This is Part Two of a two-part series on the benefits and best practices of applying AI to manufacturing. This blog outlines how to leverage AI in manufacturing. Read Part One here.
AI is reshaping every stage of the manufacturing process—from design and prototyping to sourcing and assembly. AI in manufacturing can accelerate product launches, enhance quality, improve supply chains, lower costs, and boost customer satisfaction. Embracing AI-driven advancements is critical if electronics manufacturers want to stay relevant in this rapidly evolving sector.
But how do executives transition beyond operational oversight and become strategic AI leaders? Below are nine best practices that will guide you through this important transformation.
Start with a Strategic Assessment/Plan
Through real-time collection and analysis of data, AI systems can provide manufacturers actionable insights, enabling rapid identification and resolution of issues before they escalate.
Before investing in technology, it’s critical to first step back and develop a plan. Identify where AI can deliver the most business value for your company. Are you seeking improvement in quality control? Are you struggling to accurately track your inventory? There are AI solutions available for almost any business process, so it’s important to focus your efforts and financial resources on the initiatives that best align with your company goals. For example, Intel’s strategic planning involves creating production optimization targets, which include minimizing yield loss and shortening time to market. Having a strategic plan allows Intel to prioritize its AI initiatives based on their potential to deliver measurable progress on these key performance indicators (KPIs).
Integrate AI in Manufacturing from the Design Phase
While AI in manufacturing can greatly improve the manufacture of electronics, you’re missing out if you don’t integrate AI from the very beginning, at the design stage. AI tools can be used for simulation, design rule checks, and generative design—a design approach where AI systems optimize design solutions based on your specific manufacturing requirements, constraints, and goals. Generative design can be an essential part of design for manufacturability (DFM), helping a company ensure manufacturability and product performance before making a substantial investment in a new product offering.
Using powerful algorithms, AI systems generate a wide range of design alternatives and help engineers identify the most efficient, cost-effective, and high-performance options. Once a design has been chosen, rapid prototyping can then help engineers efficiently test options while keeping costs under control.
Prioritize Smart Implementation
Before investing in full-scale AI integration, test the technology’s usefulness for your business by running a pilot program on a small, high-impact project. During this pilot project, make sure the AI tools you’ve chosen can deliver the performance you need. One way to ensure quality, for example, is to use AI models that provide clear explanations for their decisions. With transparent data, operators and engineers will more easily understand—and trust—AI-driven recommendations.
A smart implementation will also address cybersecurity and intellectual property protection. This protection has become increasingly difficult as IT teams face the formidable task of protecting proprietary data that’s stored across multiple devices: PCs, tablets, phones, Internet of Things (IoT) devices, the cloud, etc. Each endpoint—i.e., any device that connects to a computer network—is vulnerable to attack by hackers. In fact, IBM reports that as much as 90% of successful cyberattacks originate at endpoint devices.
- Regularly update and monitor your systems: Stay on top of security by using intrusion detection and prevention systems (IDPS) to monitor your network.
- Integrate Strong Access Controls and Segment Your Network: Access to critical systems should always be done with multi-factor authentication and role-based permissions. To prevent possible breaches, segment your information technology (IT) and operational technology (OT).
- Develop an Incident Response Plan: Despite your best efforts, incidents can happen. Train your employees on best practices, including how to respond and recover in the event of a breach.
Use AI-Driven Automation on the Factory Floor
AI-driven automation can provide efficiency and cost savings on the factory floor. For example, collaborative robots (cobots) can work alongside humans to assemble, solder, and inspect a PCB board. Furthermore, machine vision can provide real-time quality assurance and process control.
Foxconn, the world’s largest electronics manufacturer, has been experimenting with AI-powered robots. By embracing this innovation, they hope to improve efficiency, enhance product quality, and address labor shortages. Some of Foxconn’s AI-powered robots include advanced humanoid models developed with partners like UBTech Robotics and eBots. Using AI-driven decision-making systems, computer vision, and enhanced dexterity, these robots are able to precisely place tiny chips on a circuit board and perform delicate soldering tasks, among other intricate maneuvers. As an added bonus, the robots use real-time feedback to adapt to new production requirements and minimize human error.
Integrate Humans with Automation
Despite the rapid advance of AI in manufacturing, there are still some tasks that humans do better than existing technology. For this reason, it’s important to train employees to work alongside AI-enhanced automation technologies. And it’s especially important to maintain human oversight for critical operations and decisions, so that humans are ready to intervene when AI outputs are uncertain or require expert judgment.
