Simulating a Digital Product Passport in 1 Product

Simulating a Digital Product Passport in 1 Product

Arkive ran a fast, “public-data-first” simulation for a potential client to show how a Digital Product Passport (DPP) and an LCA-style footprint can be structured end-to-end before any ERP/PLM integration is in place. The goal: prove what the passport can look like, what inputs matter, and how quickly a company can move from product data to decision-ready insights.

The product (simulation input)

We used a single SKU to keep the model simple and transparent:
  • Product: T‑shirt
  • Material: 100% organic cotton
  • Weight: 200 g
  • Made in: Turkey (production in İzmir region)
  • Color: White
  • Cost: €25
  • Production volume: 200,000 units per batch, 2 batches/year
  • Distribution countries: Norway, Netherlands
  • Recycling: in the distribution country
This is exactly the type of “minimum viable dataset” many teams can provide early, often from a spreadsheet - while the deeper data mapping happens in parallel.

What the simulation demonstrates

This simulation is not a verified footprint or a certified LCA. It’s a structured, auditable way to:
  1. Turn product attributes into a DPP-ready data model
  2. Estimate CO₂ hotspots across lifecycle stages using clear assumptions
  3. Show what additional data is needed to move from estimates to verified results
  4. Create a repeatable workflow that scales across SKUs and batches

Method: lifecycle stages we modeled

We structured the simulation the way a DPP and LCA workflow typically needs to be organized:
  1. Sourcing (materials)
  2. Sourcing transport (farm → factory)
  3. Manufacturing (energy)
  4. Manufacturing transport (factory → warehouse)
  5. Warehousing (energy over time)
  6. Sales & distribution (placeholder in this simulation)
  7. Usage (not applicable for apparel in this simplified model)
  8. End of life (framework defined; requires facility inputs)

Step-by-step: how the CO₂ estimate was calculated (with transparent assumptions)

1) Materials (organic cotton)

We used public information from CarbonCloud for the organic cotton factor:
  • Emission factor (EF): 0.978 kg CO₂ per kg organic cotton
  • Material mass in product: 0.2 kg (200 g)
Material CO₂ per product: 0.978×0.2=0.1956 kg CO₂

2) Sourcing transport (cotton farm → production facility)

  • Farm location: Manisa
  • Factory location: İzmir
  • Distance: 38.4 km
  • Transport assumption: 0.5 kg CO₂ per km per 1 kg of material
  • Product material mass: 0.2 kg, so we scale by 0.2 (divide by 5)
Transport CO₂ per product (as used in the simulation): 0.5×38.4÷5=3.84 kg CO₂
Total sourcing stage CO₂ per product: 3.84+0.1956=4.0356 kg CO₂
Note: The transport assumption is intentionally explicit here. In real deployments, Arkive replaces placeholder assumptions with client-specific logistics modes, weights, and verified factors.

3) Manufacturing (electricity)

We modeled manufacturing emissions using energy per product:
  • Energy per product: 5 kWh
  • Turkey electricity EF: 0.398 kg CO₂/kWh
Manufacturing CO₂ per product: 0.398×5=1.99 kg CO₂

4) Manufacturing transport (factory → warehouse)

  • Warehouse location: Wałbrzych, Poland
  • Distance (İzmir → Wałbrzych): 2,375 km
  • Same transport assumption: 0.5 kg CO₂ per km per 1 kg
  • Scale to 0.2 kg product mass (÷5)
Transport CO₂ per product (as used in the simulation): 0.5×2375÷5=237.5 kg CO₂
This result is a perfect example of why simulations are valuable: it immediately flags where assumptions are unrealistic or need better logistics inputs (mode of transport, load factors, shipment allocation, etc.). Arkive uses these “red flags” to drive the data collection checklist.

5) Warehousing (energy over time)

Warehousing was calculated based on time in storage:
  • IN date: 1 Feb 2026
  • OUT date: 3 Mar 2026
  • Duration: ~1 month
  • Energy per product: 1 kWh/day → 30 kWh/month
  • (EF not applied in the simulation text yet)
In a production-grade model, this becomes: Warehousing CO₂=kWh×electricity EF (region-based)

6) Sales, distribution, usage, end-of-life

  • Sales & distribution: marked as “same as warehousing transport” (placeholder)
  • Usage: excluded (simplified apparel model)
  • End of life: framework defined; needs facility energy per kg and an EF

Output: what the client gets from a simulation like this

Even before integrations, a simulation can produce:
  • A DPP-ready product dataset (what fields exist, what’s missing, what’s optional)
  • A stage-based footprint structure (where emissions sit across the lifecycle)
  • A data improvement roadmap (what to replace with verified inputs)
  • A repeatable template to scale across product families and batches

Why this matters (and how Arkive helps)

Most DPP projects stall because teams try to “boil the ocean” on day one—waiting for perfect data, perfect integrations, and perfect calculations.
Arkive flips that workflow:
  • Start with a structured simulation to align stakeholders
  • Identify the minimum data needed for credible outputs
  • Then connect ERP/PLM/suppliers to replace assumptions with verified inputs
  • Scale the same model across SKUs, batches, and markets

Want to simulate your product?

If you’re preparing for Digital Product Passport requirements and want to see what your DPP could look like - fast - Arkive can run a structured simulation and give you a clear path from “spreadsheet reality” to verified, scalable compliance.
Terug naar blog

Reactie plaatsen