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. What artificial intelligence also brings to the manufacturing table is its capability to open up completely new avenues for business.
Below is an outline that covers both these aspects of artificial intelligence within the Industry 4.0 paradigm, and how this powerful technology is already being used by manufacturers to drive efficiency, improve quality and better manage supply chains.
Industrial AI’s Impact on Manufacturing
Artificial intelligence’s impact on manufacturing can be organized into 5 main areas:
Predictive maintenance has become a very sought-after use case for manufacturers looking to advance to Industry 4.0. Instead of performing maintenance according to a predetermined schedule, predictive maintenance uses algorithms to predict the next failure of a component/machine/system and then alerts personnel to perform focused maintenance procedures to prevent the failure, but not too early so as to waste downtime unnecessarily.
One of the most common applications of AI for manufacturing is machine learning, and most predictive maintenance systems rely on this technique to formulate predictions. The advantages are numerous and can significantly reduce costs while eliminating the need for planned downtime in many cases.
By preempting a failure with a machine learning algorithm, systems can continue to function without unnecessary interruptions. When maintenance is needed, it’s 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 labour forces are needed to perform maintenance procedures.
Regression labeling for Predictive Maintenance. Each recorded instant (5Y, 4Y, 3Y etc.) describes the Remaining Useful Life of an asset before it is predicted to fail.
Manufacturers are finding it harder than ever to maintain consistently high levels of quality. This is due in part to a rising complexity in products (that integrate software, for example) and very short time-to-market goals.
Despite these challenges, managers are highlighting quality as a top priority, realising the importance of the customer’s experience of a product and the power of customers to push a brand forward as well as being aware of the pain point of high defect percentages and product recalls.
Using Industry 4.0 techniques, this new quest for quality has been appropriately named Quality 4.0 and artificial intelligence is at its forefront. Quality issues cost companies a lot of money, but with the use of AI algorithms developed through machine learning, manufacturers can be alerted of initially minor issues causing quality drops, similar to the way alerts are created for predictive maintenance.
Quality 4.0 allows manufacturers to continually improve the quality of their output while collecting usage and performance data from products and machinery in the field. This data becomes a vital source of information that forms the basis for product development and crucial business decisions.
According to the International Federation of Robotics, by the end of 2018, there will be 1.3 million industrial robots working in factories around the world. The general approach is that as jobs get taken over by robots, workers will be offered training for higher-level positions in programming, design, and maintenance. In the meantime, the efficiency of human-robot collaborative work is being improved as manufacturing robots are approved for work alongside humans.
As the adoption of robotics in manufacturing increases, AI will play a major part in ensuring the safety of human personnel as well as giving robots more responsibility to make decisions that can further optimize processes based on real-time data collected from the production floor.
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/design decisions worked, and which did not. In this way, additional improvements can be made until an optimal solution is found.
Artificial intelligence permeates the entire Industry 4.0 ecosystem and is not only limited to the production floor. One example of this is the use of AI algorithms to optimize the supply chain of manufacturing operations and to help them better respond to, and anticipate, changes in the market.
To construct estimations of market demand, an algorithm can take into account demand patterns categorized by date, location, socioeconomic attributes, macroeconomic behavior, political status, weather patterns and more.
This is groundbreaking for manufacturers who can use this information to optimize inventory control, staffing, energy consumption, raw materials, and make better financial decisions regarding the company’s strategy.
Industry 4.0 Demands Collaboration
The complexity of using artificial intelligence in industrial automation requires that manufacturers collaborate with specialists to reach customized solutions. Attempting to build the required technology is costly and most manufacturers don’t have the necessary skills and knowledge in-house.
An Industry 4.0 system consists of a number of elements/phases that need to be configured to suit the manufacturer’s needs:
To truly leverage AI, manufacturers will do well to partner with experts who understand their goals and who can help create a clearly defined roadmap with an agile development process that links the AI implementation to relevant KPIs.