Notable use cases of AI in Manufacturing

The adoption of AI in Manufacturing is among the trends to watch in the near future. It is expected to increase productivity and address any possible disruption in the production process.

AI-empowered processes have become an integral attribute of the manufacturing sector. There is an incredible diversity to the use cases of AI in manufacturing. While augmented reality devices have offered a helping hand to those who run the production line, automated systems boost and facilitate efficiency and product quality in many ways, including reducing unexpected human mistakes.

Quality assurance

One of the practical applications of AI in manufacturing is automated flaw detection. With advanced visual inspection tools and intelligent algorithms, smart machinery can continuously monitor the productivity of manufacturing lines. At the same time, it can identify any defects or inconsistencies which may be undetectable to the human eyes. The defective products will be marked, and the alerts will be sent to human workers. That way, the manufacturers can eliminate under-qualified items before distributing them in public.

Predictive maintenance

Maintenance is estimated to be the most extensive implementation for AI in manufacturing. Manufacturers could foresee any possible glitch that might happen thanks to the predictive analytical solutions, powered by machine learning algorithms.

a production line that can be improved by leveraging AI in manufacturing

Besides, several modernized factories even embed Internet of Things (IoT) sensors and devices in machines to produce better maintenance predictions. As a result, they can avoid unplanned downtime, and disrupted workflow, as well as let the staff focus on other value-adding tasks.

Predictive demand and inventory management

A subtle shift in consumer demand can bring about major fluctuations in the future. AI and machine learning applications collect and learn customer data from past orders, then predict future demand accordingly.

This well-managed predictive data could also be the byproduct of inventory monitoring in the factory. Via IoT devices or computer vision, warehouse managers monitor stock automatically and accurately, without the need for manually checking shelves and storages.

With the help of AI in manufacturing, businesses can optimize the time between demand creation and stock delivery significantly. They will no longer be in a passive situation when it comes to handling shifts in demands and balancing sticks. Therefore, manufacturers can prevent inefficient inventory management t and reduce the risk of material surplus or deficit during the production period.

Customer Management

As manufacturers often serve large-scale clients, they should pay more attention to customer service. Though often overlooked in this particular industry, a successful retention strategy may help gain customer satisfaction and boost businesses’ performance, productivity, and sales. With the help of AI in manufacturing, which rapidly and effectively conducts customer data analysis, service providers can analyze the patterns in their behaviors, better understand their needs, and quickly resolve them. 

Furthermore, chatbots can act as smart assistants to improve customer experience. They can enable seamless communication between manufacturers and clients about placing and processing orders. They can also serve as a specialized resource in the operation rooms and on the factory floor.

So far, the contributions of AI-enabled solutions to Manufacturing are evident and undeniably impactful. However, many manufacturers are unwilling to adopt them into their expensive machines and equipment. Any mishap in applying AI in manufacturing could lead to a cost blowout. Additionally, the return on investment of AI systems and the accuracy of the inspection and maintenance process are still in question. Though these systems are getting more and more accurate, businesses can be reluctant to take the risk.

Additionally, some businesses have not succeeded in implementing AI tools because of the lack of efficient data collection and a skillful workforce. Regarding data, many companies have unstructured databases that need extra effort for refining and organizing before they can be processed by AI. In terms of the workforce, acquiring AI-savvy personnel and giving AI training are challenging tasks for decision-makers, especially when they take into account the costs and time.

In short, AI in manufacturing can be effective at improving accuracy, productivity, and cost-effectiveness. Although many AI advancements have been successfully achieved, there is still much work to be done. To make AI a high-return investment, businesses need to determine the integration strategy that fits their model to enhance performance to harmonize the collaboration between machine and human elements to yield the most optimal outcomes.

Outstanding case study in manufacturing sector

From the idea of a visionary South Korean start-up company, GEM developed a dynamic virtual marketplace for the manufacturing sector. The enablement of rapid and transparent supplier-buyer communications is the online platform’s most striking advantage. The case study also stands out due to the amount of technical consultancy input provided by GEM.