Why Manufacturing Ecosystems Win: Building Scalable Partner Architectures for Industrial Data

Open Standards Break Lock-In

Proprietary platforms may simplify an initial implementation, particularly when one supplier provides most of the equipment and software. The limitations emerge when manufacturers add plants, modernize machinery or introduce analytics tools from another provider.

Open interfaces make components replaceable without requiring an organization to reconstruct its entire data architecture. They also allow manufacturers to select partners based on their ability to solve a business problem rather than on compatibility with an incumbent vendor.

Emerging initiatives could make industrial applications more portable as well, with Veronesi pointing to the Industrial Information Interoperability eXchange (i3X), an open application programming interface specification developed by the Clean Energy Smart Manufacturing Innovation Institute.

The specification is intended to let analytics and AI tools work across compliant manufacturing information platforms without rebuilding the underlying connections.

It’s a modularity allowing an ecosystem to evolve, whereby manufacturers can change cloud providers, analytics applications or integration partners while retaining the data models and interfaces supporting their operations.

DIVE DEEPER: Find out how to manage the convergence of IT and operational technology securely.

Give Each Standard a Job

Interoperability requires more than moving raw data between systems. Information must arrive with enough context for applications and users to understand what it represents.

The standards include OPC UA, which describes industrial information and its context; ISA-95, which provides the organizational hierarchy connecting enterprise, site, production line and equipment information; and MQTT Sparkplug B, which handles data movement.

“Combined, they let different systems from different vendors actually work together, without building a custom bridge every time,” Veronesi says.

The standards do different jobs that together solve the same problem: getting the right data to the right place, in a form that makes sense for users and the organization alike, he explains.

Choose Partners for Scale

A successful proof of concept does not necessarily demonstrate that an ecosystem can support multiple factories. Pilots are usually limited to one use case, controlled data and a small team whose members are committed to making the project succeed.

“The proof of concept is misleading because it almost always ‘works,’” Veronesi cautions. “It's small and controlled, and its success metrics can be defined very loosely.”

He suggests manufacturers examine whether prospective partners have moved similar projects into production across multiple sites and evaluate their adherence to open specifications, the availability of managed services and whether the partner offers reusable integration patterns.

Commercial alignment matters too. Manufacturers should determine whether a partner’s business model rewards long-term operational performance or, primarily, the initial implementation.

READ MORE: How AI is helping manufacturers adopt a unified approach to IT and operational technology.

Govern for Business Outcomes

Multivendor ecosystems require governance capable of surviving platform changes, contract renewals and internal reorganizations. IT leaders should begin by defining shared business outcomes rather than relying entirely on infrastructure metrics.

“Start with business outcomes such as production efficiency, supply chain lead times and data quality, and make those the metrics governance is measured against,” Veronesi says.

Each industrial data set should also have an accountable owner responsible for its quality, permitted uses and access policies. Wherever possible, technical controls should enforce those policies automatically across participating vendors.

Manufacturers should also document what each deployment delivers, including efficiency improvements, lower costs and shorter lead times.

“Manufacturers that build an incremental, evidence-based business case are significantly more likely to progress from early participation to full-scale deployment in industrial data ecosystems,” Veronesi says.