AI in manufacturing: top use cases,
trends & implementation guidelines

By adopting predictive analytics software, manufacturers can streamline logistics operations, such as inventory and delivery management, and prevent supply chain disruptions.

• Demand forecasting
  Analyzing sales, market trends, social media sentiment, and other factors to predict future product demand and adjust production plans accordingly.
• Inventory optimization
  Predicting supply and demand based on component supplier lead times, historical and real-time sales data, and other metrics to optimize inventory levels across warehouses and minimize the risk of overstocking and stockouts.
• Route planning
  Fine-tuning delivery schedules and routes in real time based on factors like traffic, weather, and vehicle breakdowns to reduce shipping costs and lead times.

The implementation of AI technologies like computer vision in smart factories provides industrial robotics systems with superior spatial awareness, making them faster and more accurate.

• Robots
  Fully automating assembly processes (spot welding, riveting, etc.) across the production line with computer vision-guided mechanical arms and other machines to maximize the output.
• Cobots
  Assisting human workers with repetitive tasks like component placement to improve their productivity and mitigate physical workloads while ensuring their safety using data from proximity sensors.

Unlike traditional quality control systems that rely on a limited set of predefined rules to spot product defects, machine learning-powered anomaly detection software can recognize a wider range of potential outliers or even previously unknown anomalies with utmost accuracy.

• Automated visual inspection
  Scanning products on the production line to detect scratches, dents, misalignments, or discolorations and sort out any defective items.
• Root cause analysis
  Investigating product defects from the visual production line inputs to help identify and address potential causes, such as injection molding flash due to a worn mold.

The anomaly detection capabilities of AI systems have already helped multiple manufacturers move from a reactive to a proactive approach to equipment and asset maintenance.

• Condition monitoring
  Analyzing equipment performance and conditions, such as torque, vibration, or acoustic emissions, to detect subtle changes that can be a sign of degradation.
• Failure prediction
  Predicting machinery’s remaining useful life based on degradation patterns and schedule maintenance activities accordingly to prevent breakdowns and consequent downtime.

Digital twins are virtual models of a factory or an entire supply chain network created by collecting real-time data on assets and processes with IoT sensors. Combined with AI-powered analytics, these digital replicas enable manufacturers to simulate and predict future scenarios.

• Production process simulation
  Tracking factory floor operations to identify inefficiencies and simulate the impact of certain changes, such as a different facility layout, on production processes before implementing them.
• Supply chain risk prediction
  Simulating logistics scenarios to assess how demand spikes, supply shortages, or other events can impact your company and partners and thus create effective contingency plans.

With the help of AI-powered business software for HR administration, manufacturers can streamline various workforce management tasks, optimize labor resources, and ensure safer working environments.

• Workforce planning
  Analyzing production plans, workloads and skill requirements for specific tasks, staff performance, and labor supply and demand to help HR departments better optimize hiring initiatives, training programs, and shift schedules.
• Worker safety
  Monitoring the factory floor with computer vision-powered cameras to detect unsafe behavior or potential hazards in real time, such as workers without safety gear, and automatically send alerts to prevent incidents.