PLM strategy
A vision, scope and phasing tied to business outcomes: time to market, compliance, cost and reuse.
PLM Consulting
One trusted product record, from design to market.
We help product-centric organizations define how product data should be owned and moved, choose platforms against that design, and connect PLM to ERP, DAM, MLR and downstream systems.
Typical challenges
What we are usually asked to fix.
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Fragmented product data
The same attribute exists in several systems with different values, owners and update cycles.
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Platform decisions led by vendors
Selection is driven by demonstrations rather than by your processes and data model.
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Slow change and launch cycles
Engineering changes, artwork, claims and regulatory content move through manual hand-offs.
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Implementations that drift
Scope expands, integrations are under-estimated and business adoption trails the technical go-live.
Curiosbot capabilities
From PLM strategy to measured adoption.
Process and operating-model design
End-to-end product processes, roles, decision rights and change-control mechanisms designed before configuration begins.
Platform evaluation
Requirements-led selection with scenario-based scoring, total cost of ownership and implementation-risk analysis.
Product-data governance
Data model, ownership, quality rules and lifecycle states that make a single product record credible.
Integration with ERP, DAM, MLR and downstream systems
Integration architecture and mappings so approved product data and assets reach the systems that consume them.
Implementation assurance
Independent oversight of scope, design, testing, cutover readiness and vendor performance.
Adoption and value measurement
Training approach, usage indicators and benefit tracking that show whether the investment is working.
Engagement model
Start small, go as deep as the value justifies.
Durations are indicative and depend on scope, access and organizational complexity. We confirm them after a first conversation.
Typically 4–8 weeks
PLM strategy and roadmap
Current-state review, target model, business case and phased roadmap.
Typically 6–10 weeks
Platform evaluation
Requirements, vendor scenarios, scoring and a recommendation you can defend to the board.
Ongoing through delivery
Implementation assurance
A senior, independent voice on design, risk and readiness alongside your system integrator.
Deliverables
What you can expect to hold in your hands.
- PLM vision, scope and phased roadmap
- Target process maps and operating model
- Platform evaluation report with scoring and cost model
- Product-data model and governance framework
- Integration architecture for ERP, DAM, MLR and related systems
- Implementation risk register and assurance findings
- Adoption plan and value-measurement framework
Transformation roadmap
A sequence you can plan and fund.
- Weeks 1–4
Assess
Processes, systems, data and pain points across engineering, operations, commercial and regulatory teams.
- Weeks 5–10
Design
Target processes, data model, governance and integration architecture.
- Weeks 11–16
Select and plan
Platform and partner evaluation, business case and delivery plan.
- From mobilization
Assure and adopt
Implementation assurance, adoption activities and benefit tracking.
FAQ
Frequently asked questions
Are you tied to a particular PLM vendor?
No. Our advice is platform-agnostic and starts from your processes and data. We assess vendors against requirements you have agreed in advance.
Why include DAM and MLR in a PLM discussion?
Product information does not stop at engineering. Digital assets and medical, legal and regulatory review depend on approved product data, so gaps between these systems create rework and compliance risk.
Can you help if our PLM implementation is already under way?
Yes. Implementation assurance is designed for this: an independent review of scope, design and readiness, with concrete recommendations to get back on track.
Do you work with the integrator we already have?
Yes. We work alongside integrators and vendors, representing your interests without competing for the delivery work.
What does product-data governance involve in practice?
Defining what data exists, who owns it, which system is the source of truth, what quality rules apply and how changes are approved, then making those decisions operable in the tooling.
Related insights
Further reading.
Connecting PLM, DAM and MLR processes
How product data, digital assets and regulatory review can share one governed flow instead of three disconnected ones.
Talk to us about your product-data landscape.
A first conversation is a working session, not a sales pitch. Bring the ambition and the constraints; we will help you shape the sequence.