16 Applications of Machine Learning in Manufacturing in 2024

Artificial intelligence and machine learning (AI/ML) are beginning to permeate the manufacturing industry, promising a new level of productivity, agility and cost control. So far, ML has made the most inroads, as a subset of AI that analyzes data, recognizes patterns and suggests what might come next. And among ML applications in manufacturing, predictive maintenance has so far gained the most traction. But these are still early days and innovation continues at a rapid pace.

This article describes nine benefits and 16 applications of machine learning in manufacturing.

What Is Machine Learning in Manufacturing?

Machine learning is a subset of artificial intelligence that uses algorithms and statistical models to parse data, identify patterns, pinpoint anomalies and describe next steps, incorporating all it learns from the data to continually improve its output.

In today’s economy, ML is used for everything from setting consumer credit scores to filtering email spam. AI is a broader field enabling self-driving cars, smart assistants, advanced robotics and other uses by employing techniques, such as computer vision and natural language processing, along with ML and the neural networks that support all of these capabilities. The generative AI chatbots that became popular in the past year have ML and neural networks at their core, and their ability to contextualize ML data into conversational responses is accelerating AI/ML use overall. All that said, the terms AI and ML are often used interchangeably.

Manufacturers increasingly apply ML for predictive maintenance, quality control, supply chain optimization, production process optimization and product development. ML elevates manufacturing operations by facilitating quick decision-making based on real-time data to enhance productivity, minimize errors and reduce waste. That data may come from sources ranging from shop floor control systems to external information repositories, wireless sensors and other Internet of Things (IoT) devices or from enterprise resource planning (ERP) software suites.

For example, ML can help respond to extreme weather events, tracking external information feeds, finding alternate transportation routes and suggesting contingency production schedules to reduce the impact of supply chain disruptions. Some companies are also taking the next step of using ML to automate — not merely recommend — certain actions based on this kind of real-time information.

Key Takeaways

• Most manufacturers are still experimenting with AI/ML, but adoption levels are accelerating.
• Predictive maintenance is the top application today, leveraging AI/ML’s ability to recognize patterns, identify anomalies and recommend next steps.
• Automation represents the next level of AI/ML-empowered advances in the field of manufacturing.

Machine Learning in Manufacturing Explained

Most manufacturers are still piloting and experimenting with AI/ML to identify the technology’s potential applications, management requirements and implications for business, according to a 2023 survey by the Manufacturing Leadership Council (MLC). Over a quarter of manufacturing companies are implementing AI/ML operationally.

Integration can supercharge the benefits of ML in manufacturing, especially with the integration of operational technologies (OT), such as machine controllers, and information technologies (IT), such as ERP systems. Inputting data from a company’s sales, marketing, procurement and supply chain departments into its production department’s ML models can fuel additional insights and optimization. Notably, many of these departments are also upgrading to ML.

In an integrated scenario, production and supply chain management teams could tap into ML-powered demand planning software in the marketing department for historical sales data, market trends and social media sentiment. Then, if sales look ready to spike, ML-enabled inventory management software could check the stock levels of components and finished products, suggesting how to adjust the flow of materials so that production lines can meet demand — while avoiding costly overstock.

Benefits of Machine Learning for Manufacturing

At the current stage of AI/ML adoption, most manufacturers see potential benefits at every level, according to the MLC survey, starting with companywide improvements. Among these, superior decision-making, planning and cost control rise to the top of the list of survey responses. The poll also drilled down into specifics, which are broken out below, along with other findings on the benefits of ML for manufacturing.

• Predictive maintenance: In the MLC survey, 43% of manufacturers said they expect ML benefits in predictive maintenance to be high, while 48% expect moderate improvements.
• Increased efficiency and productivity: Thirty percent said that process improvement is a key application of insights gained from ML. They expect high or moderate benefits in operational efficiency, including uptime (88%) and the economical use of raw materials (83%).
• Quality control and assurance: Thirty percent also said that quality improvement is a key application area for AI/ML in plants and factories.
• Supply chain optimization: In a 2024 survey by the Material Handling Institute (MHI), 85% of logistics professionals predicted they’ll adopt AI/ML for supply chain management within five years, with 40% citing its potential to increase competitive advantage. And, in the MLC poll, the highest-ranked supply chain benefits include improvements in supply chain agility (89%), planning (89%) and visibility (87%).
• Customization and personalization: The cost difference between mass production and customization/personalization has been narrowing for decades, thanks to technological innovation. Already it varies widely among different product categories — computers, for example, are relatively easy to personalize but automobiles are not. AI/ML technology is only the latest contributing advancement, with ML-driven efficiencies continuing to narrow the gap.
• Safety and compliance: Three-quarters of manufacturers see ML heightening their ability to comply with safety guidelines and other regulations, per the MLC survey.
• Energy efficiency: Energy use in manufacturing plants accounts for nearly 30% of U.S. greenhouse gas emissions, according to the Environmental Protection Agency, and energy prices remain historically high. Companies are looking to AI/ML to help lower energy use and costs, including smart building maintenance that eliminates wasted heating and cooling.
• Data-driven insights and innovation: For 36% of the MLC respondents, AI/ML is already part of a larger digital transformation strategy to apply data-driven insights for continuous improvement. In a related benefit, 86% of manufacturers said they expect AI/ML’s benefits to include new business opportunities.
• Cost savings: In a separate MLC survey, over three-quarters of manufacturers said that increased access to manufacturing data has generally helped to reduce production costs. In line with this sentiment, early AI/ML adopters saw an average 14% savings on costs, according to a 2023 manufacturing industry survey by Boston Consulting Group.

