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- #054: AI in Manufacturing Ops: Challenges and Opportunities
#054: AI in Manufacturing Ops: Challenges and Opportunities
The manufacturing floor faces increasing pressure to uphold quality standards, enhance efficiency, and minimize downtime in today's fast-paced production environment. Yet, many manufacturers still rely on outdated, reactive maintenance techniques—waiting for machinery to fail before taking action.What if issues could be anticipated and avoided before they arise?This is where artificial intelligence is beginning to transform the industry.
Nagesh Nama & Kashyap Joshi
April 17, 2025
Table of Contents
This newsletter is a summary of this podcast on ContinuousTV.
Host: Anand Natarajan, Director, 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 Data Is the Differentiator
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 Proactive > Reactive
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.
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 Boosting Quality from the Inside Out
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.
5.0 What’s Holding Us 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
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.
”Innovation isn’t just about having the right tools—it's about using them at the right time. With AI, that time is now”
AI has the potential to transform uncertainty into precision and the shop floor into a well-oiled, insight-driven ecosystem.
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