Connecting PLM to Enterprise: Architecture for Scalable Innovation

A packaging change can ripple across regulatory, supply chain, procurement, marketing and sales. Multiply that by thousands of SKUs, faster launch cycles and a growing web of global requirements, and product innovation quickly becomes an enterprise-wide challenge.Product lifecycle management is taking on a much larger role in helping consumer goods companies manage that complexity. Learn how a connected PLM architecture can keep product data flowing among teams and systems, accelerate decisions and establish the trusted foundation companies need to apply automation and AI effectively.In this CGT Expert Exchange, Lisa Johnston, VP of content at CGT, spoke with Eric Symon, VP of sales, Americas, at Centric Software, and Mateus Grou, associate partner at EY, about how companies can modernize PLM while improving data quality, integration and governance.Symon described PLM as the digital backbone for product innovation. Product specifications, formulations, packaging information and approvals originate within the platform, while the resulting decisions reach nearly every corner of the organization.“PLM is still the system where the product design decisions begin, but those decisions have to flow across the enterprise,” he said.That expanding role requires a clear product data model. Companies typically rely on several trusted systems of record: ERP for transactional and production data, PLM for product specifications and formulations, and PIM or DAM platforms for customer-facing content and digital assets. The architecture must define ownership and govern how information moves among them.“PLM has become a platform for orchestrating product information across the whole enterprise,” he noted.When it comes to one of the most common obstacles, data quality still tops the list. Inconsistent naming conventions, product attributes and units of measure can weaken integrations and slow transformation. Symon recommended addressing those issues early and incorporating integrations into the initial architecture.Connections to regulatory and quality systems carry particularly high stakes. Errors involving ingredients, allergens, claims or labeling can lead to recalls, legal exposure and consumer safety risks. Product data must also flow accurately into packaging, commerce and marketing systems to maintain consistency and protect customer trust.Aligning the architecture with the company’s operating model is equally important. When product information changes, connected workflows should trigger the appropriate decisions and actions across regulatory, supply chain, procurement and other affected functions.“The data must flow,” Grou said, adding that the architecture should reflect the company’s operating model and provide the orchestration needed to turn information into action.Preparing for AI requires a flexible, configurable architecture built on three foundations: trusted data, a connected technology landscape and an orchestration layer that reflects the organization’s priorities. With those elements in place, AI can help teams work faster and make more informed product decisions.Ultimately, PLM modernization should be treated as a product data transformation, Symon said. Teams need to agree on how product information will be structured, governed and shared before configuring the technology to support faster innovation, accuracy, compliance and control.

Connecting PLM to Enterprise: Architecture for Scalable Innovation

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