5 Powerful Use Cases of AI in Manufacturing

A successful test run saw it catch 72 instances of component failure across 7,000 robots. Although covering all the AI use cases in manufacturing would go beyond the scope of this blog, let’s delve into the five most impactful ones. These serve as excellent starting points for manufacturers to direct their efforts. Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year.

Electronics manufacturer Philips also operates a factory in the Netherlands that makes electric razors, where a total of nine human members of staff are required on site at any time. This is a trend that we can expect to see other companies working towards adopting as time goes by as technology becomes increasingly efficient and affordable. Using a robots-only workforce means a factory can potentially operate 24/7 with no need for human intervention, potentially leading to big benefits when it comes to output and efficiency. Of course, questions will need to be addressed about what the impact removing humans from the manufacturing workforce will have on wider society. Design engineers in the manufacturing industry can use this method to create a wide selection of design options for new products they want to create and then pick and choose the best ones to put into production. In this way, it accelerates product development processes while enabling innovation in design.

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Manufacturers can potentially save money with lights-out factories because robotic workers don’t have the same needs as their human counterparts. For example, a factory full of robotic workers doesn’t require lighting and other environmental controls, such as air conditioning and heating. An AI in manufacturing use case that’s still rare but which has some potential is the lights-out factory. Using AI, robots and other next-generation technologies, a lights-out factory operates on an entirely robotic workforce and is run with minimal human interaction.

• Moreover, these solutions ensure that warehouse managers purchase materials on time to avoid potential shortages.
• Jackson has more than 30 years of executive experience and was previously CEO of video analytics provider Drishti, which was recently acquired.
• Whether you’re a manufacturing veteran or a tech enthusiast, this article will help you understand the significant role AI has to play in shaping the future of manufacturing.
• Companies can use digital twins to better understand the inner workings of complicated machinery.
• The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces.

Some have owned a manufacturing company, so they understand the language you speak, and the challenges you face. There are vendors who promise a prebuilt predictive maintenance solution and all you do is plug your data in. The solution you need is based on understanding your process and tweaking based on your priorities. With any new technology rollout, it makes sense to start with a pilot such as piloting AI on one production line.

Environmental impact

Similarly, detecting and tracking supplies and preventing bottlenecks within processes gives manufacturers the real-time information to make future-proof decisions that have the smallest impact on production lines. AI in manufacturing enables predictive maintenance by analyzing sensor data from machinery and equipment. This allows manufacturers to anticipate when equipment might fail and perform maintenance tasks before a breakdown occurs. This reduces downtime and maintenance costs and enhances AI algorithms can identify patterns and anomalies in data, predicting when a component might fail based on historical data and real-time inputs, thus enabling timely interventions.

These systems adapt production processes to accommodate individual customer preferences, resulting in tailored products. The manufacturing industry, a cornerstone of global economies, stands on the cusp of a technological revolution powered by artificial intelligence (AI). This article delves into the remarkable ways AI reshapes manufacturing processes, illuminating ten pivotal use cases that underscore its transformative potential. A. AI enhances product quality and reduces defects in manufacturing through data analysis, anomaly detection, and predictive maintenance, ensuring consistent standards and minimizing waste. To realize the full impact of AI in manufacturing, you will need the support of an expert AI Software development services company like Appinventiv.

This will need to be followed by managing the migration to a new digital architecture and executing it flawlessly. Helping clients meet their business challenges begins with an in-depth understanding of the industries in which they work. In fact, KPMG LLP was the first of the Big Four firms to organize itself along the same industry lines as clients. KPMG has market-leading alliances with many of the world’s leading software and services vendors. Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there.

The outcome is high-precision manufacturing, with a remarkable 15% enhancement in uniformity compared to traditional methods. Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design. Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning.

Kellogg’s has fully embraced the potential of AI across operations, from enhancing supply chain efficiency to crafting optimal flavor combinations for new products. This results in data-driven decision-making, faster design cycles, and the ability to create products that fit market needs. Each oversees a different production stage—from conception to assembly to operation. It also suggests energy-saving opportunities, boosting overall production line performance. The power of automotive AI-based predictive maintenance can be seen in the example of a leading automotive manufacturer- Ford. US Steel is building applications using Google Cloud’s generative artificial intelligence technology to drive efficiencies and improve employee experiences in the largest iron ore mine in North America.

When paired with a vision system, a machine learning model predicts potato weights as they’re processed. This move saved the company a significant amount by eliminating the need for expensive weighing elements. Another ongoing project aims to assess the “percent peel” of a potato post-peeling.

Demand Forecasting

Manufacturers can streamline logistics and reduce lead times by predicting demand, automating procurement, and identifying potential disruptions. This predictive approach enhances supply chain efficiency and builds stronger relationships with suppliers. The use of generative design software for new product development is one of the major AI in manufacturing examples. With the help of a generative AI development company, engineers can input design parameters and performance goals, and the AI algorithms can generate multiple design options, exploring a vast range of possibilities. The use of generative AI in manufacturing thus accelerates the design iteration process, resulting in optimized and innovative product designs.

Read more about Cases of AI in the Manufacturing Industry here.