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Cars, sealants, composites, ceramics, and technical glass have little in common on the shelf. In the lab they share one problem: the product is defined by its materials and its process, and one change to a raw material can touch dozens of finished products. When that product record lives in spreadsheets, PDFs, and a PLM built for mechanical parts, a single substitution turns into weeks of tracking down what it affected. This is a look at how a product lifecycle and quality platform built for formulations keeps that change on one connected record instead.
Why does one material change cost material-intensive manufacturers so much?
The cost comes from disconnection, not from the change itself. A restricted solvent, a discontinued filler, or a requalified resin has to reach every formulation that uses it, every specification, and every test method, across every product and site. In a stack of documents, someone has to find each affected product by hand, and something is always missed. The result shows up later as a failed batch, a missed audit, or a reformulation nobody flagged.
What does a PLM built for materials do differently from one built for parts?
It treats the formulation, not a mechanical bill of materials, as the heart of the product record. Traditional PLM was designed for parts and assemblies, so it models a product as components bolted together. A material-intensive product is defined by its recipe and process, so the platform keeps the formulation, its specifications, its documents, and its stage-gate approvals together as one source of truth. Change an ingredient once and it propagates to every product that uses it, routed through change control with the right people notified. Sun Chemical described the shift as "a new level of knowledge access and capture across our organization," with the team able to develop and deliver products faster.
How does connecting QA/QC to the product record catch problems earlier?
It closes the gap between the spec and the test. When a specification changes, the revised targets carry straight into QA/QC, so testing checks against the current formulation rather than a stale one. An out-of-spec result traces back to the batch, the raw-material lot, and the formulation revision that produced it, and it can trigger a quality event without anyone rekeying data. Development, production, and quality all reference the same record, so a result means the same thing to everyone who reads it.
What does this look like across automotive, sealants, composites, ceramics, and glass?
The workflow is the same, the tests differ. Automotive and sealants teams track cure behavior, adhesion, and durability against tightening regulatory limits. Composite teams manage layup, cure cycles, and mechanical properties, where one resin change means requalifying panels. Ceramics and technical glass teams carry coating and process data through qualification and scale-up. In each case, the value is the same: every ingredient, parameter, and result is structured and searchable, so a relationship between a process change and a property is visible instead of buried. On a single project, Sika reported roughly 75% fewer experiments and over 50% faster time to market, and Covestro reported development timelines up to six months faster.
How hard is it to move off a legacy PLM?
Less than most teams expect, because it is configuration rather than custom code. Sample types, stages, gates, and instrument connections are set up around how your lab already runs, during a staged rollout, so you are not rebuilding your process to fit the tool. Cibimmob increased its product-development output after moving onto one connected platform. If you are weighing a move off a PLM that was built for parts, the companion post on migrating off a legacy PLM walks through what actually transfers.
Structure first, AI second: once the product record, its formulation, and its tests are connected, the analysis and automation on top of them finally have something reliable to work with.
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