What One Coatings Manufacturer Got Right About Manufacturing Data
Manufacturers have invested heavily in automation, data collection and digital technologies. Yet many still struggle to turn that investment into meaningful improvements in production, efficiency and profitability.
The problem often isn’t a lack of data or technology. It’s the lack of a reliable foundation that connects equipment, systems and information in ways that make the data useful.
Without that foundation, each new initiative becomes another independent project. An OEE program requires one set of integrations. A scheduling application requires another. Predictive maintenance and AI introduce additional demands. Before long, manufacturers find themselves repeatedly solving the same data problems.
There’s a better approach, and it begins long before selecting a software platform.
Build the Foundation First
At Polaris Automation, we’ve learned that successful digital transformation depends on more than connecting equipment and collecting information. It requires building a data infrastructure that supports today’s operational needs while preparing for tomorrow’s opportunities.
Three characteristics are essential.
Reliable data. Information must be accurate, consistent and trustworthy. When operators and managers question the numbers, they revert to spreadsheets, manual records and other workarounds.
Contextualized data. Knowing that a machine produced a particular value isn’t enough. That information becomes useful when it’s connected to the equipment, production line, batch, recipe and operating conditions that produced it.
Accessible, structured data. Information must be organized so it can be used across applications, from production dashboards and MES to enterprise reporting, analytics and future AI initiatives.
These characteristics need to be considered together. A system may work well for a single application but become difficult and expensive to expand when the underlying data wasn’t designed for broader use.
The objective is to establish a common information foundation that can support multiple applications without requiring manufacturers to start over each time.
Start With the Information, Not the Software
One of the most important decisions in a digital manufacturing initiative happens before the technology is implemented.
How should equipment, processes and production information be represented?
Too often, the answer is determined by the software platform. Engineers connect PLC data to a SCADA system, historian or other application, and the resulting structure reflects the limitations or conventions of that technology.
This may satisfy an immediate requirement, but it can create problems when additional equipment, applications or facilities need access to the same information.
Polaris Automation takes a different approach.
We begin by defining an information model that describes the equipment, its operating states, critical parameters and the context associated with its data. That model establishes a consistent structure independent of any particular application.
We then determine how to implement it using the available technologies, documenting compromises when platform limitations require them.
This distinction matters because technology changes. Manufacturing operations evolve. New applications emerge.
A well-designed information model gives manufacturers greater flexibility to adapt without rebuilding their data infrastructure every time something changes.
The same principle applies to a Unified Namespace (UNS), which provides a consistent way for systems to share and access contextualized manufacturing information.
An MQTT broker can be an important component of that architecture, but the broker alone is not the UNS. The value comes from the underlying information model and the ability to connect operational and business information across systems.
A Real-World Example: Transforming a Coatings Plant
A global coatings manufacturer provides a practical example of what this approach can accomplish.
At one of its long-established production facilities, plant leadership developed a long-term vision for a leaner, more automated operation with reduced employee exposure to chemical handling.
Rather than attempting to accomplish everything through a single, large-scale implementation, the plant established a roadmap of manageable improvements.
The transformation also required more than technology.
Plant leadership had established a culture of continuous improvement supported by Kaizen initiatives, daily accountability meetings and clearly defined responsibilities. This created an environment where better information could directly influence how people managed production.
The technology needed to support that culture.
From Whiteboards to Real-Time Visibility
Before the initiative, the plant relied heavily on whiteboards, magnetic work-order indicators and spreadsheet-based production schedules.
These familiar tools helped people manage daily operations, but they also limited visibility, consistency and the ability to share information across the organization.
Polaris Automation began by implementing OEE and downtime tracking, giving the plant access to real-time production performance information.
That initial work established the foundation for subsequent improvements.
The next phase replaced spreadsheet-based scheduling with digital scheduling through Chordata, Polaris Automation’s manufacturing execution system built on Ignition.
Digital dashboards made production information readily available to support the plant’s established management and accountability processes.
Instead of manually assembling information for daily meetings, teams could work from shared, current production data.
An Incremental Approach to Improvement
The transformation progressed through four phases.
