Like many components of digitalization, the implementation of artificial intelligence seems inevitable. How to effectively use and manage the billions of data points generated by intuitive computing power and the machines connected to them is a common concern for manufacturers. Many are unsure how to begin and often attribute their caution in adopting AI to cost, IT requirements, and/or concerns about not being ready for "Industry 4.0."
To remain competitive, it is important for manufacturers to adapt to more data-driven business models. This typically includes personnel restructuring and hardware and software upgrades.
Artificial intelligence, a concept often associated with the future, is now a reality that can be applied to your factory. Here are five ways industrial AI is revolutionizing manufacturing and implementation techniques:
Predictive and preventative maintenance
Some of the largest downtime in production operations may be due to mechanical or electrical failures that cause core mechanical components to go offline. Often, these failures are easily preventable through a recommended preventative maintenance schedule that tracks the machines. Project managers are frequently overlooked or fail to optimize for optimal performance. Leveraging the power of IoT devices, sensors, MES data, and machine learning algorithms, manufacturers can utilize numerous machine data points to predict failures. PM plans can be optimized before failures are predicted to keep machines in top condition and the production floor running smoothly.
Supply chain optimization
Today's supply chains are incredibly complex networks of management, involving thousands of parts and hundreds of locations. Artificial intelligence is becoming an essential tool to enable products to go from production to the customer in a timely manner. Through machine learning algorithms, manufacturers can define optimized supply chain solutions for all their products. Questions like "How many resistors should we order next quarter?" or "What is the best shipping route for product A?" can finally be answered without relying on best guesses or approximations.
Internal inventory management is a significant challenge in itself. Production lines heavily rely on inventory to ensure supply and product production. Each process step requires a certain number of components to operate; once used, they need to be replenished promptly to continue processing. Ensuring that the factory floor has all the necessary inventory is a challenge that AI can help manage. AI can view component quantities, expiration dates, and optimize their distribution throughout the factory.
Production optimization
Process optimization can be a data-intensive task involving countless historical datasets. Determining which process parameters produce the highest product quality is no easy feat. Manufacturing and quality engineers have been conducting extensive experimental design to optimize process parameters, but these designs are often expensive and time-consuming. With the rapid data processing capabilities of artificial intelligence, engineers can find optimized process formulations for different products. Questions like, "What conveyor speed or temperature should I input to achieve the highest yield?" or "What machine should I use to manufacture this high-frequency emerging technology circuit board?" will be continuously learned from all production data points, constantly improving process parameters.
Forecasted output
When discussing the application of artificial intelligence in manufacturing, production forecasting is always a key topic. The return on investment for AI models with high accuracy in forecasting is virtually limitless. Predicting production volume allows for better preparation of the supply chain and inventory management to address future component demand. Knowing whether production is below expectations can alert production managers to increase production time to meet demand. Production forecasting is a complex problem involving large amounts of data, requiring artificial intelligence to solve.
Augmented and Virtual Reality
With the continuous improvement of augmented and virtual reality technologies, more and more large companies are developing equipment for this market, and it's only a matter of time before the manufacturing industry fully adopts them. Virtual reality can help better train product manufacturers to perform assembly or preventative maintenance tasks. Augmented reality technology provides real-time reporting driven by machine learning on the factory floor or in the field, helping to quickly identify defective products and areas for operational improvement. The applications of AR/VR in manufacturing are endless and can play a significant role in solving today's challenges.
Benefits: Energy Management
Artificial intelligence can help in the often-overlooked field of energy management. Most engineers don't have the time to analyze the cost of energy consumption in a factory. AI-driven analysis of energy consumption in production operations can significantly reduce operating costs. Furthermore, reduced costs can allocate more resources to process improvement, leading to higher output and quality.
What if you had a system that could automatically detect production problems in real time before they occur?
The benefits will include predictive maintenance, inventory and product outlier detection, taking operational excellence to a new level in an accessible and intuitive way.
This will change your competitive advantage. Yes. Data is the new bacon, and artificial intelligence is taking it to new heights.
