We often talk about how AI is being used in manufacturing, and it’s no mystery why. Manufacturers operate in a highly competitive environment, and there are many proven use cases for AI solutions. Given the early success and wealth of data that’s found on modern shop floors, manufacturing is an industry that’s primed for continued data-driven innovation.
Today, manufacturing is no longer just about volume and efficiency. Amidst the Industry 4.0 revolution, modern manufacturers are dealing with many more angles, complexities, and multistage processes. At the heart of it all are a few very simple questions that lack simple solutions:
- What’s the best way for us to forecast demand and allocate resources most profitably?
- How can we optimize production, keeping costs low and product quality high?
- How do we best manage production risks to ensure, smooth, consistent product delivery?
AI solutions can not only help address these these issues, but also unlock and operationalize rich insights about your production process that would have otherwise gone undiscovered.
For example, by building a model to help predict machine maintenance needs, you can often identify the most fragile components. Similarly, modeling for optimal product delivery times can surface insights about where your supply chain is most likely to break.
There’s clearly an abundance of opportunity for manufacturers to leverage AI and, fortunately, most are already embracing cyber-physical systems and using available IoT data. However, those that are slow to get started with enterprise-wide AI adoption are at a big risk of being outperformed by global competitors.
For this reason, we’re discussing a way forward amidst Industry 4.0—specifically, how AI can be adopted to optimize and innovate nearly every aspect of the manufacturing business.
Key Benefits of AI in Manufacturing
Simply put, artificial intelligence helps all kinds of manufacturers work quicker and smarter. Before getting into specific use cases, it’s worth exploring the three high-level business benefits of leveraging AI in manufacturing.
- Drive revenue: Manufacturers can use AI to drive revenue in a number of ways—everything from predicting and forecasting demand to creating intelligent, connected products that generate new and innovative business models.
- Cut costs: In addition to driving revenue, AI can also help to drastically cut costs. Manufacturers can leverage it to predict maintenance needs before they arise, optimize production and supply chain efficiency, and analyze service patterns to improve product design and minimize warranty costs.
- Avoid risks: Risk avoidance is another benefit that shouldn’t be overlooked, as the impact is significant. Manufacturers can utilize AI to detect product issues early and improve quality assurance processes to reduce liability, manage production risks, address customer service issues before they have a widespread impact, and even minimize safety hazards by predicting the likelihood of harm from certain activities.
5 Powerful Use Cases of AI in Manufacturing
Now that we have a clear understanding around the major benefits of AI for manufacturers, let’s explore some of the most impactful use cases in more depth.
1. Demand forecasting
Accurately forecasting demand is crucial for manufacturers to best plan production and, in turn, reduce inventory costs and maximize profit. Demand forecasting isn’t a new concept, but many manufacturers still utilize traditional forecasting methods (like Excel) to manage their supply chain.
These methods are semi-manual, inflexible, and worst of all, often inaccurate. However, AI brings a new level of sophistication and precision to this age-old task.
Machine learning models can aggregate historical data with new data from a variety of sources, like enterprise resource planning systems, point-of-sale systems, and social media marketing campaigns. When paired with other important variables like raw material prices, supplier issues, and changing consumer preferences, this data provides a comprehensive understanding of how popular a product is—allowing manufacturers to plan production accordingly.
And unlike traditional forecasting solutions, machine learning models are, by definition, constantly taking in new information and adapting. This allows manufacturers to quickly react to changing circumstances. The result? More dynamically accurate forecasts and, ultimately, reduced costs.
2. Product quality assurance
It’s still common today to see humans performing visual inspections to detect product flaws on the assembly line. However, human senses, attention spans, and energy levels have limitations, which can lead to costly errors. People can make more efficient use of their time elsewhere, and AI allows them to do just that.
Manufacturers can leverage AI with human-in-the-loop inspectors to improve quality assurance, and drastically reduce the likelihood of product flaws and imperfections. On top of that, this solution frees up workers’ time for more strategic tasks.
Smart cameras and other AI-enabled technology can be trained to analyze images and identify anomalies. Such image recognition programs review product features like curves, colors, and corners, then evaluate the products against a set of standards to determine if they pass or fail the inspection process.
While the process differs based on the product, image recognition programs are incredibly flexible and can be applied to almost any product with a high degree of accuracy. One McKinsey study found that image recognition programs may increase defect detection rates by up to 90% compared to human inspection.
3. Operations optimization
Operations optimization refers to methods that increase throughput and uncover hidden efficiencies in the manufacturing process. There are hundreds of different value creation opportunities baked into this one particular use case.
For instance, one of the largest inputs for any manufacturer is electricity. Though most factories operate 24-hours a day, machine learning makes it possible to schedule more energy-intensive activities for different times, ensuring they occur when power is cheapest.
Because these algorithms can process great amounts of data, they can consider other important factors alongside energy prices, like equipment maintenance, inventory, and labor costs to ensure maximum savings.
Another example is supply chain optimization, which is crucial for every manufacturer due to inventory and cash flow implications. If you increase your supply chain’s efficiency by 10%, you can produce 10% more product, while also reducing volatility in the production process.
AI can analyze a millions of variables that impact the supply chain—this includes production-specific variables like throughput quantity, production defect rates, and machine downtime, as well as global factors like fuel prices and tariff rates. Machine learning models can assess all this information in real-time, and meanwhile, a supply chain analyst with an AI-based decision-making app can adjust operations on the fly.
4. Health & safety risk avoidance
Fostering a safe work environment is crucial for a handful of reasons like protecting employees and boosting morale. This issue is especially pertinent to chemical manufacturing, which is strictly regulated through a mix of national and international protocols for operational procedures, as well as health, safety, and environmental factors.
Traditional preventive methods like annual training, posters, and protective equipment leave too much room for unsafe worker activity and hazards. But AI offers a better way forward.
Using industrial IoT data, manufacturers can monitor worker activity, machinery compliance, and causes of safety shutdowns in real-time. Employees can wear smart devices to track biometrics like body temperature, which notify supervisors when health concerns arise.
Additionally, machine learning can help to enable video surveillance systems to detect when workers are not wearing proper protective equipment or are engaged in unsafe practices. Paired with sensors on the shop floor, machine learning can also provide insight into the performance of safety systems to identify root causes of shutdowns, as well as identify leading indicators of machine issues so facility managers can address them before an accident occurs.
5. Predictive maintenance
Though there are an incredible array of AI use cases in manufacturing, the one that often dominates the conversation is predictive maintenance, and for good reason.
Ensuring maximum availability of critical manufacturing systems while simultaneously minimizing the cost of maintenance and repairs is essential. However, reactive (fixing something after it breaks) and preventative (periodic examinations) maintenance models are not flexible or cost-effective.
Using machine learning for predictive maintenance allows manufacturers to foresee when equipment failure is likely to happen so they can proactively replace parts or schedule repairs. Data from IoT sensors can continuously be fed into machine learning models that will compare this live operating data with historical data to provide extremely accurate predictions.
Paired with cloud computing, IoT also empowers you to leverage information from multiple machines, making predictions even more reliable. The overall result is maximized efficiency, minimal downtime, drastically lower maintenance costs.
As you can see, there’s limitless potential when it comes to leveraging AI in the manufacturing industry. While the technology has already had a tremendous impact, there’s still so much untapped potential that can help manufacturers optimize every facet of their business.
Figuring out how your business can benefit from AI and machine learning is overwhelming. Let us help – request a free AI assessment where RapidMiner will create and analyze a portfolio of use cases for your organization.
Get a complimentary copy of the 2020 Forrester Wave: Multimodal Predictive Analytics And Machine Learning Solutions