Artificial Intelligence (AI) in manufacturing has, for the last couple of years, been a hot topic of discussion. From it being a good thing and bringing efficiencies to being a threat to jobs, AI is a trend to watch out for, not just today but in the future. When you hear or read the terms AI in manufacturing, you probably picture robots on factory floors in assembly lines working round the clock—which is, by the way, one of the examples of AI in manufacturing—but the impact of AI on manufacturing goes well beyond the stereotypical image of robots on the production line.
But, what is AI in manufacturing? AI in manufacturing refers to the optimization of manufacturing processes with improved data analysis by using machine learning (ML) solutions and deep learning neural networks.
The Evolution of AI in the Manufacturing Industry
Even though AI in the manufacturing sector has only started getting attention recently, it’s been around for quite some time now. It dates back to the 1960s when automation and robotics with basic control systems and programmable logic controllers (PLCs) paved the way for the first wave of mechanization. However, it wasn’t until the 1980s and 1990s that AI began to have actual effects in the industry, with the advent of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems. This era also witnessed the rise of expert systems that introduced rule-based decision-making into manufacturing.
Soon after, between the 1990s and 2000s, the integration of AI with robotics enabled robots to execute tasks with increased adaptability and intelligence. During this period, AI algorithms enhanced robot capabilities, enabling more adaptive and flexible automation in manufacturing operations.
The 2000s, then brought about the rise of machine learning and manufacturing entered the era of predictive maintenance, quality control, and process optimization by leveraging the power of data-driven insights.
Today, Industry 4.0, has seen the culmination of technologies such as the Internet of Things (IoT), big data, cloud computing, and AI. Smart manufacturing has become synonymous with real-time monitoring, analytics-driven decision-making, and interconnected production systems.
Applications of AI in Manufacturing
So, how is AI used in manufacturing? Well, AI applications in manufacturing are diverse but this section will discuss AI use cases in manufacturing under three broad categories:
Predictive Maintenance and Machine Learning
Manufacturing plants and assembly lines will at one point break down due to wear and tear. This leads to a lot of downtime and consequently, massive production losses. AI in manufacturing helps solve this problem through predictive maintenance with ML algorithms helping manufacturers predict when equipment will fail. It does so through the integration of sensors and IoT devices on manufacturing equipment which allows AI to collect and transmit data about its performance, operating conditions, and potential issues. ML algorithms then analyze this real-time data and identify patterns and anomalies that may indicate impending failures. This proactive approach to maintenance helps manufacturers transition from traditional, reactive maintenance strategies to a more efficient and cost-effective model.
Quality Control and Visual Inspection Systems
In an era where product quality is key to driving and maintaining sales, quality control becomes a necessity during manufacturing. Using AI in manufacturing helps in controlling the quality of products faster and better than human workers. AI uses computer vision systems powered by deep learning algorithms to conduct visual inspections for defects, deviations, or irregularities. This enhances customer satisfaction and reduces the likelihood of recalls and associated by ensuring that only products meeting stringent quality standards reach the market.
Supply Chain Optimization and Demand Forecasting
Since supply chains comprise numerous processes, it can be quite complex to manage them efficiently. However, with AI managing certain processes becomes easier. For instance, AI algorithms can handle inventory management by analyzing historical data, market trends, and external factors to optimize inventory levels. This, in turn, reduces the risk of overstocking or stockouts, leading to cost savings and improved operational efficiency.
Demand forecasting is also a critical component of supply chain management, and AI plays an important role in enhancing its accuracy. While traditional forecasting methods often rely on historical data and statistical models, AI utilizes machine learning algorithms to analyze many data sources, including customer behavior, market trends, and social media sentiment, to predict future demand more accurately. This ability of AI to process large datasets quickly and identify complex patterns enables manufacturers to respond rapidly to changes in market conditions.
Benefits of Implementing AI in Manufacturing
The use of AI in manufacturing has several benefits including:
- Reduced Downtime: AI-driven predictive maintenance helps manufacturers anticipate equipment failures before they occur. This proactive approach reduces downtime, minimizes production losses, and extends the lifespan of machinery.
- Cost Savings: AI, through robotic process automation reduces the need for manual labor in repetitive tasks. This not only lowers labor costs but also minimizes errors and increases production speed.
- Competitive Advantage: Implementing AI in manufacturing provides a competitive advantage in a global market. The efficiency gains, cost savings, and ability to adapt to market changes position businesses at the forefront of industry advancements.
- Improved Worker Safety: The use of collaborative robots (cobots) equipped with AI capabilities working alongside human workers in smart factories allows AI to take on tasks that pose safety risks. This not only enhances overall workplace safety but also allows human workers to focus on more complex and creative aspects of their roles.
- Innovation and Product Customization: AI supports innovation in manufacturing by enabling the development of customized products. Through machine learning and adaptive manufacturing processes, businesses in manufacturing can respond to individual customer preferences and stay competitive in a market that values personalized offerings.
Prepare for the Future of Manufacturing Today
The benefits of AI in manufacturing that businesses have so far reaped indicate that the future of AI in manufacturing will be even more transformative. As such, to stay competitive in the market, you must take strides towards implementing AI in your production processes. One way you can do this is by investing in AI solutions in manufacturing, such as Surgere’s Interius.
Operating on a 100 percent cloud-based infrastructure and built on Microsoft architecture, Interius provides clients with unparalleled access to real-time supply chain metrics, reports, and insights. It leverages the power of AI and machine learning to ensure that the platform not only delivers critical data but evolves to provide proactive alerts, identify trends, and address issues, ultimately keeping your business ahead in its supply chain operations. Need more information about Interius? Contact us today.