PLM for Process
PLM for process is fundamentally different. It’s not just parts and BOMs—it’s formulas, constraints, compliance, and controlled variability.
Why this distinction matters
PLM typically splits into two different worlds:
- PLM for Discrete products (parts and assemblies)
- PLM for Process products (formulas, batches, and variable outcomes)
If your business makes food, beverage, beauty, chemicals, supplements, or any product where the recipe matters as much as the package, you live in PLM for Process. That drives different data structures, different governance risks, and different operational needs than discrete manufacturing.
PLM for Discrete
What it is
Discrete PLM manages products made from component parts. The product definition is a structured BOM where items and relationships are usually stable.
Typical characteristics
- BOM is the center of the product definition
- Revisions are usually linear (Rev A → Rev B → Rev C)
- Changes are commonly engineering-driven (ECR/ECO)
- Compliance is often component-based (material declarations, RoHS/REACH, etc.)
- End item is assembled and typically doesn’t vary once released
Common data objects
- Parts and part numbers
- Assemblies and subassemblies
- BOM with quantities and alternates
- CAD drawings and revisions
- Approved Manufacturer Lists (AML) / Approved Vendor Lists (AVL)
- Engineering change objects and routings
Where discrete PLM usually connects
- CAD and PDM tools
- ERP for manufacturing planning and purchasing
- Quality systems for nonconformance and deviations
What “good” looks like
- Clean part master
- Controlled BOM revisions
- Reliable ECO process
- Engineering and manufacturing stay aligned
PLM for Process
What it is
Process PLM manages products where the definition is formula-driven and outcome-driven. The product is not just a BOM; it’s a set of controlled inputs, constraints, and rules that yield a compliant, repeatable result.
Typical characteristics
- Formulas replace (or dominate) traditional BOMs
- Product definition includes targets, ranges, and tolerances
- Ingredients may vary by supplier, region, or availability
- Outcomes must meet regulatory, quality, and labeling requirements
- Versioning is often branching and contextual (not just linear)
Common data objects
- Formulas / recipes
- Ingredients and raw materials with attributes (allergens, claims, hazard, country of origin, etc.)
- Specifications tied to ranges (pH, viscosity, potency, etc.)
- Nutritional, allergen, and regulatory attributes
- Labeling and claims logic
- Packaging specs tied to regulated statements and artwork
- Supplier/region variants and substitutions
- Finished good specs that reflect “what’s allowed,” not just “what’s listed”
Where process PLM usually connects
- ERP for costing, inventory, and production execution
- Labeling and regulatory platforms (claims, allergens, language, compliance)
- Quality systems (COAs, test results, deviations)
- Sustainability/EPR reporting
- Artwork systems where compliance language must match approved data
What “good” looks like
- Teams trust the data for compliance and production decisions
- Formulas and specs are governed by clear rules
- Approvals mean “ready for downstream,” not just “approved”
- Changes don’t break labeling, claims, or ERP costing
- Reporting is reliable without manual reconciliation
Key differences (the practical version)
| Category | Discrete PLM | Process PLM |
|---|---|---|
| Product definition | Structure is the definition (BOM is truth) | Intent and constraints are the definition (formula + rules) |
| Variability | Minimized | Managed (substitutions, ranges, regions) |
| Compliance | Component-based | Outcome-based |
| Versioning | Linear revisions | Contextual branching |
| Approval meaning | Engineering release | Operational readiness |
| Integration impact | Primarily manufacturing | Enterprise-wide (ERP, labeling, quality, reporting) |
Where PLM for Process usually breaks
- Approval does not equal readiness
- Supplier substitutions happen without controlled downstream updates
- Formula and packaging data drift away from label and artwork
- ERP costing and planning run on incomplete or mismatched data
- Teams rely on spreadsheets for conversions, rollups, and reporting
- Ownership is unclear: who fixes what, and when, before data moves
How Dazmii supports PLM for Process
Specification and master data execution
- Digitization and structured data entry
- Cleanup, normalization, and readiness checks
- Reference data and controlled vocabulary alignment
- Bulk loads and migration execution (including attachments when applicable)
Change and workflow execution support
- Change intake, triage, and scope clarity
- Execution coordination across teams
- Approval coordination and readiness validation
- Preventing repeat issues by identifying the true breakdown point
Governance and data quality stabilization
- Define minimum required data to operate
- Set ownership by data domain
- Establish acceptance criteria so approvals have meaning
- Build practical standards teams can follow without slowing down
Cross-system reliability
- Support PLM data consumption across ERP, labeling, quality, reporting, and sustainability
- Surface missing business rules and enforcement gaps
- Move toward controlled data flow as reporting and integration demands increase
Who this is for
- Formula-driven or attribute-driven product companies
- Organizations where labeling, claims, allergens, compliance, or sustainability reporting matters
- Teams working across functions, regions, and suppliers
- ERP-connected environments where timing and correctness matter
- Leaders who need stability and execution before transformation
What success looks like
- Approvals mean operational readiness
- Changes don’t create downstream surprises
- Vendor and ingredient variability is controlled
- Reporting becomes leverage, not a manual project
- Your team stays in the driver seat without carrying the full burden
Start where it hurts most
If your PLM environment supports formula-driven products, the best next step is to align on where execution breaks, what “ready” should mean, and which business rules must be enforced before data moves downstream.
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