Driving the Future of Manufacturing With AI and Digital Transformation

Andy Mears is a seasoned professional specializing in digital transformation, manufacturing, and quality engineering. With extensive experience in aerospace, defense, oil and gas, medical devices, and commercial products, Andy has led global teams in product development, software product management, and technical project leadership. At Lockheed Martin, he drives Intelligent Factory initiatives, integrates advanced manufacturing technologies, and achieves significant cost savings while fostering cross-functional collaboration across diverse operational areas.

Roles and Responsibilities

As the Intelligent Factory Integration Lead, I oversee various domains within Production Operations. My work extends modelbased enterprise efforts, focusing on integrating engineering innovations into production, such as model-based work instructions. Additionally, I lead Industrial Internet of Things (IIoT) initiatives, collaborating with IT to establish cyber-secure network infrastructure, enabling machine connectivity, data pipelines, and advanced analytics. My team, including full-stack engineers, develops applications and insights that enhance operational efficiencies on the factory floor.

The Growing Focus on Data Analytics and AI in Manufacturing

Data analytics has become one of the most critical trends in manufacturing, and we have undergone a maturity process in this space. Over the past six years, my role has continuously evolved, particularly within the IIoT domain. Initially, our focus was on understanding production capabilities and assets—identifying what was available and how we could leverage that information. The first challenge was building the network infrastructure necessary to support connectivity, followed by integrating and enabling data streams from a diverse range of production assets. Given the complexity and variety of manufacturing equipment across our facilities, achieving full connectivity was a major effort.

However, since we have moved beyond simply connecting machines, connectivity is no longer the primary challenge—it has become more of a standardized process. Instead, our main focus has shifted toward advanced data analytics. Early on, we relied heavily on dashboards and visualizations, but we quickly realized that while useful, they weren’t necessarily driving meaningful change. Traditional dashboards help monitor performance, but they don’t always provide the deeper, actionable insights needed to improve efficiency at scale.

This realization has led us to focus more on predictive analytics and AI-driven insights. We are now actively exploring AI applications to move beyond surface-level data, enabling us to recognize patterns, predict outcomes, and optimize processes in ways we couldn’t before. By leveraging AI, we aim to analyze historical data, anticipate issues, identify inefficiencies, and make proactive decisions that enhance our operations.

“The industry is evolving at an unprecedented pace, and success often depends on the ability to navigate uncertainty, staying curious, and proactively adapting to new challenges and opportunities”

Data analytics and AI-driven decision-making are at the forefront of our transformation efforts. The ability to extract deeper, more meaningful insights from data is what will truly move the needle in manufacturing efficiency and innovation.

AI’s Expanding Role in Manufacturing

AI is opening up tremendous possibilities in manufacturing, and we are actively exploring where it can create the most impact. One promising application is leveraging generative AI and language models to improve equipment diagnostics and troubleshooting. For instance, when a machine experiences an anomaly or failure, AI-powered tools can quickly mine historical service data, OEM documentation, and maintenance manuals, helping facilities teams identify and resolve issues faster. This reduces downtime and improves overall efficiency.

Additionally, AI plays a key role in our 3D work instruction initiatives, where retrieval-augmented generation (RAG) AI enhances training for manufacturing engineers. By consolidating data from multiple sources, we are accelerating knowledge transfer and onboarding, enabling our workforce to adapt more quickly to new systems.

We are also investigating how generative AI can be integrated with real-time production data to provide deeper insights into factory operations. With nine primary manufacturing sites at Lockheed Martin Missiles and Fire Control, our goal is to equip senior leadership with real-time, actionable data that allows for quicker decision-making and operational adjustments. AI-driven analytics can help identify trends, inefficiencies, and potential process optimizations, ensuring that we remain proactive rather than reactive. While we are still in the early stages of some of these applications, AI is clearly becoming a powerful tool in enhancing productivity, streamlining workflows, and driving intelligent decision-making across our manufacturing ecosystem.

Manufacturing’s Industry Trajectory

The manufacturing industry is undergoing rapid transformation, and while many of the advancements we discuss—AI, big data, and automation—are still evolving, they remain central to where the industry is headed. The biggest trend continues to be how to leverage AI most effectively. The focus is shifting from merely collecting and visualizing data to truly understanding and applying it through AI-driven insights.

As manufacturing becomes more connected and data-centric, companies will increasingly rely on machine learning, predictive analytics, and intelligent automation to drive efficiency and innovation. This trajectory will continue shaping how factories operate, making digital fluency and adaptability more essential than ever.

Advice for Young Professionals

For young professionals entering the field, my biggest piece of advice is to embrace adaptability and continuous learning. A great example can be seen within our own teams that are leading the Operations transformation. These teams not only have earlycareer individuals with degrees in various engineering disciplines but in many cases, these folks have also upskilled themselves with Python coding, advanced data analytics, robotics, and control systems. These additional skills make our advanced manufacturing technologies and Intelligent Factory teams much more agile and effective. I think these examples highlight the importance of diversifying your skill set beyond a formal education.

Today, there are endless resources available outside university programs—self-learning is crucial. Additionally, I always remind my teams, especially younger professionals, to get comfortable with ambiguity. The industry is evolving at an unprecedented pace, and success often depends on the ability to navigate uncertainty, stay curious, and proactively adapt to new challenges and opportunities.