TechHub: How Manufacturers Turn Data into Results

Managing the Data Deluge in Modern Manufacturing with AI

Manufacturers are producing more data than ever.

One production line can involve hundreds of devices, each equipped with dozens of sensors collecting data about temperature, vibration, product color, throughput speed and more every second, or faster.

Terabytes of data are just a starting point for measuring the daily output. One IBM Institute for Business Value report estimated a single production line might produce 2,200 terabytes of data every month.

Harnessing that incredible volume of data is at the core of the argument today for deploying AI models that can clean and contextualize data and then offer recommendations to plant-floor personnel.

Visualizing Data vs. Deciding with Data

Modern facilities that have a Historian, SCADA and MES can already answer many questions about their production lines.

What machines are running or stopped? How much product did we produce yesterday compared to the day before? Which plants consistently stay within their quality thresholds?

But the Historian, SCADA and MES can’t necessarily take that all-important next step. They can’t recommend a course of action when product quality dips or a machine shuts down unexpectedly. Manufacturers rely on experienced operators to do that, and rightly so.

It takes time and resources to find and train operators, and manufacturers are contending with a labor shortage. Operators deserve another tool to back up their experience and help them do the best job possible.

That’s where the promise of AI, also known in this context as Productivity Management Systems, comes in.

A Productivity Management System can answer questions such as: Why does production throughput dip when comparing shift one to shift two? How does warm weather and humidity impact drying times for a paper product? Can we warn workers before a downtime event occurs?

With those answers, the system can help operators challenge the current consensus and recommend actions that have a good chance of making improvements.

Choosing the Right AI Partner

Of course, getting the most out of an AI model depends on using the right tools and choosing the right partner from a crowded field.

For example, Braincube, a GrayMatter partner, differentiates its AI-powered Productivity Management System by generating a computational model of the entire production process, from raw materials to finished goods. This end-to-end understanding reveals where the greatest potential for performance improvement and financial impact lies.

Braincube’s models can be deployed quickly, allowing teams to begin exploring optimization opportunities without long setup cycles. Once in place, the model becomes a continuous, scenario-based decision tool rather than a one-time analysis.

This empowers process engineers to act as “resident data scientists,” adjusting variables, testing hypotheses, and receiving feedback on how well real-world operations align with the model’s recommendations.

Instead of providing a simple score, Braincube identifies and ranks the variables that have the greatest influence on your optimization goals. Production and operations leaders can use these insights to determine where to focus, such as weighing increased throughput against energy costs, current demand, or other strategic considerations.

Getting Results

The biggest factor in achieving a business objective with AI remains the people involved and whether they’re invested.

“Companies with an engaged and supported workforce deliver significantly better outcomes from AI than those companies that are still halfway toward training and engaging with their employees on AI,” according to the “AI Business Value Radar 2025” Infosys report.

“Up to 18 percentage points can be added to the chance of success of AI if a company has fully established change management AI training processes and is involving its employees in decision-making about AI.”

It pays dividends to involve process engineers and operators early. They’ll be that much more likely to discover and champion new optimization opportunities down the line.

How Maple Leaf Engineers Cut Waste & Boosted Productivity in 90 Days

GRAYMATTER & BRAINCUBE WEBINAR

Join GrayMatter & Braincube for a practical session on how to unlock productivity improvements using data already in your production systems.

You’ll hear how the engineering team at Maple Leaf Foods used Braincube to drive a 12 percent yield increase, fast-track ROI and surface continuous improvement opportunities across lines.

Topics we'll cover:

How to leverage the data captured in your production systems (MES, Historian, Quality, LIMS, ERP etc.) to find hidden inefficiencies
The workflow Maple Leaf’s engineers used to prioritize and act on performance gains
A live view of Braincube in action, which is purpose-built for process teams
How engineers can quantify potential gains and reduce waste in under 90 days

The Darknet’s Growing Threat to Industrial Control Systems

2025 Unboxed: A Year of Thinking and Doing