Getting real about AI in advanced manufacturing sectors: 3 key steps

After completing these assessments, you should be able to determine whether your plant’s OT network can reliably transmit the right data for AI … and whether your data can enable meaningful analysis.

Infrastructure readiness

Reliable AI performance depends on the ability of sensors, PLCs and other devices to transmit information for analysis and action. These systems need to communicate seamlessly from the plant floor to the application layer, without manual intervention.

A connected facility’s process and packaging lines, quality inspection stations, sensors, utilities and various databases send production and performance information to a central location in real-time so it can support data analytics. A temperature spike, dropped cycle or change in change in machine status can be automatically logged, timestamped and associated with the right machine and run.

But accuracy suffers when workers have to manually input this information into a system at the end of a shift. For instance, relying on operators to enter machine output data into spreadsheets can’t guarantee data accuracy or timeliness. If AI were to use this data to forecast product quality, predict maintenance needs or optimize batch runs, the results would be unreliable.

To gain a realistic picture of the current state, you should assess every system on the plant floor. Map out which lines, machines and control systems are connected to a network (meaning they can send and receive data automatically) and which rely on human entry.

Identify any gaps that exist. For example, perhaps your new packaging lines have direct PLC connections, but your older mixing tanks are isolated and require manual log sheets and shift-end reporting. In this scenario, you can optimize throughput and spot issues quickly on automated lines, but you have to wait for manual reports before you can determine whether there’s a problem with a batch mix. The result is blind spots that disrupt operations and delay responses.

Data readiness

AI runs on data. Information powers every analysis and prediction. Your infrastructure assessment determines how effectively your network can connect and integrate every asset to ensure automated, real-time data collection.

AI-ready data must be complete, current, consistent, centralized and accessible.

When data quality falls short, your AI effort is at risk. If AI is fed inaccurate or incomplete data, it can’t deliver reliable recommendations, no matter how advanced the algorithms are. Missing information, mismatched formats or fragmented data that lives on local machines can undermine AI’s results. 

To gain a realistic picture of the current state, you should assess every system on the plant floor. Map out which lines, machines and control systems are connected to a network (meaning they can send and receive data automatically) and which rely on human entry.

Identify any gaps that exist. For example, perhaps your new packaging lines have direct PLC connections, but your older mixing tanks are isolated and require manual log sheets and shift-end reporting. In this scenario, you can optimize throughput and spot issues quickly on automated lines, but you have to wait for manual reports before you can determine whether there’s a problem with a batch mix. The result is blind spots that disrupt operations and delay responses.

Goal identification

AI’s effectiveness depends on your ability to clearly define the purpose behind deployment. Start by establishing what your organization or plant wants AI to accomplish. Do you want it to reduce downtime? Improve product quality? Optimize energy use? Identifying which challenges or opportunities to target helps you focus and measure your efforts.

These objectives should be mapped out and communicated across leadership and operations teams so that expectations are aligned. If goals aren’t defined and connected to business value, even well-integrated AI systems will struggle to deliver results.

Step 2: Build context and operational integration

There must be a tie-in between what’s happening on the production line and your bigger-picture goals, whether those involve improvements to quality, operational efficiency or costs.

Once your plant network is ready to collect information, that information has to be transformed into something more useful than raw numbers. This means adding context.

Without it, you’re stuck piecing together data to create fragmented reports or spreadsheets to inform basic questions and troubleshoot problems after they happen.  

To handle this data translation, some plants hire data scientists or entire data analytics teams to make sense of it all. But this isn’t a sustainable solution: You’ll spend valuable time and money simply cleaning up information so you can generate value from it.

Every data point needs to tell a story, whether it’s about the SKU being produced, the operator running the line, the shift during which it happened, throughput levels during each batch, etc. In addition to supporting analysis and reporting, this context also enables better automation. Context is what turns raw data into information that can be used to automatically trigger controls, alerts and recommendations to respond to real-world events.

When AI-ready data is structured at the source with proper tagging and contextualization, it's ready to deliver usable insights about line performance, quality and efficiency.

Step 3: Enable automated insights and scalable use cases

Once your infrastructure is solid, and context is built into every piece of critical AI-ready data, the final step is to take the analytics and automation you established in pilot areas and extend those gains across more lines, products or processes to achieve even bigger results.

What does this look like in practice? In some environments, it may mean bringing AI-driven automation into daily operations to support machine setup. Operators simply log in and rely on system recommendations to optimize everything from recipe selection to throughput settings. Instead of relying on trial and error or intuition, AI’s recommendations are drawn from years of historical data and robust analytics.

This step is also the time to feed insights back to plant-floor control systems so you can update standard operating procedures and fine-tune every process for continuous improvement. You get to use the intelligence you built to make every production run more profitable, efficient and consistent, without having to reinvent the wheel every time. 

Belden helps bridge the gap from data to action

Belden can help you operationalize this framework to assess your current state, integrate and contextualize your plant data, and scale AI-driven automation and insights across your operation. We can also help you design and implement the right OT infrastructure to automate data collection for real-time visibility.

We start by leading you through assessments that identify digital and operational gaps, followed by outlining step-by-step plans to modernize your network infrastructure and unify plant-floor data.

We’ll show you how to unite production lines and assets through a unified network and automation infrastructure that supports data contextualization and AI. Our complete connection solutions enable real-time asset visibility and data capture, no matter the protocol or equipment age.

If your organization is asking you for your AI plan, and you aren’t sure how to respond, then reach out to me. Our advanced manufacturing team can help you create an AI strategy that aligns with your organization’s goals, budget and timeline/p>

Learn more about our approach

Related links:

• CPG Plant Data: Your Secret Weapon to Boost Customer Loyalty
• Why IT-OT Convergence Is Critical for CPG Transformation
• Why You Need to Know—and Reduce—Your CPG Plant’s True Downtime Costs
• Planning for CPG Legacy Equipment Upgrades: 4 Questions to Ask