Do you know over 60% of manufacturing companies have already adopted AI technology to increase operational efficiency, reduce downtime, and deliver high-quality products that meet unique consumer demands? According to Global AI in Manufacturing Market Trends, the market is projected to reach $16.7 billion by 2026, registering a CAGR of 57.2% during the forecast period.
Ask any manufacturer what is the most trendy term in this industry, and you will get an answer like: “Digital transformation”, or “Industry 4.0” powered by Artificial Intelligence (AI).
This article describes top 5 use cases of AI in manufacturing, giving the business owners a set of powerful tools, such as Machine Learning, Deep Learning, and Computer Vision.
What are the common AI use cases in manufacturing?
Order and Inventory Management
Inefficiency in order and inventory management can result in significant cost overheads for the manufacturing company. Using AI tools allows manufacturers to manage their order records and add/delete new inventories. Specifically, the technology behind AI helps manufacturers to keep track of supplies and send alerts when they need to be replenished or reordered. Retex is one of the pioneers in exploiting this technology. Retex is a startup aimed at creating smart textile factories in Vietnam with an IoT system that allows manufacturers to manage and create orders, manage raw materials, and get informed about constant production progress. Furthermore, the system also allows businesses to know whether there are enough materials for production and if not, how much they have to order and how long it takes to arrive. It is obvious that this implementation helped a lot of textile enterprises reduce order entry and inventory management costs and ensure order profitability.
Price and Demand Forecast
Artificial intelligence systems using predictive analytics can also forecast the product demand efficiently. AI tools will collect data from various sources. Later, based on data, tools can accurately predict the product demand.
Apart from demand forecast, AI can also predict pricing. Commodity prices keep changing due to a variety of external factors, it may be hard for manufacturers to decide when it is the best time to buy materials. Knowing the prices of resources is also necessary for companies to estimate the price of their product when it’s ready to reach end-users. AI tools can completely address these issues and provide accurate price recommendations even in the case of dynamic pricing. Retail giant Amazon is using machine learning algorithms to analyze historical and competitive data and thus always offer competitive pricing leading to more profits.
Factories may find it common when finding out some errors or faults in a product. However, not all errors are visible to human eyes. By merely observing the functionality of a product, experts often fail to identify the flaws. And this can be detrimental in manufacturing as thousands of minor errors can accumulate into major faults in the product or the process.
In contrast, AI can identify minor faults in machinery or product, giving manufacturers the option to immediately address before it could become a major flaw.
This is the primary reason why many manufacturing companies today use AI-powered automation and robust tools to detect flaws in the manufacturing process. Through in-depth quality testing using AI, manufacturers ensure high-quality products with faster time to market.
For example, Coca-Cola built the AI-based visual inspection app. The app diagnoses the facility system and detects issues. Technical specialists receive notifications about detected problems and take further actions.
Artificial intelligence in supply chain management
AI-enabled systems can help both parties which are manufacturers and suppliers to assess various scenarios (in terms of time, cost, revenue) to improve last-mile deliveries. AI can predict optimal delivery routes, track driver performance in real-time, and assess weather conditions and inform both sides if there are any delays in delivering.
One example is the carmaker Rolls Royce. It uses advanced machine learning algorithms and image recognition to power its fleet of self-driving ships, which in turn improves its supply chain efficiency and safely transports its cargo.
Digital twins help boost performance
A digital twin is a virtual model of a physical object that receives information about its physical counterpart through the latter’s smart sensors. An application of Digital Twin embraces four technologies which are Internet of Things (IoT), Extended Reality (XR), Cloud, and Artificial Intelligence allowing to create a digital representation, gather and store real-time data, and based on obtained information provide valuable insights. Kaeser, an air-product manufacturing company, has applied digital twins that enable the company to switch from selling a product to selling a service. Specifically, customers will need to pay to monitor operational process of the equipment during its lifecycle, in particular the air consumption rate. As the result, the company has managed to reduce commodity costs by 30% and onboarded nearly half of all major vendors using digital twins.
Is AI the future of manufacturing, especially post-Covid?
100% Yes. Actually, factories started to apply AI in their production before Covid-19 and its benefits are proved even more obvious when the pandemic occurred. A lot of factories are doing well during the pandemic when utilizing the benefit of reducing human labor in some areas which significantly reduces labor costs. In addition, using AI in manufacturing offers process optimization, low-cost overheads, and high productivity. It also allows manufacturers to make quick decisions and improved customer services.
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