16 Applications of Machine Learning in Manufacturing

New applications of AI/ML continue to emerge at a rapid clip for manufacturing activities ranging from design and planning to production and post-production processes. Experts, including MLC, expect the pace of AI/ML progress will be “fast and furious” and that in a few years it will “permeate almost every part of industrial enterprises.” The 16 applications below represent some of today’s most common uses of ML in manufacturing.

1. Predictive Maintenance

Old-school maintenance customarily involves waiting for something to break before fixing it, which can lead to downtime, lost production, safety risks and damaged customer relations — not to mention the related costs for contingencies, such as overtime and rush orders of spare parts. Reactive maintenance also increases the wear and tear on machinery, reducing its quality of output, longevity of service and overall maintenance costs.

Instead, smart factories increasingly rely on predictive maintenance — the most prevalent use of ML in manufacturing to date. Predictive maintenance uses a combination of sensors and AI/ML-enabled cloud software to identify problems that could become equipment failures. According to technology consultants at the RandGroup, ML models can be trained on data to deliver critical information, including:

• The probability that a failure will occur within a specified time frame.
• The remaining useful life of a piece of equipment.
• Whether the equipment is behaving in an unusual way.
• Which machine requires servicing most urgently.

2. Quality Control

Manual quality control of products is slow, error-prone and costly because of humans’ inherent limitations, inevitable inconsistencies and the cost of labor. Poor quality, in turn, can damage customer relationships and brand reputation and cause costly product returns, regulatory noncompliance and fines and, ultimately, reduced sales and profitability.

AI/ML-powered visual inspection systems use cameras with “machine vision” that can analyze images to detect defects on production lines in real time. For example, they can report inconsistent colors, surface defects, size variations, missing components, printing errors, incomplete processes and other problems.

3. Supply Chain Optimization

Manufacturers strive to optimize material flow through their supply chains primarily to reduce transportation, storage and handling costs — not to mention capital investment in stockpiled raw materials, components and finished products. Supply chain optimization also delivers a long list of other benefits, such as minimizing waste and energy consumption while increasing sustainability and scheduling production time more efficiently.

One way AI/ML helps streamline supply chains is route optimization, to find the quickest route for transporting shipping inputs to the factory floor and then delivering final products to customers. Another is to run simulations tracking inventory levels and predicting the outcome in terms of necessary changes to the bills of materials that go into a product or to the production process that makes it. In fact, logistics, shipping and transportation constitute the top category of AI/ML usage in the supply chain, according to the MHI survey. Other leading uses include supplier selection and due diligence, inventory management and consumer behavior tracking.

4. Manufacturing Process Optimization

AI/ML has long been transforming manufacturing operations, according to the Manufacturers Alliance. Gradually at first, and now at an accelerated pace, the technology has delivered breakthroughs in decision support, operations management, process automation and robotics for greater efficiency and production output.

An emerging example of AI/ML’s process optimization capabilities is found in its ability to automate the improvement process itself. An article in the Harvard Business Reviewdescribes the technology’s role in various steps toward process improvement, from defining, measuring and analyzing current processes, to identifying improvements and then monitoring them to ensure they achieve the desired results. In the well-established lean model of operational efficiency, for instance, AI/ML modeling can show how to tailor the configuration of any process to optimally produce each product.

5. Energy Consumption Forecasting

Manufacturers are feeling cost and compliance pressures today from volatile oil prices, stricter regulations on their use of carbon-based fuels and the uncertain price tag on renewable energy alternatives. For example, many states have set near-term deadlines by which factories must acquire a certain percentage of their electricity from renewable sources, while the U.S. Securities and Exchange Commission has ruled that publicly traded companies will have to provide climate-related disclosures in their annual reports and other statements by the end of 2025.

