From 1,000+ candidates to a 100-location shortlist. In three weeks.
[ numbers pending Statiegeld Nederland confirmation ]
Statiegeld Nederland is rolling out the next generation of bulk deposit return points — large-format machines deployed at non-retail sites where volume justifies dedicated infrastructure. Scouting the first hundred locations would have taken a small team six to twelve months of fieldwork. Locata delivered it in three weeks, with per-location reasoning that survived host outreach and internal review.
- Candidates evaluated
- 1,000+
- Shortlist size
- ~100
- Time to shortlist
- 3 weeks
- Scouting reduction
- ~10×
Gas stations & transfer stations
Ranked, with reasoning
End-to-end, kickoff to handover
Versus manual fieldwork
01 · The challenge
Bulk machines need more than retail floor space.
The original deposit return network was built around supermarket-installed RVMs. Volume kept up while bottle returns flowed through grocery checkouts. Bulk machines change the geometry: they live at non-retail sites — gas stations, transfer stations, amusement parks, schools — where surface, power, and 24/7 access matter as much as catchment.
Scouting them by hand is slow, inconsistent, and prone to missing the commercial reality of each host. A site can look viable in BAG and Street View and still fail because the host won't commit to a 10-year contract. The team needed a screening layer that pre-qualified candidates on physical, regulatory, and commercial criteria in one pass.
Hard constraint
Surface ≥ 400 m²
Truck access + manoeuvring space
Hard constraint
Three-phase power
Within 50 m of the proposed location
Hard constraint
24/7 accessibility
No fenced-off industrial parks
Hard constraint
Host alignment
Operator footprint, lease length, brand fit
02 · The approach
The four-step methodology, applied to bulk return points.
Nothing bespoke. Same methodology we apply to grid substations, EV concessions, and retail expansion — different prompt, same scoring backbone.
- 01
Defined the candidate universe
Every gas station and transfer station in the Netherlands, sourced from public operator data and BAG. ~1,000 candidates after de-duplication.
- 02
Enriched per location
BAG, Kadaster, BGT (surface), CBS catchment, NDW traffic, operator footprint, Street View frontage. Standardised per candidate, no manual lookups.
- 03
Scored with three models
Claude, GPT, and Gemini ran the same scoring prompt — hard constraints first, then weighted scoring against catchment, host alignment, accessibility, and public-space sensitivity.
- 04
Handed over a ranked shortlist
GeoJSON + PDF report per location with the top three reasons, top two risks, and per-model confidence. Used directly for host outreach and internal sign-off.
03 · The results
Ranked, reasoned, ready for outreach.
The handover was a GeoJSON file with all enrichment fields, plus a PDF report per shortlisted location. The team went straight from delivery to host outreach — no re-formatting, no follow-up data requests.
- Defensible to stakeholders. Every score traced to its inputs. No “the AI said so” conversations.
- Wider funnel, sharper top. All ~1,000 candidates pre-qualified — not just the obvious ones a manual scout would have visited first.
- Methodology reusable. The prompt and pipeline have since been adapted for glass containers, textile collection, and underground waste.
Indicative names — real shortlist names withheld pending host outreach.
Quote pending approval
We're holding this space for a quote from the Statiegeld Nederland team, currently in approval. We'd rather show an empty frame than invent one.
Statiegeld Nederland
[ role + name TBD ]
What this means for your rollout
Different output, same backbone.
The Statiegeld engagement defined a pattern that now runs across five verticals. Different data sources, different scoring prompts, same auditable trail.
Adjacent collection categories
Glass · textile · underground · parcel lockers
Same methodology applies — different scoring prompt. Bulk machines, glass igloos, and textile bins share most of the constraints (power, surface, access, host alignment).
See deposit return →Bigger funnel, same shape
EV charging concession scouting
Scoring thousands of plankaart locations against a CPO's business case is structurally the same problem: hard constraints, weighted scoring, ranked output for outreach.
See EV charging →Higher stakes, same pipeline
Grid operator site selection
Substation and transformer siting under congestion pressure is the most demanding application of the same methodology — public opposition risk in the first screening round.
See grid operators →Your rollout, scored
Bring a candidate set. We'll score it live.
30 minutes, online. The same methodology Statiegeld used, applied to whatever you're scaling.