The Role of AI in Smart Manufacturing

smart manufacturing

While automation was responsible for changing the 20th-century production line, AI technologies are taking manufacturing industry to the next level and into the era of Smart Manufacturing. When we talk about Smart Manufacturing, the conversation invariably moves to the use of IoT, robotics, sensors, machine-to-machine communication and the like. However, these elements in isolation and without intelligence do nothing to make manufacturing smarter. Robots, for example, can only do as much as they are programmed to do…Sensors can only generate a lot of data, and so on and so forth. What these systems need is an intelligent system that can process the volumes of data generated in the Smart Manufacturing network, and give factories optimized processes, increased capacity utilization and productivity, improved business decisions and the ability to innovate faster.

With the introduction of automation in manufacturing, factories of today are essentially a network of processes that consist of information and task flows. In order to become a smart manufacturing unit, all these processes need to run in a seamless manner without any interruptions. Jagannath Rao, Siemens’ senior vice president for Internet of Things (IoT) and go-to-market strategy says, “AI enables various aspects of the operation to…predict failures to prevent downtime; do optimal production scheduling based on orders in hand; and generate forecasts, inventory, and delivery times…AI also allows manufacturers to apply sophisticated robots for accuracy and quality and, finally, self-organize logistics for seamless deliveries.” AI is able to do so by injecting next-level automation into manufacturing processes including planning, predictive maintenance, and scheduling.

A smart factory is a networked factory system where data from design teams, quality control, production lines, and supply chains are linked together to derive deep and real-time insights. These actionable insights facilitate better decision-making by combining data collected from the connected network and applying AI to it.

AI & Smart Machines

Michael Mendelson, a curriculum developer at the NVIDIA Deep Learning Institute very aptly says, “Without flexible algorithms, computers can only do what we tell them.” In the manufacturing context, it becomes hard to convert tasks that involve perception into rule-based instructions. With AI, manufacturers can solve this conundrum and make factory robots more capable of handling tasks that need perception and make them more capable to interact with and take instructions from their human counterparts.

Whether it is machine vision that helps in precise quality analysis, or training a robot to sense its environment to avoid disruptions or danger or the rise of the collaborative robots (cobots), AI is what is making these concepts useful and usable in the manufacturing ecosystem by given creating a common language that both humans and machines can understand.

AI & the Supply Chain

While AI is the grey matter that makes machines smarter and makes it easier for robots and humans to collaborate with one another, the role of AI in smart manufacturing goes even further. The smart factory is one where everything is optimized and waste is minimal. The manufacturing supply chain in a smart factory is no different.

With AI, manufacturers can perceive demand patterns across geographies, time and socio-economic segments while simultaneously accounting for elements like weather patterns, macroeconomic cycles, political developments etc. which impact the supply chain. This helps in clearer projections of market demands that consequently impacts financial decisions, staffing, raw material sourcing, inventory, energy consumption and equipment maintenance. It also helps in giving manufacturers cues to predict demand and build products to fill the pipeline.

AI & Maintenance

The wide network of connected devices generates a wealth of data in the smart factory. AI utilizes the data from this connected network to enable predictive maintenance. In order to leverage the economic benefits proposed by the smart factory, manufacturing has to up its predictive maintenance capabilities to forecast maintenance events, extend machinery life-cycle, fine-tune production plans, and customize component-wise maintenance schedules.

Predictive Analytics also gets the extra edge with AI by helping in the prevention of unexpected and undiagnosed equipment failures. With AI, if a defect is spotted or if a new part is needed, the software can order the part and the process continues unhampered. AI also enables manufacturers to create ‘Digital Twins’ to constantly monitor performance data and generate predictive analytics to send services on a need basis rather than a time basis. With this capability, factories can handle unplanned downtimes, low productivity, and low yield issues.

AI makes QC Smarter in the Smart Factory

With better monitoring capabilities owing to AI, manufacturers can enable adaptive control of the machines and increase the productivity of the machine. With better system monitoring and planning, manufacturers are able to produce more accurate products on the same machine.

AI also enables automated quality control in the smart manufacturing ecosystem. With an exploding variety of complex products, quality control takes paramount importance. Using AI, manufacturers can perform anomaly detection, triage defects immediately and identify root causes of failure of multiple units in seconds instead of hours. This helps manufacturers resolve problems proactively before they balloon into expensive delays.

Performance enhancement, process optimization, shortened product development times, improved efficiency, and cost control are the main benefits that AI brings to smart manufacturing. Along with this, 24X7 availability and the capacity of machines to self-learn through experience are the other value additions of AI in the smart manufacturing ecosystem. AI is on the way to represent a new way in which machines and humans can interact together for better outcomes by gaining insights into predictive tendencies. With AI supporting operational decisions, critical factors such as safety and security can be optimized to make the shop floor safer for humans.

Clearly, smart manufacturing is a step taken in the direction to achieve manufacturing excellence. However, the potential of these smart initiatives can only turn into tangible outcomes by leveraging AI.