A lot of the hype surrounding artificial intelligence in manufacturing is focused on industrial automation, but this is just one aspect of the smart factory revolution — a natural next step in the pursuit of efficiency. Artificial intelligence also brings its capability to reveal new avenues for business to the manufacturing table. We will outline the ability of artificial intelligence to drive industrial automation and open up new business opportunities as part of the emerging Industry 4.0 paradigm. Also, we will introduce how this powerful technology is already being used by manufacturers to drive efficiency, improve quality, and better manage supply chains.
AI Manufacturing Use Cases
#1: Predictive Quality & Yield
Reducing production losses and preventing production process inefficiencies has always been a challenge for manufacturers across all industries. Today, this is as true as ever, as growing demand meets increased competition.
On the one hand, consumers’ expectations are very high; global consumer habits are gradually “westernizing,” even as the population boom continues. According to numerous surveys in recent years, the global population will grow by 25 percent by 2050, equating to 200,000 additional mouths to feed every day.
On the other hand, consumers have never had so many product alternatives at their disposal. Recent surveys indicate that this wealth of options means consumers are increasingly likely to permanently ditch even their favorite brands if, for example, a product isn’t available on the shelf.
With these trends in mind, manufacturers can no longer afford to accept process inefficiencies and their associated losses. Every loss in terms of waste, yield, quality, or throughput chips away at their bottom line and hands another inch to the competition — assuming their production processes are more efficient.
The challenge for many manufacturers — particularly those with complex processes — is that they eventually hit a ceiling in terms of process optimization. Some inefficiencies do not have an obvious root cause, which puts process experts at a loss to explain them.
Predictive quality and yield use AI-driven processes and machine health solutions to reveal the hidden causes of many of the perennial production losses manufacturers face. This is done via continuous, multivariate analysis, using machine learning algorithms that are uniquely trained to intimately understand individual production processes.
The specific AI/machine learning technique used here is called supervised learning which means the algorithm is trained to identify trends and patterns within the data. Automated recommendations and alerts can then be generated to inform production teams and process engineers of an imminent problem, and seamlessly share important knowledge on how to prevent losses before they happen.
#2: Predictive Maintenance
Predictive maintenance is one of the most well-known applications of industrial AI. Instead of performing maintenance according to a predetermined schedule, predictive maintenance uses algorithms to anticipate the next failure of a component, machine, or system and then alerts personnel to perform focused maintenance procedures to prevent the failure. These alerts happen at the right time so as not to waste unnecessary downtime.
These maintenance systems rely on unsupervised machine-learning techniques to formulate predictions. Predictive maintenance solutions can help reduce costs while also eliminating the need for planned downtime in many cases, which strengthens the bottom line and improves the employee experience.
By preempting a failure with machine learning, systems can continue to function without unnecessary interruptions or delays. The maintenance that is needed is very focused – technicians are informed of the components that need inspection, repair, and replacement; which tools to use, and which methods to follow.
Predictive maintenance also leads to a longer Remaining Useful Life (RUL) of machinery and equipment since secondary damage is prevented while smaller labor forces are needed to perform maintenance procedures. Improving RUL can increase sustainability efforts and reduce waste.
#3: Human-Robot Collaboration
According to the International Federation of Robotics (IFR), roughly 1.64 million industrial robots are in operation worldwide as of 2020. There are fears of robots taking jobs, but the industry is seeing workers embrace training for higher-level positions in programming, design, and maintenance.
Humans are also working alongside robots to improve efficiency and productivity on the factory floor and beyond. AI will play a significant role as robots become more ingrained in manufacturing. It will ensure the safety of human workers as well as give robots more autonomy to make decisions that can further optimize processes based on real-time data collected from the production floor.
#4: Generative Design
Manufacturers can also make use of artificial intelligence in the design phase. With a clearly defined design brief as input, designers, and engineers can make use of an AI algorithm, generally referred to as generative design software, to explore all the possible configurations of a solution.
The brief can include restrictions and definitions for material types, production methods, time constraints, and budget limitations. The set of solutions generated by the algorithm can then be tested using machine learning. The testing phase provides additional information about which ideas or design decisions worked, and which did not. From there, additional improvements can be made until an optimal solution is reached.
#5: Market Adaptation & Supply Chain
Artificial intelligence permeates the entire Industry 4.0 ecosystem and it is not limited to the production floor. AI algorithms can optimize the supply chain of manufacturing operations and help manufacturers better respond to, and anticipate, the changing market.
An algorithm can construct estimations of market demand by taking into account demand patterns categorized by a multitude of factors like date, location, socioeconomic attributes, macroeconomic behavior, political status, weather patterns, and more. Manufacturers can use this information to plan for the road ahead. A few processes that can be optimized with these insights include inventory control, staffing, energy consumption, raw materials, and financial decisions.
Industry 4.0 & Collaboration
AI is trendy but it requires collaboration to get it right. To get started, manufacturers should weigh the pros and cons of buying vs. building the required technology and expertise. An Industry 4.0 system consists of a number of elements and phases that are unique to the manufacturer:
- Historical data collection.
- Live data capturing via sensors.
- Data aggregation.
- Connectivity via communication protocols, routing, and gateway devices.
- Integration with PLCs.
- Dashboards for monitoring and analysis.
- AI Applications: machine learning and other techniques.
Industrial AI is no longer a far-off aspiration. Manufacturers can use these technologies today to meet their specific business challenges and needs. As Industry 4.0 evolves with more complexity, manufacturers will require the agility and visibility made possible by artificial intelligence.
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