In no other sector is artificial intelligence having more of an impact than on manufacturing, and the revolution is just beginning.
There’s no doubt that the manufacturing sector is leading the way in the application of artificial intelligence technology. From significant cuts in unplanned downtime to better designed products, manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees. Here’s how:
Industry 4.0 and smart maintenance
In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation. Moreover, studies show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.
For this reason, predictive maintenance has become a must-have solution for manufacturers who have much to gain from being able to predict the next failure of a part, machine or system.
Predictive maintenance uses advanced AI algorithms in the form of machine learning and artificial neural networks to formulate predictions regarding asset malfunction.
This allows for drastic reductions in costly unplanned downtime, as well as for extending the Remaining Useful Life (RUL) of production machines and equipment.
In cases where maintenance is unavoidable, technicians are briefed ahead of time on which components need inspection and which tools and methods to use, resulting in very focused repairs that are scheduled in advance.
The rise of quality 4.0
Because of today’s very short time-to-market deadlines and a rise in the complexity of products, manufacturing companies are finding it increasingly harder to maintain high levels of quality and to comply with quality regulations and standards.
On the other hand, customers have come to expect faultless products, pushing manufacturers to up their quality game while understanding the damage that high defect rates and product recalls can do to a company and its brand.
Quality 4.0 involves the use of AI algorithms to notify manufacturing teams of emerging production faults that are likely to cause product quality issues. Faults can include deviations from recipes, subtle abnormalities in machine behavior, change in raw materials, and more.
By tending to these issues early on, a high level of quality can be maintained.
Additionally, Quality 4.0 enables manufacturers to collect data about the use and performance of their products in the field. This information can be powerful to product development teams in making both strategic and tactical engineering decisions.
Productivity and Quality Office