Smart factories employ AI/ML to monitor their historic and current energy use and to project future needs based on their company’s business strategy. They also integrate external information services that deliver continuous data streams from myriad sources, for scenario modeling and cost forecasting.

6. Energy Consumption Reduction

All the energy usage and price analysis described above can feed into an AI/ML model for planning reductions in energy consumption in areas such as machine operations, heating, cooling and lighting — and then monitor execution to ensure results.

At the same time, AI/ML’s application to predictive maintenance and operational efficiency, described in the sections above, can also produce savings in energy consumption. For instance, the technology can single out a machine for maintenance or replacement if it is not operating at optimal efficiency.

7. Product Design and Development

Manufacturing companies must continually develop new products to remain competitive. And they have to get them right. That means hitting the mark in analyzing customers’ changing tastes and needs (described in the next section). But it’s also about balancing innovations in features, functionality and materials with practical production considerations, such as complexity and cost. All of this means that the crucial product design and development process has many moving parts and can take a long time, despite ever-present time-to-market pressures.

AI/ML can speed product development. Used in rapid prototyping, for instance, the technology can answer fundamental design questions, such as how different materials might work together in a product. Or a “digital twin” can be created, using sensors embedded in a prototype, to gather operational and physical information during a simulated manufacturing process. This can align a product’s design with a business’s manufacturing capabilities and supply chain logistics before the product even leaves the drawing board (metaphorically speaking).

8. Market Trend Analysis and Product Innovation

Consumer trends change notoriously fast. Makers of consumer goods are leading-edge users of AI/ML for continual market trend analysis and rapid product innovation.

Cross-functional teams that include marketing, design and production staff use AI/ML to produce insights on emerging trends from internal and external data sources, including sales patterns, social media, customer reviews and syndicated market research reports. AL/ML may also help in various other tasks ranging from prioritizing product features to releasing a “minimum viable product” and then analyzing customer feedback about it.

9. Customized and Personalized Manufacturing

Achieving customer satisfaction today often requires going beyond mass production to what some call “mass customization.” For example, online shoppers can increasingly opt for different fabrics for shirts, lenses for eyeglasses, memory sizes in laptops, engravings on mugs and more. While product makers can charge more for these customizations, they also face greater complexity and costs during production.

AI/ML can assist in taming that complexity and cost. By analyzing customer data and market trends, the technology can facilitate the stocking of the fabrics currently in fashion, for example, or production in the latest colors. A smart factory equipped with sensors and machine-to-machine communications can fulfill customized and personalized orders with greater agility, using AI/ML to analyze the orders and then identify the required components, configurations and production steps.

10. Worker Safety and Ergonomics

Production and material transportation accounted for some of the highest levels of injury and illness reported by any type of private industry in 2022, according to the U.S. Bureau of Labor Statistics. Production injuries included overexertion, falls and exposure to harmful substances. Musculoskeletal disorders from repetitive motions and heat illnesses also continue to be major problems, according to the AFL-CIO.

AI/ML-powered safety systems can predict when and where a safety violation could happen, to remedy the situation before it causes harm, according to the BDO consulting group. Among other applications, sensors and machine vision cameras combined with AI/ML can trigger alerts if they identify or anticipate safety concerns, such as excessive fumes or lack of personal protective equipment. Ergonomic applications include AI/ML-equipped wearables to monitor worker posture and movements for real-time feedback on potential harm.

11. Robotics and Automation

Robotic arms, automated storage and retrieval systems and autonomous guided vehicles are among the robots increasingly used to automate assembly lines and warehouses. They pick and pack goods, paint cars, conduct surveillance and perform other functions. While robotics and automation can improve efficiency, increase accuracy and reduce labor costs, robots have been known to be inflexible — performing repetitive tasks without the ability to adapt to changes or respond to unexpected situations.

AI/ML is helping to reduce these limitations as it advances the field of robotics to the next level of automation to handle product complexity and the growing demand for customization. Enhanced with IoT sensors and AI/ML, robots are becoming more capable of sensing what they are working on and reacting to that information, making them more mobile, collaborative with human workers and able to rapidly switch from one product version to another.

12. Anomaly Detection

Pattern recognition and anomaly detection are core capabilities of ML. On the factory floor and in warehouses, this talent comes in handy for predictive maintenance, quality control, surveillance and, more generally, identifying inefficiencies in the production and handling of goods.

Combined with IoT sensors, for example, AI/ML can predict potential breakdowns from the vibrations or temperature of a machine. In combination with robotics, another example of anomaly detection is using drones to scan for stockouts among products stacked up to the high ceiling of a warehouse, which improves worker safety.

