According to the World Economic Forum, the global manufacturing sector could be one of the sectors most influenced by the latest technological trends like AI, machine learning and IoT – collectively termed Industry 4.0 – with great potential for disruption and transformation if these technologies are employed intelligently.
While AI has proven to be one of the most broadly disruptive technologies of the digital revolution, it best maximizes its potential when deployed in conjunction with two augmentative domains – robotics and Internet of Everything (IoE).
Robotics has become an integral part of the manufacturing sector over the past two decades and the finesse, complexity, and sophistication of robotic tasks have been significantly enhanced via AI. Tasks which were previously relegated to the human domain due to complexity and labor constraints are now routinely completed by robots.
As for IOE, the ease of deployment and advanced capabilities of sensors allow for the universalization of AI in the manufacturing sector. Because sensors collect data continuously and can be placed nearly anywhere, manufacturers can expect to increase productivity, connectivity, and scalability as IOE becomes more engrained in the sector.
The question for manufacturers remains, wherein the operation is AI most applicable? We will outline a list of 15 use cases across seven segments.
Predictive and Preventive Maintenance
A top area in maintenance is the area of data-driven maintenance enabling the transformation of maintenance in manufacturing from reactive to preventive maintenance powered by AI enabled predictive capability. A whopping $647 billion is lost globally each year in industrial asset downtime, per the International Society of Automation (Source: https://www.isa.org/standards-publications/isa-publications/intech-… ). Role of sensors and IOT enabled devices to enable real-time information feed to AI engines is key. IOT when applied as sensors in an industrial setting often termed as IIOT Industrial IOT. This works in conjunction with AI to achieve the desired results.
AI has the potential to drive the regime towards enhanced uptime reducing downtime via different possibilities:
Use Case 1: Real-Time Alert of Wear, Tear, Fault, or Breakdown – Warning signals of potential breakdown by AI, it could even look ahead for fatigue
Use Case 2: Lifetime Prediction: Using AI to accurately predict Time to Live for Assets like Machinery improving overall life of machinery and assets
Use Case 3: AI to enable more informed asset maintenance schedule triggering a focused repair and MRO schedule optimizing overall effort, cost, and quality across assets.
Enhancing Robots Effectiveness
While currently, robots are quite mainstream in automating manufacturing shop floors presence of AI can enhance the role of robots by better task handling
Use Case 4: Enhanced effectiveness of robots in form of powerful software to enable robots to take on complex tasks. Not just complexity but also the versatility of tasks enhanced by AI
Use Case 5: Role of AI in better human-robot interaction to enable more effective utilization of robots is key. Cobots are emerging as potential enablers in this area.
Manufacturing supply chain
The overall manufacturing industry is heavily dependent upon the accompanying supply chain effectiveness for overall productivity and efficiency. AI combined with IOT has tremendous potential
Some identifiable use cases are as below:
Use Case 6: Real-time tracking of supply vehicles helps in better utilization of logistics fleet thereby optimizing overall production schedule
Use case 7: Better data-driven AI-based approach to analyzing inventory and thereby using it to lower inventory costs can be a great cost saver for manufacturers.
Use case 8: Shipping and Delivery Lead Time can not only be accurately predicted, but it is also optimized via application of AI algorithms
Design Disruption
AI has an element of technology which has enabled take on roles of creative tasks like art music etc. A related use case in the context of manufacturing is appearing more and more real.
Use Case 9. Use of AI-based generative design is being used by large design houses like auto manufacturers. airplane manufacturers etc enabling creative machine or part or asset designs not limited by human designers.
Quality Management and Improvement
Several data-driven initiatives are now becoming mainstream in manufacturing processes, most prominent of them being in the area of quality management and improvement.
Use Case 10. Quality process improvement. AI can enable understand limitations, shortcomings, or deficiencies of current as manufacturing quality processes and using AI applied on quality data several improvement opportunities can be harnessed.
Use Case 11: Using complex AI like computer vision to explore defects in produced items can be a great way to ensure product quality.
Digital Twin
A recent initiative spanning several sectors of manufacturing is the idea of digital twin where there is an equivalent mapped equivalent of a process in reality. AI role is such a digital twin areas below.
Use Case 12: Idea of such a digital twin is to understand and simulate how the process flows occur and identify what if scenarios via AI. AI thus enables the realization of potential implications of the process
Use Case 13: Exception Management: In conventional workflows, exceptions are usually routed to humans to take care of the same. In an AI wired process such processes could be automated and straight through actions could be taken by programs rather than humans
Use Case 14: Testing of design and manufacturing feasibility of items can be carried out intelligent simulations.
Mass Customization and N=1
In the world of data driven product management, a key application of AI will be in terms of understanding customers closely.
Use Case 15: Understanding customers closely and designing, manufacturing and testing products with a high level of customization. This leads to change of models of design and manufacturing also to include flexible ways of catering to all diverse products. Example of BTO models falls in this.
So we can safely now say AI is here to disrupt the manufacturing industry in conjunction with Robotics and IOT like technologies ushering in the broadly accepted term of Industry 4.0
