04.17.25 5 min read

#054: AI in Manufacturing Operations: Challenges and Opportunities

Discover the key challenges and opportunities of AI in manufacturing operations. See how it can enhance productivity, reduce downtime, and drive success.

This newsletter is a summary of this podcast on ContinuousTV.
Host: Anand Natarajan, Director of AI/ML Solutions Delivery, xLM Continuous Intelligence
Guest: Osvaldo Santiago, Operations Manager, Johnson & Johnson

Ozzy Santiago is an experienced operations leader with over 20 years in manufacturing and supply chain management across the consumer products and medical device industries. Currently Operations Manager at Johnson & Johnson Vision, he has led high-impact teams and cross-functional initiatives in regulatory compliance, lean manufacturing, and new product introduction. His prior leadership roles at DePuy Synthes, Procter & Gamble, and Gillette focused on driving operational excellence, team development, and cost optimization. Ozzy holds an MBA in Management from Cambridge College.

1.0 The Pain Points are Real—and Expensive

Unscheduled downtime remains a significant challenge in production. The costs extend beyond lost output, impacting personnel, materials, and the long-term health of equipment. Inadequate troubleshooting skills or unclear escalation procedures often make repairs—especially unexpected ones—the most expensive type of downtime.

Preventive maintenance can also be misaligned: it may be overly strict, inspecting parts unnecessarily, or too lenient, failing to detect critical issues until it's too late. Without a predictive framework, businesses either over-maintain or under-protect vital assets.

2.0. Why Data Is the Game-Changer

Today, data collection is essential for modern operations. However, simply gathering data is not enough. Many technicians receive raw, unanalyzed data that lacks real-time value. True change begins when data becomes actionable.

Artificial intelligence (AI) solutions, particularly industrial-scale natural language interfaces, can help interpret machine performance and provide immediate answers to questions like "Why is this module running slowly?" or "Is this part optimized?" Teams can then make informed decisions on the spot rather than relying on trial-and-error or intuition.

3.0. Predictive Maintenance with AI

Predictive maintenance is one of the most promising applications of AI. AI-powered systems can automatically detect early signs of binding, overheating, or degradation—well before these issues lead to failure—by analyzing machine signatures and usage patterns. Imagine receiving real-time alerts that a component may need replacement within the next 72 hours. This allows technicians to plan, source replacement parts, and prevent disruptions.

AI-Driven Predictive Maintenance: From Breakdowns to Breakthroughs

This approach reduces downtime and the hidden costs of reactive repairs, such as waiting for specialists, off-site component collection, and unscheduled calibration.

4.0. AI-Driven Quality Control

AI significantly impacts quality control beyond maintenance. AI systems can autonomously adjust factors such as temperature, pressure, or time by evaluating data from inline inspection devices. This leads to fewer errors, less waste, and greater assurance that every product meets strict requirements.

AI-Guided Perfection: Real-Time Quality Control in Action

AI-powered quality control ensures that every unit meets rigorous standards—especially important in regulated manufacturing environments like medical devices or pharmaceuticals.

5.0. What’s Holding AI Back?

Despite its potential, several obstacles hinder AI adoption on the shop floor. One major barrier is awareness—many manufacturers view AI as overhyped or futuristic. Others perceive it as too complex or costly for regulated environments.

However, AI does not replace the human touch. Instead, it acts as a co-bot, an intelligent companion that enhances human expertise. By streamlining complex processes like real-time analysis, it allows frontline employees to focus on high-value tasks.

6.0. The Road Ahead: AI in Manufacturing

In manufacturing, waiting for equipment to fail is no longer a viable strategy. AI-driven predictive measures offer an intelligent alternative that shifts maintenance from reactive to proactive. By improving quality control, predicting equipment failures, and streamlining workflows.