AI for smart manufacturing
Manufacturing is changing fast. With artificial intelligence (AI) advancing in leaps and bounds, small and medium-sized enterprises (SMEs) have access to a range of new technologies and capabilities—many of which are more affordable than most people realize.
Indeed, AI can be used very effectively in manufacturing due to several inherent characteristics of the industry and the nature of AI technology. “AI has proven useful in a number of industries, but it is particularly beneficial in manufacturing because data can be easily collected and tasks tend to be structured and repetitive,” says Benedicte Vaugeois, Business Advisor, BDC Advisory Services.
By integrating AI in their manufacturing processes, SMEs can reduce costs, improve quality and boost productivity. For example, one common application is predictive maintenance, where AI anticipates equipment failures to minimize downtime. Another one is quality control, where AI-driven visual inspection systems can detect defects.
Here are the most common use cases for AI in manufacturing.
Top use cases for AI in manufacturing
AI solutions for quality control
AI can help you improve your quality control processes, leading to better product quality and increased customer satisfaction. “Effective quality control not only reduces material waste, but also time waste, by minimizing the need for rework,” says Vaugeois. “It can lead to significant gains in efficiency.”
Here are three use cases for AI in quality control.
Automated optical inspection (AOI)
AI-powered AOI systems can inspect components or finished products accurately and quickly.
These systems use computer vision to detect anomalies such as scratches, dents, or incorrect labeling on items moving along production lines.
Real-time temperature monitoring
AI-powered systems can track and analyze the temperature and humidity levels of a product on a production line.
In the pharmaceutical industry, for example, such systems ensure drugs are kept within optimal temperature ranges, promptly alerting operators to any deviations. This capability enhances uniformity across production lots, ensuring safety and consistency throughout.
3D print monitoring
In additive manufacturing, AI can monitor the printing process, detect defects like voids or warping, and ensure dimensional accuracy of printed parts.
Systems using AI can also track crucial parameters, including hot-ends and print bed temperatures, print speed, and material flow rates.
Using advanced algorithms, [AI] applications can predict when a piece of equipment is most likely to fail while also recommending optimal maintenance schedules.
Benedicte Vaugeois
Business Advisor, BDC Advisory Services
AI solutions for predictive maintenance
Equipment failure is costly. By helping to prevent it, AI applications can cut downtime, reduce maintenance expenses and minimize customer delays.
Here are three use cases for AI in predictive maintenance.
Production line remote monitoring
AI-powered systems use real-time and historical data to monitor your production line as it is operating.
Using advanced algorithms, these applications can predict when a piece of equipment is most likely to fail while also recommending optimal maintenance schedules,” explains Vaugeois.
Hydraulic system monitoring
Several AI-powered applications can monitor hydraulic systems, such as those used in lifts, brakes, shock absorbers, jacks, heavy machinery, or even aircraft.
A number of key parameters can be tracked, including oil temperature, vibration and flow rate. Machine learning algorithms can use these data inputs to predict potential equipment failures before they actually occur, enabling timely maintenance interventions for optimal performance.
Building systems monitoring
Predictive maintenance can help maintain comfort, safety and energy efficiency in buildings.
For instance, advanced algorithms can analyze real-time data and historical data to suggest optimal maintenance schedules for heating, ventilation and air conditioning (HVAC) systems, among others.
AI algorithms can now analyze factors such as lead times, supplier reliability and demand variability to optimize inventory levels.
Benedicte Vaugeois
Business Advisor, BDC Advisory Services
AI solutions for supply chain and inventory management
Supply chains are complex, and a poorly managed inventory can be costly for your company. Fortunately, supply chain and inventory management is a business function where AI can help businesses gain productivity and prevent operational disruptions.
Here are three use cases for AI in supply chain and inventory management.
Demand forecasting
AI-powered apps can analyze historical sales data, market trends and external factors such as weather or economic conditions to forecast demand more accurately.
This helps you improve your inventory planning and reduces the risk of stockouts or overstocking.
Inventory optimization
AI algorithms can now analyze factors such as lead times, supplier reliability and demand variability to optimize inventory levels,” says Vaugeois. “This helps streamline procurement and reduce costs by ensuring you have the right amount of stock at the right time and in the right locations to meet customer demand.”
Route optimization
If you deliver your products yourself, AI algorithms can optimize your transportation routes by analyzing traffic conditions, fuel costs and delivery schedules.
Doing so not only reduces transportation costs, but also improves delivery times and minimizes environmental impact.
AI solutions for process optimization
AI opens the door to a whole new world of efficiency gains in manufacturing. By offering several ways to optimize processes, systems based on advanced algorithms can help you significantly improve efficiency, quality and flexibility.
Here are three use cases for AI in process optimization.
Production planning and scheduling
AI algorithms can optimize your production schedule based on factors such as demand forecasts, labour and raw material availability, and machine capacities. This maximizes throughput and minimizes production lead times.
Energy management
AI can help your manufacturing business optimize energy consumption by analyzing real-time data from sensors and adjusting equipment operations accordingly. This not only enables you to reduce your energy costs, but can also aid in achieving your sustainability goals.
Product customization, design and development
AI algorithms can analyze customer feedback, preferences and purchasing history, market trends, demographic information and historical data to optimize product designs and innovations. This helps manufacturers develop products that better meet customer needs and preferences.
What are the advantages and disadvantages of AI in manufacturing?
AI can benefit SMEs in a number of ways. Here are AI's main advantages specific to the manufacturing industry.
- Reduced downtime: predictive maintenance prevents unexpected equipment failure
- Increased safety: well-maintained equipment is safer to operate
- Cost savings: AI-driven optimization of processes, energy usage and supply chain management reduces operational costs
- Improved quality: real-time defect detection ensures products meet standards and quality is consistent
While AI offers significant benefits, it also presents challenges, which vary by industry. In manufacturing, the following disadvantages are particularly noteworthy.
- Worker concerns: Employees may be apprehensive and think that AI systems are there to replace them. For this reason, companies need to be transparent about their ultimate goals, emphasizing how AI can augment employees’ roles rather than replace them.
- Costs: While AI systems can help save money in the long run, implementing a new system often entails significant upfront costs.
- Complexity: Integrating AI with existing manufacturing systems and processes can be complex. Consequently, you might need to hire new, highly-skilled employees, call in consultants or train your current team.
How can SMEs start using AI tools in manufacturing?
Manufacturing SMEs should start small. A first project should be easy to implement since a failure can impact morale negatively and compromise future projects. To begin using AI systems, manufacturing companies should follow these steps:
1. Assess needs and goals
Identify areas where AI can provide value, such as predictive maintenance, quality control or process optimization. Define clear goals and objectives for implementing AI. Target quick wins.
2. Monitor your competitors
Determine how they are using AI. This will help you determine which tools and applications are the most likely to work in your industry.
3. Choose a suitable solution and start with a pilot project
Research and select an AI tool that will align with your manufacturing requirements and budget. Consider factors such as ease of integration, scalability and vendor support. Start small with a pilot project and develop afterwards.
Types of AI solutions
Out-of-the box
Pre-built
Custom
4. Ensure data readiness
Make sure you have already collected data related to the manufacturing processes you would like to improve. Clean and prepare your data to train AI models effectively.
5. Get help
First projects are never easy—partner with consultants, technology vendors, or research institutions to leverage their expertise.
6. Monitor and evaluate
Monitor the performance and impact of your project on your operations—track performance indicators. Collect feedback from employees and stakeholders to refine processes and maximize benefits.
Next step
Establish a solid framework for AI by downloading BDC’s free guide, Harnessing the Power of Industrial Automation and Robotics