Machine learning (ML) is a subfield of artificial intelligence (AI) that studies and understands patterns, relations, and themes in data to support learning, processing, and decision-making without human input. In simplified terms, machine learning is the process of making computers and machines “intelligent” enough to develop their understanding and enable them to make decisions and predictions like any human would.

This process of making a machine capable of human-like learning is achieved by submitting massive quantities of data to the machine learning algorithms, which then analyze and make data-driven and data-centric suggestions based only on the supplied data. Due to several causes, the use of machine learning techniques has proliferated during the last two decades as well as the improved accessibility and capability of existing machine learning technologies. Today, machine learning is widely used in several aspects of production, such as management, optimization, monitoring, control, and diagnostics.

According to a survey, 92% of the top players of the business industry are already investing in machine learning, automation, and artificial intelligence. Process productivity and efficiency have improved by 55% in the past few years by implementing machine learning. 

Global machine learning in manufacturing is expected to grow to $16 billion by 2025 from $1 billion in 2018, according to a Global Market Insights analysis. These statistics are a clear indication of how important machine learning in manufacturing can be to increase profits, cut costs, improve product quality, reduce production time, and enhance process efficiency. 

How does machine learning help in the manufacturing industry?

Given the challenges of a rapidly evolving, unpredictable industrial setting, ML, as part of AI, can learn and adapt to changes, so that the owners, managers, designers, and engineers do not have to predict and give answers for all potential circumstances that might arise throughout the entire production process.  As a result, given the hardships of most first-principle modelling techniques to deal with adaptability, ML provides a compelling case for its application in manufacturing. A significant characteristic of machine learning is its ability to effectively train from and adjust to different environments.

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Only after data is examined and transformed into usable information does it become helpful. This is especially true in manufacturing, given the difficulty of acquiring real-time data during a live production program while balancing technological, economical, and knowledge constraints. Using machine learning in manufacturing can lead to the extraction of patterns from current data sets, which can be used to build estimations about the system's future behavior. This additional data (knowledge) might help process owners make better decisions or can be used routinely to enhance the service.

In various industries, ML techniques have been successfully used in performance improvement, tracking and management applications in manufacturing, and predictive maintenance. Machine Learning models offer good potential for improved quality control optimization in manufacturing systems, particularly in complex manufacturing environments where identifying the trouble spots is tricky. In some NP-hard manufacturing challenges, Machine Learning developers can help mitigate cycle time and wastage, as well as increase resource usage. In addition, machine learning provides strong tools for continual quality improvement in big and complicated manufacturing processes.

Where is machine learning used in industry?

Manufacturing is one of the most significant businesses in the world's economy as it generates a worldwide output of a whopping $13.9 trillion. One of the most important production objectives is to manufacture more high-quality items at the lowest possible cost. However, for firms that lack the necessary resources and skills to create and manufacture high-quality items, producing products may be an extremely costly and demanding procedure. However, the face of the manufacturing industry has altered considerably during the last few years due to the use of machine learning in manufacturing. The below-mentioned list highlights the most prevalent areas where machine learning in manufacturing is being materialized.  

When compared to conventional organizations, manufacturers which use machine learning technologies are better positioned to move quicker. With the potential of connection, sophisticated analytics, automation, and more, everything from manufacturing efficiency to product personalization can be altered. Manufacturers may enhance speed to market, improve service efficacy, and build a new business model for increased productivity by combining these technologies.

1. Intelligent data processing through data analytics

Data has become a very precious asset, and collecting and processing it is now more affordable than ever. 

With the help of Artificial Intelligence specifically Machine Learning — manufacturers are harnessing that data to drastically boost their profit margins. Machine learning may help manufacturers exploit their key production data in a variety of ways. This data can be any sort of data. It may be consumption data, shipping data, historical data, operational data, or inventory data. When it comes to managing inventory, machine learning and contract management software are a great match. Machine learning algorithms excel in deriving useful insights from structured data, while contract management will allow you to scale operations in an efficient way. With the use of data analytics in manufacturing, one can create a calm environment at the workplace since it takes care of everything by evaluating every component of the production process, providing data, and modifying each stage of the process.

2. Pattern Detection and predictions.

Manufacturers may use important data to build a product with higher consumer value and reduce the risks associated with introducing a new product to the market. When planning, strategizing and modelling the product, actionable insights are considered, which aids in the judgment call.

ML can also be used to forecast energy consumption,  consumer demand, and maintenance needs. Machine learning models offer a degree of intricacy to consumer forecasting. These models will include historical and current data from a variety of sources, like  ERP systems, point-of-sale systems, and social media marketing campaigns. This information may be paired with other relevant variables such as raw material pricing, supplier conflicts, and weather interruptions to determine a product's market relevancy. Machine learning solutions, unlike traditional forecasting systems, will be continually updated based on fresh data.

These algorithms can arrange the best time to undertake energy-intensive operations for optimum cost reductions by taking into account energy pricing, employee salaries, equipment maintenance, and storage minimization.

3. Fault and Error Detection

According to Forbes, using machine learning to automate quality testing may raise detection rates by up to 90%. For product inspection and fault detection, machine learning is being used. Manufacturers may gain a competitive edge by decreasing equipment downtimes by diagnosing production equipment process issues and potential threats quickly and accurately. Machine learning algorithms are increasingly being used in the manufacturing industry to enable problem detection of production systems as more consumers demand manufacturers to offer high-quality goods quickly and at a reasonable cost.