One best practice is to establish a cross-functional AI steering committee to oversee your AI implementation. Be sure to involve your information technology (IT), operational technology (OT), and data science teams from the very beginning. By encouraging knowledge sharing between domain experts and AI specialists, you can craft specific AI solutions for your manufacturing processes. With a cross-functional team, you’ll be better able to collaborate and proactively address any challenges that arise.
Invest in Predictive Maintenance
It’s good to perform regular maintenance at scheduled intervals, regardless of a machine’s condition. But it’s even better to employ AI in manufacturing to receive real-time data from sensors and perform maintenance only when equipment actually needs it. In other words, while preventive maintenance is good, predictive maintenance is even better.
Electronics manufacturers can improve their predictive maintenance by focusing first on critical equipment (the most crucial or failure-prone machines); by collecting and analyzing data in real time; and by turning predictive insights into timely actions and strategies for continuous improvement.
Apple Computer is an example of a manufacturer that has fully embraced predictive maintenance and benefitted from it. Their equipment uses sensors to measure vital operational data like vibration, temperature, and pressure. The data is then analyzed by AI to spot any unusual changes that signal potential problems. Being able to predict when equipment is heading towards failure allows Apple to reduce unexpected downtime, cut repair costs, and keep manufacturing processes running smoothly.
Track the Metrics of AI in Manufacturing
To evaluate how AI in manufacturing is affecting the quality, efficiency, and cost effectiveness of your electronics manufacturing, you’ll want to track several important metrics. Be sure to get accurate baseline data so that you can compare pre- and post-AI implementation metrics. Here are a few ways that AI-enhanced systems should improve your metrics.
- Defect rate: This metric measures the percentage of defective units produced. AI vision systems can be used at earlier stages of production to catch anomalies like misalignments or microscopic cracks before the product makes it to the end of the line.
- First Pass Yield (FPY): As its name implies, this metric is for measuring the percentage of products that pass inspection on first review. By catching defects early in the production process, AI systems allow electronics manufacturers to achieve a higher FPY, leading to less rework.
- Defects Per Million Opportunities (DPMO): This metric quantifies the number of defects per one million chances for a defect to occur. By analyzing DPMO, manufacturers can pinpoint specific failure modes and use AI systems to reduce defect opportunities through enhanced process control and precision.
- Overall Equipment Effectiveness (OEE): OEE measures equipment utilization and efficiency. By enabling predictive maintenance and improving quality assurance, AI systems minimize equipment breakdowns and increase efficiency.
- Cycle Time: This metric tracks the production time per unit. Because AI makes it easier for engineers to find optimal workflows, it helps reduce the time required to manufacture a unit of product.
- Energy Consumption: The manufacture of electronics is an energy-intensive process. By using AI systems to analyze energy consumption, you can detect and rectify inefficiencies that waste energy.
- Cost Savings: Investing in AI systems can be costly, but using AI in manufacturing should reduce both labor costs and material expenses.
By keeping track of these important metrics, you’ll know just how much you’re benefiting from your AI implementation, which can inform your decision on whether and when to expand your use of AI in manufacturing.
Optimize the Supply Chain with AI Analytics
AI doesn’t just optimize design and production. Its analytic capabilities are ideal in other areas that touch on manufacturing but aren’t necessarily part of production. For example, you can use AI to analyze market trends and help you determine which new product to develop. And AI-enhanced data analysis improves demand forecasting for your current products. Combined with IoT devices, AI lets you monitor stock levels in real time, so you can accurately predict potential shortages or surpluses, minimize waste, and prevent costly stockouts—significantly improving inventory management.
You can also mitigate your supply chain risk by continuously monitoring data from your suppliers, and so anticipate problems before they occur. AI systems can even monitor thousands of global news sources, enabling you to quickly identify supply chain risks as they arise and take pre-emptive action, like re-routing a delivery before it is delayed.
Harnessing AI for Continuous Improvement
Through real-time collection and analysis of data, AI systems can provide manufacturers with actionable insights, enabling rapid identification and resolution of issues before they escalate. AI-augmented workflows enable employees to better plan, execute, monitor, and act using real-time data. Furthermore, this real-time data allows AI models to be regularly updated and retrained, leading to greater accuracy and easier adaptation to process changes, all of which supports and promotes a culture of continuous improvement.
For these reasons, electronics manufacturers are finding that AI is no longer optional—it’s a strategic imperative. To thrive, you must leverage AI across all your operations, from design to production to packaging and fulfillment. By adopting the best practices outlined here, operational leaders can position their organizations for long-term, AI-powered success.