13. Warehouse Management

Warehouse management systems help manufacturers organize, manage and monitor the locations and processes involved in moving raw materials, components and products into and out of storage. AI/ML can increase the accuracy and efficiency of this already complex and challenging function.

For instance, AI/ML-enhanced warehouse management can improve productivity in these vast facilities. From shipment to shipment, using AI/ML together with wireless identification tags and other sensors can find the quickest possible routes required to pick products off of their shelves. The range of other applications include environmental control, demand-based restocking and optimized use of costly warehouse space.

14. Supply Chain Management

Generative AI represents yet another ML-based technique that is enhancing supply chain management, in addition to the optimization benefits of AI/ML described earlier in this article.

Manufacturing personnel can use generative AI to help with data entry and tracking of operational tasks and routing. Working with supply chain management software suites, generative AI can create procurement orders and request letters directly on software dashboards, developing personalized vendor engagement communications and customer updates on delivery schedules.

15. Demand Forecasting

Manufacturers can no longer rely only on historical sales data to plan and execute production and supply chain operations. Consumer demand volatility is particularly high lately, which can create costly problems for manufacturing companies — whether it’s missed sales of the “latest model” of consumer electronics or costly overstocks in off-trend fashions.

Today, demand forecasting requires manufacturing companies to bring together cross-functional teams to share internal and external data sources, including social media reviews, consumer surveys, real-time point-of-sale feeds and subscriptions to specialized market research — in addition to sales figures and direct customer feedback. AI/ML helps to crunch all of these numbers and predict demand. In turn, better forecasting enables dynamic tuning of operational parameters, such as stock levels, replenishment frequencies, production schedules and delivery cycles, based on sales velocity, demand signals and market conditions.

16. Automation

AI/ML tools are used to recommend the best course of action based on their reading of patterns and anomalies in the data they’re fed. At the next level, they can also automate recommended steps without human intervention. In the MLC survey, 40% of respondents said they expect plants and factories to be run mostly autonomously by 2030.

For example, barcode scanners, weight sensors and radio frequency identification tags in warehouses can automatically trigger replenishment when supplies diminish to a preset level. An automaker’s robots can detect and adjust for surface flaws when painting cars. At the other end of the supply chain, “machine customers” will autonomously obtain and pay for goods. Examples include IoT devices placing orders independently of human intervention and intelligent replenishment algorithms, according to Gartner, which has identified machine customers as one of its top supply chain trends in the near-term.

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The field of manufacturing is witnessing a transformation due to AI/ML’s data analysis skills, predictive insights and capacity to learn continuously on the job. At this early stage of the technology’s application, AI/ML is already making a clear contribution in areas like predictive maintenance. Accurate business data — and lots of it — is vital to the success of AI/ML in any manufacturing endeavor.

Machine Learning in Manufacturing FAQs

How is machine learning used in manufacturing?

Manufacturers are embracing machine learning (ML) to improve everything from maintenance and quality control in production processes to resilience in supply chain operations. The technology combines real-time data from enterprise resource planning (ERP) systems, manufacturing management software suites, Internet of Things (IoT) sensors in factories and external market intelligence services, among other sources, to empower faster decisions. Benefits include higher productivity, fewer errors and less waste across the entire manufacturing operation.

How is deep learning used in manufacturing?

As a form of machine learning, deep learning goes beyond ML’s basic pattern recognition and prediction capabilities by drawing on neural networks with many more layers of interconnected nodes. This enables deep learning systems to better mimic the human brain and learn more intricate or nuanced patterns from even larger amounts of data. In manufacturing, some defect detection systems rely on industrial cameras equipped with deep learning capabilities, particularly in the making of complex products and detection of subtle defects.

How can machine learning play an important role in AI-assisted manufacturing strategies?

Machine learning (ML) is rapidly transforming manufacturing by analyzing real-time data from shop floor sensors, internal enterprise resource planning (ERP) systems, external market intelligence and other sources to optimize production and supply chain operations by informing such processes as predictive maintenance and quality control. This data-driven approach makes for faster and better decision-making — in turn, boosting productivity, minimizing errors and reducing waste. Furthermore, some companies are pushing the boundaries by using ML not just to recommend actions based on its powers of pattern recognition, but also to automate the steps it recommends.

How is machine learning used in the supply chain?

Machine learning, with its strengths in delivering data analysis, predictive insights and recommendations, can optimize material flow through supply chain stages, including transportation and storage, to reduce costs, such as stranded capital investment in stockpiled raw materials, components and finished products. One way is route optimization, finding the quickest route for sourcing inputs, moving them to the factory floor and then shipping final products to customers. Another way is to run simulations tracking inventory levels to predict their potential impact on the production process. Other top uses include supplier selection and due diligence, inventory management, sustainability initiatives and consumer behavior tracking.