Phase 1: Production visibility
Implemented OEE and downtime tracking to establish reliable, real-time information about manufacturing performance.
Phase 2: Digital scheduling
Replaced spreadsheet-based scheduling with Chordata, providing improved visibility into production schedules and schedule adherence.
Phase 3: Expanded implementation
Extended Chordata throughout the plant, using the established information model to support additional equipment and processes.
Phase 4: Plant ownership
Trained plant personnel to manage, maintain and expand the system independently, allowing improvements to continue without relying on Polaris Automation for every change.
Each phase delivered a useful operational capability while building on the work completed before it.
The result went beyond implementing new software applications. The plant established a connected information environment that supports daily operations and ongoing improvement.
Today, the facility is among the strongest performers in its company’s manufacturing network, and the company has committed to a major expansion of the site.
That performance reflects a broader transformation involving leadership, operational discipline, technology and people. The digital foundation helped support and sustain that progress.
Building Systems Manufacturers Can Own
An important part of this success was the ability of plant personnel to take ownership of the technology.
That requires understanding the difference between configuring a system and customizing it.
Configuration allows manufacturers to adapt software through established settings, parameters and operating rules. Customization involves changing or adding software code to accommodate particular requirements.
Both approaches have their place. However, extensive customization can increase maintenance requirements, complicate upgrades and create long-term dependence on specialized development resources.
Chordata is designed to support configuration across different manufacturing environments, allowing customers to adapt the system as their operations change.
At the coatings facility, this approach allowed plant personnel to learn the system and expand its capabilities independently.
For Polaris Automation, that represents an important measure of success: delivering technology that customers can understand, manage and continue improving.
Preparing for What’s Next
The coatings plant continues to build on its digital foundation.
Today, its operational capabilities include real-time OEE visibility, digital production scheduling, Ignition-based SCADA systems and digital management boards.
Its roadmap includes expanding digital boards to additional management levels, extending the platform to other facilities and refining its UNS architecture.
AI-assisted scheduling is also part of the longer-term vision.
These initiatives are possible because the plant has established a consistent way to organize, contextualize and share manufacturing information.
As additional applications and technologies are introduced, they can build on that foundation rather than requiring entirely new approaches to data collection and integration.
The same principle applies to AI.
Manufacturers interested in advanced analytics, machine learning and AI need reliable, contextualized information. Without it, even sophisticated technologies will struggle to deliver useful results.
Where Should Manufacturers Begin?
Building a strong data foundation doesn’t necessarily require a large, enterprise-wide project.
In many cases, the best starting point is a specific operational challenge where improved information can deliver measurable value.
Before beginning, manufacturers should consider several questions:
The answers can help identify both immediate opportunities and weaknesses in the existing data infrastructure.
A practical first step is to select one production line, establish a consistent information model and implement an application that addresses a meaningful operational need.
The important consideration is designing that initial work with future expansion in mind.
The Foundation Is the Work
Digital transformation isn’t accomplished by purchasing a software platform, launching a pilot or implementing the latest technology.
It happens when manufacturers establish the systems, information and operational practices needed to make better decisions and continually improve performance.
The coatings plant demonstrates what can happen when technology supports a clear operational vision, each improvement builds on the last and people are equipped to use and expand the systems themselves.
For manufacturers, the lesson is straightforward.
Don’t build a new data solution for every new initiative. Build a foundation that makes the next initiative easier.
That’s how manufacturers turn production data into operational improvements today while preparing for the opportunities ahead.
About Polaris Automation
For more than 40 years, Polaris Automation has helped manufacturers connect equipment, systems and people to create smarter, more efficient operations. Through industrial automation, manufacturing execution systems and data infrastructure, Polaris Automation helps organizations transform production information into actionable insights and measurable improvements.
Polaris Automation is 100% employee-owned and the developer of Chordata, its proprietary MES and Batch platform built on Ignition.
This article is adapted from “The Foundation Is the Work: What PPG Got Right About Manufacturing Data,” presented by Scott Cooke, Chairman & CEO, and Scott Brown, Director of Digital Innovation, at the 2026 Ignition Community Conference.
