggle to adopt it efficiently. Let's review some of the common hurdles faced today.
"Big data" problems—sometimes, it's a good one to have. However, many heavy industry facilities lack the right data type needed to properly train machine learning models. For instance, gas companies may only have access to two years of data. That's not nearly enough time to build a model capable of predicting future failures.
Then if the data is present, it may not be easy to work with. For instance, data might be stored in different formats, making it difficult to extract relevant variables. Or it may not contain the information needed to create reliable models.
Heavy industries adopting AI often suffer from high implementation costs. This makes it hard for them to justify adopting machine learning solutions. The reason? It takes time to implement and maintain them.
Plus, implementing a solution requires additional resources. For example, engineers taking time from their day job to learn the software. And the money spent on hardware upgrades.
There's also the issue with finding the right AI solution. Most don't come preloaded with the features necessary for your specific problem. So you'll need to purchase multiple systems or program it from scratch.
This eats away at productivity and slows the process of ML modeling.
Perfect predictions don't always mean better decisions. For example, industrial companies with ML platforms that predict faults in areas that are low risk. Rather than detecting higher risk issues, they waste money replacing small parts with little impact.
It's critical to prioritize risk assessments and fault predictions so your engineers can make the most effective repairs.
Heavy industries require large amounts of data to deploy machine learning solutions. This is because most of the training data come from past events. If you don't have enough historical data, you won't be able to predict future failures accurately.
And once you collect the data, you still need to analyze it. This means you need to hire people who know how to code. You also need to pay for storage and compute power. These things add up quickly.
Not to mention, the time it takes to implement. It can take several years to deploy an AI solution.
Fortunately, these four problems can be overcome using an innovative approach: Knowledge-first AI.
Heavy industry companies face a common problem when implementing machine learning and predictive modeling: the solution revolves around anomaly detection.
Finding something wrong in a system or machinery is only one-third of the problem. You also need to find the root cause. And then determine whether the detection was accurate. This requires human knowledge from industry experts.
"Anomaly detection is useful, but it doesn't complete the job. Anomaly Detection only says, Hey, I've now learned the patterns of the last 10 years. Now I see something different. That's it, the rest is your problem. But to build a full solution, you must sit down with the expert. And it turns out the procedure for the expert isn't that complicated. The diagnosis expressed to the company isn't available to us in rows of data. It's available to us as knowledge." — Christopher Nguyen, CEO of Aitomatic
Here's a look at several examples showing the importance of the knowledge-first approach.
Airlines lose millions of dollars each day planes are grounded. But finding mechanical issues early isn't just about money—it's critical to prevent lethal in-air malfunctions.
“When Google gets something wrong, maybe you click on the wrong ad. When we get things wrong, people get hurt.There's a lot of industry and legislative requirements regarding auditability, reliability, actually inability and so on. Any solution we build must address these things.” — Christopher Nguyen, CEO of Aitomatic
For Boeing, integrating machine learning predictive models was an easy decision. And although the company gathered terabytes of data each day, it wasn't enough to train machine learning to pinpoint faults. It can detect an anomaly in machinery, but identifying the sensor and anomaly before a breakdown was problematic.
The missing component was human experts with 30 years' worth of domain knowledge. They were paramount to going in, looking at the anomalies, and tying them to specific sensors and components. With this input, ML became advanced within a short period.
Fishermen today rely on technology to simplify daily tasks. For instance, scouring the ocean for key locations to place fixed nets to capture generous amounts of fish. It's helpful, but without prediction capabilities, fishermen wasted hours daily driving to the boat to find smaller than expected volumes.
They needed real-time detection to see when the net is near capacity (the longer fish are in the net, the higher the odds of them getting out). This would enable the fishermen to leave at the right time to gather optimal yields. The company in question adopted an AI solution to achieve this.
But there was another problem: there was no way to accurately detect fish species. This was critical to quickly identify high-value fish and capture them before escaping the net. Unfortunately, AI comes with limitations and can't determine exact matches 100% of the time.
At least, not without human intervention. This required someone with expert domain knowledge to teach the system species classification.
A downed refrigeration system in a grocery store leads to hundreds of thousands of dollars in lost product and even more in revenue. It's a problem no retailer wants to have and many avoid it using machine learning.
The only problem is that it uses anomaly detection, which requires analyzing past data to identify issues before they emerge. In most cases, there aren't enough failures that occurred over the past year to train ML models efficiently.
Then when new data points come in, it can't adequately determine if it's normal or abnormal. So it leads to false positives and false negatives. In other words, wasted time and resources on inspecting potential issues that weren't business-relevant.
The solution: using human expert fuzzy logic models. One popular Japanese retailer did this across thousands of its stores. Experts came in, codified the rules, and deployed it within six months (vs. the three years it typically takes). The outcome: meaningful gains in market share.
There's an overwhelming amount of evidence proving the value knowledge-first AI brings to the ML table. Without it, you're left with anomaly detection that takes years to learn patterns meaningful enough to impact your bottom line.
Each industry has domain experts on standby (or even retired) that can come in and assist with model training. Using this resource is invaluable to launching credible AI solutions in months instead of years.
Not leveraging human knowledge reduces the reliability of AI and ML in critical industries like manufacturing, pharmaceuticals, supply chain, and utilities can lead to disastrous scenarios.
