Many of the hype surrounding artificial intelligence in the manufacturing industry has focused on industrial automation, but this is only one aspect of the intelligent factory revolution - the natural next step in the pursuit of efficiency. AI also brings the ability to uncover new business avenues for manufacturing tables.
As part of the Emerging Industry 4.0 paradigm, we will outline the ability of artificial intelligence to drive industrial automation and open up new business opportunities. In addition, we will describe how manufacturers can use this powerful technology to improve efficiency, improve quality, and better manage the supply chain.
1: Predict quality and production
Reducing production losses and preventing inefficient production processes have been challenges for manufacturers in all industries. Today, as growing demand meets increasingly fierce competition, this continues to apply.
On the other hand, consumers have never had so many products to choose from. Recent surveys have shown that this rich selection means that consumers are increasingly likely to permanently abandon their favorite brands, for example, if there are no products on the shelves.
Predicting quality and production uses AI driven processes and machine health solutions to uncover the hidden causes of many perennial production losses faced by manufacturers. This is accomplished through continuous multivariate analysis using uniquely trained machine learning algorithms to gain an in-depth understanding of various production processes.
The specific AI/machine learning technology used here is called supervised learning, which means that the algorithm is trained to identify trends and patterns in data. Automatic suggestions and alerts can then be generated to inform the production team and process engineers of imminent issues, and important knowledge on how to prevent losses before they occur can be seamlessly shared.
2: Predictive Maintenance
Predictive maintenance is one of the most famous applications of industrial artificial intelligence. Predictive maintenance is not about performing maintenance based on a predetermined schedule, but rather using algorithms to predict the next failure of a component, machine, or system, and then alerting personnel to perform key maintenance procedures to prevent failures. These alerts occur at the right time to avoid wasting unnecessary downtime.
These maintenance systems rely on unsupervised machine learning techniques to make predictions. Predictive maintenance solutions can help reduce costs, while in many cases eliminating the need for planned downtime, enhancing the bottom line and improving the employee experience.
By preventing failures through machine learning, the system can continue to operate without unnecessary interruptions or delays. The maintenance required is very targeted - technicians are informed of components that need to be inspected, repaired, and replaced; Which tools to use and which methods to follow.
Predictive maintenance can also extend the remaining service life (RUL) of machines and equipment because it prevents secondary damage and requires less labor to perform maintenance procedures. Improving RUL can increase sustainability efforts and reduce waste.
3: Human-machine cooperation
According to the International Federation of Robotics (IFR), as of 2020, there are approximately 1.64 million industrial robots in operation worldwide. People are worried that robots will steal jobs, but the industry is seeing workers accept programming, design, and maintenance.
Humans also work with robots to improve efficiency and productivity both inside and outside the factory floor. As robots become increasingly entrenched in the manufacturing industry, artificial intelligence will play an important role. It will ensure the safety of human workers and give robots more autonomy to make decisions that can further optimize processes based on real-time data collected from production workshops.
4: Market Adaptation and Supply Chain
Artificial intelligence permeates the entire Industry 4.0 ecosystem and is not limited to production workshops. Artificial intelligence algorithms can optimize the supply chain of manufacturing operations, helping manufacturers better respond and predict changing markets.
The algorithm can construct market demand estimates by considering demand patterns classified by multiple factors such as date, location, social-economic attributes, macroeconomic behavior, political status, weather patterns, and so on. Manufacturers can use this information to plan future roads. Some processes that can be optimized using these insights include inventory control, staffing, energy consumption, raw materials, and financial decisions.