THE USE OF AI IN MANUFACTURING
Submitted by christian on Fri, 09/02/2022 - 13:51
- Artificial intelligence in logistics: A constant challenge with manufacturing is the losses from overstocking or under-stocking inventories. Overstocking often leads to wastage and lower margins. Under-stocking can translate into losses, revenue and customers. Using technologies like 3 D printing, manufacturers can produce serial parts in-house or at near-shore facilities, reducing their reliance on far-off, low cost manufacturing locations and managing their inventories more efficiently. Manufacturers can also use robots to replace human couriers and ensure uninterrupted last-mole deliveries.
- AI based robots: AI robots tap into machine learning algorithms to automate decision-making and repetitive tasks at manufacturing plants. Since these algorithms are self-learning, they keep improving to handle their assigned processes better. Additionally, AI robots don’t need breaks and aren’t as susceptible to errors as humans. So, manufacturers can easily scale their production capacity. Robots can also do the heavy lifting on production floors while humans take charge of more delicate tasks. This improves workplace safety and overall production performance.
- Artificial intelligence in supply chain management: AI-enabled systems can help manufacturers assess various scenarios (in terms of time, cost, revenue) to improve last-mile deliveries. AI can predict optimal delivery routes, track driver performance in real-time, and assess weather and traffic reports besides historical data to forecast future delivery times accurately. AI can also give manufacturers greater control over their supply chains from capacity planning to inventory tracking and management. They can set up a real-time and predictive supplier assessment and monitoring model to get notified the moment there’s a supplier failure and assess the extent of supply chain disruption immediately.
- AI autonomous vehicles: Autonomous vehicles on the production floor can automate everything from assembly lines to conveyor belts. Self-driving cars and ships can optimize deliveries, operate 24/7, and speed up the overall delivery process. The demand for autonomous vehicles is rising steadily and is expected to make up 10-15% of global car sales by 2030. Connected vehicles equipped with sensors can also track information about traffic jams, road conditions, accidents, and more in real-time to optimize delivery routes, reduce accidents, and even alert the authorities in case of emergencies. This improves delivery efficiency and road safety.
- AI for factory automation: With AI, manufacturers can reduce labor costs significantly while improving overall productivity and efficiency at their plants.
- AI for IT operations: AI for IT operations, also known as AIOps, is crucial to optimize IT operations., AIOps combines big data and machine learning to automate IT operations processes. The biggest use case for AIOps is automating big data management. Other use cases include event correlation and analysis, performance analysis, anomaly detection, causality determination, and IT service management.
- AI in design and manufacturing: AI-enabled software can help create several optimized designs for a single product. The software, also known as generative design software, requires engineers to provide certain input parameters. Using these parameters, the algorithm can generate various design permutations. The software lets engineers can test various designs against a wide range of manufacturing scenarios and conditions to pick the best possible outcome.
- Artificial intelligence and IoT: IoT refers to smart, connected devices equipped with sensors that generate large volumes of operational data in real-time. In manufacturing, this is known as IIoT or the Industrial Internet of Things. Together with AI, IIoT can help manufacturing processes achieve greater levels of precision and productivity.
- AI in warehouse management: AI can automate several aspects of warehouse operations. Since they collect data in real-time, manufacturers can monitor their warehouses continuously and plan their logistics better. Demand forecasting can further help manufacturers take action to stock up their warehouses in advance and keep up with the customer demand without enormous transportation costs. Robots in the warehouses can track, lift, move, and sort items, leaving the more strategic tasks to the humans and reducing workplace injuries. Automated quality control and inventorying can reduce warehouse management costs, improve productivity, and require a smaller labor force. As a result, manufacturers can increase their sales and profit margins.
- AI process automation: AI-powered process mining tools can identify and eliminate bottlenecks in production processes automatically. These tools also allow manufacturers to compare factory performance across several regions. This lets them standardize and streamline workflows to build better manufacturing processes. Another use case is RPA (robotic process automation) where robots perform repeatable tasks on the shop floor independently. Human intervention is required only when the robots are faced with exceptions or anomalies. Similarly, robots can use computer vision to screen and inspect processes without any human intervention.
- AI for predictive maintenance: The greatest value from AI in manufacturing is because of predictive maintenance.
- AI-based product development: AI-based product development can help manufacturers create several simulations and test them using AR (augmented reality) as well as VR (virtual reality) before starting production.
- AI-based connected factory: Connected factories or smart factories built using sensors and the cloud are the way forward for the manufacturing industry.
- AI-based visual inspections and quality control: AI-powered defect detection taps into computer vision, which uses high-resolution cameras to monitor every aspect of the production process. Such a system can flag defects that the human eye might miss and trigger correcting measures automatically. This helps reduce product recalls and cut down on wastage. Detecting anomalies like toxic gas emissions on the fly also helps prevent workplace hazards and enhances worker safety at factories. Another AI-based system is AR overlays, which compare the actual assembly parts with those provided by suppliers to spot any quality deviations. AR can also help with remote training and support so that technicians from any location can connect with those at a facility and guide them.
- AI for purchasing price variance: For manufacturers, any variance in the cost of raw materials can affect their margins. Estimating raw material costs accurately and choosing the right vendors is a major challenge.
- AI order management: Order management processes must be agile, cost-effective, and able to adapt as per fluctuations in the market, demand, consumer expectations, or manufacturing strategies.
- AI for cybersecurity: Manufacturers suffer the most from cyberattacks, as even a brief shutdown of the assembly line can prove costly. As the number of IoT devices increase, the threats will continue to grow exponentially. Smart factories are particularly susceptible to cyberattacks.AI-driven cybersecurity systems and risk detection mechanisms can help secure production facilities and mitigate threats. Using self-learning AI, manufacturers can spot attacks across cloud services and IoT devices and interrupt them in seconds, with surgical precision. The system can also alert the right teams to act immediately to prevent any further damage. Using sandboxing, code signing and other such security measures can help combat cyber threats to IIoT technologies.
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