Artificial intelligence in asset management is part of a more significant trend toward fully automated production. AI systems can transform the way organisations manage their production lines with the development of “smart factories,” enabling higher efficiency by increasing human capabilities, offering real-time insights, and promoting design and product innovation.
Successful asset management is a key to successful growth for most businesses. It’s only practical that companies should try to improve and manage their assets continuously, especially in today’s fast-paced industrial markets.
In this blog, we will see how asset management, predictive maintenance, and overall asset optimisation present both obstacles and opportunities when viewed through the perspective of Industry 4.0 and Artificial Intelligence.
Impact of Industry 4.0 on asset management
Industry 4.0 allows for the real-time gathering of essential data regarding a machine or equipment’s operation. According to the poll, 55% of manufacturers presently use this data to make asset management decisions based on actionable, real-time, role-based data—another 28% plan to do so within the following year.
Two-thirds of executives who describe their company’s Industry 4.0 skills are presently employing real-time data for their asset management.
In addition, digital leaders are increasingly employing innovative human-machine interfaces on their plant floors, which, when combined with equipment-specific technology, can assist manufacturers in digitising machine operation and maintenance. As a result, digital leaders say that their manufacturing equipment and processes contain higher percentages of intelligent devices and embedded intelligence.
According to the study’s findings, the deployment of Industry 4.0 in plants and processes and its impact on asset management activities has increased productivity and profitability for practically all businesses. The adoption of intelligent devices has reportedly improved asset management performance for most manufacturers.
How does AI impact modern asset management?
The possibilities for AI applications in asset management are limitless, affecting the entire manufacturing chain. Given the evolution of technology in society, it’s not surprising that new technologies and use cases in asset management continue to emerge. Today’s technologies, such as AI and machine learning, are built on top of existing technology and engineering infrastructure.
We already see solutions to help clients avoid risk, cut costs, increase profits, and provide products and services quickly. As asset managers collect ever-increasing amounts of consumer data, AI is also utilised for data management.
Modern technology, such as innovative AI-powered solutions, can assist tackle a variety of outdated challenges in the asset management business while staying competitive in an ever-changing and competitive market.
What are the Challenges ahead?
While a reactive MRO approach may suffice for a non-critical item quickly and cheaply, the more complicated and critical the asset, the more data and sophisticated skills are required to monitor its state.
The ability to process and analyse these multiple streams of data simultaneously – even in real-time – will become increasingly important. It is where AI will provide the most value. Its ability to analyse data and learn and adjust to new inputs will help companies make better business decisions and minimise waste across the supply chain.
However, there are still significant obstacles to fully deploying AI and Industry 4.0 in a way that benefits asset management. To begin, firms must hire people with the proper skill sets and upskill their current employees to run and work with digital systems at the level required for their jobs.
Second, in terms of interoperability, the challenge is well-known. Industry must develop technology-bound standards that allow for simple data movement across platforms. It is one of AI’s most significant issues because analysing data across platforms will be extremely difficult without a standard language, given many information sources and languages.
The success or failure of AI is partly determined by the smooth movement of data between equipment and providers. Therefore providing data accessibility will be critical. However, many OEMs, component suppliers, and manufacturers are still hesitant to do so because of security concerns.
According to ERIKS’ research, 79% of respondents would only share a small amount of information with their OEM equipment partner. In the view of IT directors, the risks of opening up IT networks to suppliers outweigh the benefits.
As a result, the industry must break down the obstacles that inhibit data sharing. You can address these issues by developing suitable security solutions, ranging from firewalls to private cloud environments, FOG computing, etc. Still, we must also highlight the benefits that more data sharing may offer.
Importance of AI and Industry 4.0 on asset management
The asset management landscape and the software that supports it are reshaped by technological breakthroughs. Organisations can safely ignore hardware and IT investments in favour of investing in their main lines of business because of the cloud. Analytics has become increasingly sophisticated to deliver the best data for essential daily decisions. Advanced sensors and sensor fusion and edge devices are used in artificial intelligence, the internet of things, and machine learning to give more advanced monitoring and diagnostic capabilities.
Industry 4.0 allows for the real-time gathering of essential data regarding a machine’s or piece of equipment’s operation. According to the poll, 55 manufacturers presently use this data to make asset management decisions based on actionable, real-time, role-based data, with another 28% planning to do so within the following year.
FactoryWorx Maintenance and Asset Management is an IIoT-based system with sophisticated capabilities to swiftly collect and analyse data from various equipment and deliver critical insights. Their solution assists firms in lowering manufacturing costs while also increasing efficiency, quality, and customer happiness.
Your organisation can use Artificial Intelligence to make better decisions to decrease costs, boost productivity, and drive innovation if it can capture valuable data from every piece of equipment on the manufacturing floor and broader business systems and external sources.
Within firms’ asset productivity strategies, the benefits of Industry 4.0 become pretty clear. The extensive use of sensors to collect data, software to collect and aggregate the data, and analytics combined with artificial intelligence to analyse and develop actionable predictions—is the power it can offer intelligent asset management.