4. Manufacturing process optimization and monitoring.

Production personnel's efforts to identify the ideal set of requirements associated with a specific manufacturing process may be augmented by the prescriptive analytics capabilities of ML approaches, allowing the improvement and optimization of manufacturing processes. In the coming years, the field at the confluence of machine learning in manufacturing and process optimization that results in industrial analytics discoveries that enable quicker mass and personalized production with minimal wastage will develop manifold.

5. Predictive Maintenance

Maintenance of equipment is one of the largest expenditures in the manufacturing business, costing plants and factories about $60 billion in unexpected downtime, 42 percent of which was due to asset failure. As a result, predictive maintenance has emerged as a critical option that may assist save a significant amount of money. Complex ML models are producing reliable forecasts about the condition of resources and machines. The equipment's Remaining Useful Life (RUL) extends dramatically with the help of predictive maintenance. If something has to be fixed or changed, experts will know ahead of time and would even know which procedures to employ.

Machine learning algorithms may use data from sensors implanted in machinery and equipment to generate a predictive maintenance model that eliminates guessing from maintenance operations. Service requests can be initiated automatically using machine learning to inform personnel and engineers of impending problems Models can also compare real-time operational data with historical data to forecast when the problems will occur.

6. Quality Control and its optimization.

Machine Learning plays a critical role in improving the manufacturing process quality. Machine learning algorithms can aid in the availability, performance, quality, and vulnerabilities of assembly equipment. In today's day and age of tight deadlines and increasing product complexities, meeting the finest quality guidelines and requirements becomes even more difficult. Customers demand flawless goods. Thus, it is extremely important to strictly monitor and maintain quality control. Furthermore, product faults might result in recalls, which can severely harm the company's brand and its image. Companies may use AI to detect faults in the manufacturing process that could lead to quality difficulties. These defects might be severe or minor, but they all have an impact on total performance and can be removed early on.

7. Supply chain management and inventory management.

By utilizing machine learning to improve the manufacturing floor, ML can be deployed across the supply chain to produce actual business advantages such as improving operating efficiency and cutting costs. Inventory levels and wastage can be reduced drastically by using machine learning to predict demand and supply and improve production schedules.

ML also helps companies maximize their value by enhancing their logistical solutions, such as capital management, supply chain management, and inventory management procedures. Effective integration of artificial intelligence (AI) and the Internet of Things (IoT) is required for a contemporary company's supply chain to function at its best.

Supply networks have gotten more complicated as they have become more global. A single change in weather or fuel costs may ricochet throughout any organization, having a significant influence on the bottom line. Machine learning can examine each variable that affects the supply chain and alter the whole operation and implement the required adjustments. Simply said, a machine learning algorithm may analyze dozens—or even hundreds—of variables before making the optimal decision for a company.

8. Smart manufacturing

AI and machine learning can generate an almost endless number of design solutions for any issue or commodity, based on several parameters such as dimensions, materials, cost, and so forth. Engineers may use this information to discover the best design solution for a product before it has been put into production. By employing advanced Machine Learning solutions to learn how things are used, how they operate, and what causes them to fail, ML also enables the improvement of new product design and product quality. ML may also be used to forecast the performance of suggested design improvements, which can be communicated back to design teams. 

How AI is improving manufacturing?

When it comes to harnessing AI in the industrial business, the possibilities are endless. While the technology has had a significant influence, there is still a lot of unused potential that may help manufacturers improve every aspect of their operations. Artificial intelligence companies enable all types of manufacturers to perform more efficiently and effectively.

AI can help manufacturers make money in a variety of ways, from anticipating and forecasting demand to developing intelligent, connected devices that produce new and inventive business models.

AI has the potential to not only increase income but also substantially reduce costs. Manufacturers can use it to anticipate maintenance needs, enhance manufacturing and supply chain efficiency, and evaluate service trends to improve product design and reduce warranty costs.

Another advantage that should not be forgotten is risk minimization, which has a huge influence. Manufacturers may use AI to discover product flaws early and enhance quality control procedures to decrease liability, control production risks, solve customer service concerns before they spread, and even reduce safety risks by forecasting the possibility of harm from certain actions.

Benefits of ML in manufacturing 

Machine learning in manufacturing helps in the development of new and efficient manufacturing processes. Machine learning technologies are revamping the entire manufacturing industry and the manufacturing process completely.  A very brief  list of  the major benefits of employing machine learning in manufacturing are as follows: 

  • Significant loss reductions increased profit margins due to process improvements.
  • Cost savings due to predictive maintenance
  • Revenue generation due to consumer-driven product development possible due to Smart manufacturing.
  • Increase in production capacity through optimizing various production processes.
  • Ability to grow product lines via process simplification and optimization.
  • Extended life of machinery and equipment by predicting Remaining Useful Life(RUL) 
  • Better Supply chain management with the help of real-time monitoring and delivery route optimizations
  • Better product quality with optimized quality control.

The manufacturing industry's future will be shaped by machine learning and artificial intelligence.

Overall, using machine learning in manufacturing provides process improvement, reduced overhead, and great productivity. It also enables manufacturers to make rapid judgments and increase the quality of their client service.

The implementation of these technologies at various stages of the manufacturing process aids in the discovery of the new potential for developing more efficient processes, eliminating waste, and saving money. These technologies will continue to advance, and they will become an important tool in the face of market shifts. Manufacturing must be transformed into a process that is as efficient as feasible in terms of human resources, time, and materials. In the close future, companies that do not use Artificial Intelligence(AI)  and Machine Learning(ML) in their manufacturing processes will lose their competitive edge.

Research & analytics team

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Machine Learning in Manufacturing