Manufacturing

Predictive maintenance allows companies to predict when machines need maintenance with high accuracy, instead of guessing or performing preventive maintenance. Predictive maintenance prevents unplanned downtime by using machine learning. Technologies such as sensors and advanced analytics embedded in manufacturing equipment enable predictive maintenance by responding to alerts and resolving machine issues. 

Automation will help the manufacturing industry reach a high level of accuracy and productivity, a level that is even beyond human ability. It can even work in environments that are otherwise dangerous, tedious or complicated for humans. Robotics as a service, which are expected in the future, will have capabilities like voice and image recognition that can be used to re-create complex human tasks.

Some flaws in products are too small to be noticed with the naked eye, even if the inspector is very experienced. However, machines can be equipped with cameras many times more sensitive than our eyes – and thanks to that, detect even the smallest defects. Machine vision allows machines to “see” the products on the production line and spot any imperfections. The logical next step might be sending the pictures of said flaws to a human expert – but it’s not a must anymore, the process can be fully automated. 

This sounds very general but in reality, there’s a whole variety of ways to use big data in manufacturing. Manufacturers collect vast amounts of data related to operations, processes, and other matters – and this data combined with advanced analytics can provide valuable insights to improve the business. Supply chain management, risk management, predictions on sales volume, product quality maintenance, prediction of recall issues – these are just some of the examples of how big data can be used to the benefit of manufacturers. This type of AI application can unlock insights that were previously unreachable.

To manufacture products, you first need to purchase the necessary resources, and sometimes the prices can get a little crazy. For example, if you buy stainless steel, its price is affected by a variety of factors, including the listings of Metal Exchange or the prices of other elements, some of them not listed on the metal exchange. With the rapid changes in prices, sometimes it may be hard to assess when it’s the best time to buy resources. Knowing the prices of resources is also necessary for companies to estimate the price of their product when it’s ready to leave the factory. Let’s stick to the example of stainless steel: the prices can vary, depending on the current listings of e.g. nickel or the price of ferrochrome. The system is able to provide accurate price recommendations just like in the case of dynamic pricing that’s used by e-commerce businesses where machine learning algorithms analyze historical and competitive data to always offer competitive prices and make even more profit.

In manufacturing, however, the importance of customer service is often overlooked – which is a mistake as lost customers can mean millions of dollars in lost sales. AI solutions can analyze the behaviors of customers to identify patterns and predict future outcomes. Observing actual customers’ behaviors allows companies to better answer their needs. Our solutions allow service providers to quickly identify issues and prioritize improvements. There’s a variety of ways artificial intelligence can improve customer service. 

While humans are forced to work in 3 shifts for ensuring continuous production, while robots are capable to work for 24/7 in the production line. Businesses can be witnessed to expand in terms of production capabilities and meet the high demand of customers worldwide.

With several errors taking place on the manufacturing plant, a step towards AI means less human resource have to carry out dangerous and overly laborious work. As robots replace humans and perform normal and risky activities, the number of workplace accidents will decrease all across.

Although, bringing AI onto the manufacturing industry would necessitate a huge capital investment, the ROI is significantly high. As intelligent machines start taking care of day-to-day-activities, businesses can enjoy considerably lower operating cost.

Bring visibility, insight and control to global operations. Do more with less. Increase demand responsiveness. Automate manufacturing and business processes to increase agility across the value chain.