A Practical Guide to Using AI for Data-Driven Plant Floor Transformation

Quick diagnostic sprints deliver measurable results in weeks, not years, helping manufacturers prove AI value before committing significant resources.

Matt Holman

Jan. 5, 2026

4 min read

Key Highlights

Whether you're exploring data for the first time or struggling to see returns from existing MES and analytics platforms, targeted diagnostics reveal where your process actually needs improvement.
Manufacturers achieved tangible improvements like optimized startup processes and reduced filling variability by using machine learning to identify inefficiencies in historical data.
DataOps and exploratory data analysis provide concrete insights that align team decisions with actual process performance, preventing common pitfalls of scope creep in transformation programs.

Ever had your boss walk into your office and say, “Hey, I heard we should use AI on the plant floor. Make it happen”? Many teams have been there.

Some are just beginning to explore what data can do and feel like they’ve been handed a map without any sense of direction. Others have already invested in MES, analytics or data platforms and still wonder why real returns are so hard to see.

Seeing this situation with many of our manufacturing clients, we developed a way to help organizations move through early uncertainty with small, focused steps rather than large, long-term commitments. The idea is to understand the process, look at the data and figure out what can actually make a difference before anything major is put in motion.

The goal is to reduce risk and give everyone clearer insight into what’s possible.

Work here begins with a short discovery phase. We spend time with the team, get familiar with operations and look at what data is available. After that, we run a diagnostic using exploratory data analysis and machine learning. This helps confirm where inefficiencies may exist and whether assumptions about the process line up with the data.

The analysis highlighted which filler heads were contributing most to the variability and even indicated where mechanical failure might occur.

Those early findings often shape the direction of any later effort and give teams evidence to base their decisions on. And those first steps tend to matter more than people expect. Many digital transformation programs struggle because they grow in scope too fast, long before any value is proven.

Starting small allows results to surface quickly. With our process, those early insights usually come in a matter of weeks, giving teams something concrete to react to rather than relying on broad projections or guesses.

A good example of how this works in real life comes from a client that was taking its first steps with DataOps. For those unfamiliar with this term, DataOps involves contextualizing and standardizing industrial data in a process-oriented perspective to improve quality, speed and collaboration while promoting a culture of continuous improvement in the area of data analytics. In this case, the client wanted to improve their startup process, so we dug into their historical data, reconstructed hundreds of setup runs and used machine learning to pinpoint exactly where inefficiencies were occurring. We found that certain operators achieved stable conditions much faster than others, and we used those “golden signatures” to develop an automated sequencing algorithm.

That algorithm now detects when stability is reached and shifts control at the right time. The outcome is less waste, more consistency and a clearer picture of how the client’s process could be improved. The full effort took less than three weeks.

Another client, already experienced with data, needed help understanding variability in its filling operations. We normalized the company’s data, built predictive models and used the results to outline a practical maintenance and optimization plan. The analysis highlighted which filler heads were contributing most to the variability and even indicated where mechanical failure might occur. The company finished with benchmarks for improvement and a clear path forward within a couple of weeks.

Whether you’re hearing “we need AI” for the first time or already working through the realities of incorporating AI with your established systems, taking these small, evidence-based steps tend to help. They bring clarity, keep the focus on what’s real and support decisions with data rather than assumptions. Actemium works alongside teams at whatever stage they’re in, helping make the data journey more practical and easier to navigate.

Matt Holman is director of consulting and support operations at Actemium Avanceon, a certified member of the Control System Integrators Association (CSIA). For more information about Avanceon, visit its profile on the CSIA Industrial Automation Exchange.