What d.AP does in your industry.
You don't have a concrete use case in mind yet — but you want to see what d.AP makes possible in your industry. Here is the answer.
One live. Four prepared.
We deliver in production in one industry today. In four more the architecture is in place — the first lighthouse client is open. Choose your sector, or the one that comes closest to it.
Automotive & Mobility
Three use cases in production: supply chain, quality, IT consolidation. Plus a scenario for group-wide OEE.
02 — PreparedMachinery & Plant Engineering
Plant OEE, predictive service, engineering stack, variant margins — four scenarios ready.
03 — PreparedEnergy & Utilities
Asset lifecycle, regulatory reporting, prosumers and the flex market — three scenarios.
04 — PreparedLogistics & Supply Chain
Multi-echelon, live order margins, hub networks, Scope 3 — four scenarios.
05 — PreparedTelecommunications
OSS/BSS layer, NPS drivers, fibre roll-out with a real data picture — three scenarios.
From standstill to command post.
OEMs and tier-1 suppliers juggle ECRs, MES worlds, supply chains and claims telemetry — and make decisions with last week's Excel. This is where our first live use cases are running.
Supply-chain resilience under constant stress.
Semiconductors, steel, wiring harnesses, geopolitics — new risks arrive every week. Seeing your own chain takes days.
Warranty costs are rising, nobody knows the causes.
Claims, field reports and plant telemetry speak three languages. Root-cause analyses cost quarters instead of days.
EBIT pressure meets fragmented IT.
The consolidation question is answerable — once you see what really depends on which systems. Exactly that is missing.
What d.AP already does for automotive clients.
Three use cases from automotive contexts — anonymised, technically exact. Plus one scenario that plays the architecture out directly.
From supplier event to plant response — in seconds.
Time-to-Detect −72% · 14 days lead time
Open use case 01Warranty risk before the next quarterly meeting.
Time-to-Root-Cause halved · 3.4× cause-effect pairs/Q
Open use case 02Engineering tool landscape halved, with an audit trail.
€8.6 million saved per year · 23 → 9 tools
Open use case 05Group OEE across four MES worlds.
OEE +5–7% on pilot lines · review time −50%
Open use case 06You do not see your bottleneck here? We build the scenario alongside you in a 90-minute workshop — with your systems, in your language.
Request a workshop
Made in Germany — finally with live data.
Family businesses and Mittelstand champions lead the world in engineering depth. But: data lives in CAD, ERP, service notebooks and people's heads. We bring it onto one layer — without losing its character.
Service is becoming a business — the data is not ready.
Predictive service needs telemetry, service history and engineering in a single view. Today they live in three systems, four languages, five Excel truths.
Global factories, local truths.
Every plant maintains its own MES, its own master data, its own logic. A consolidated OEE view is a political issue, not a data problem.
Margin pressure meets variant explosion.
Configurations grow faster than data maintenance. Which variant covers which margin? Without a layer, an exercise in guesswork.
What d.AP would build first for machine builders.
Four scenarios, each building on a use case from our library — transferred to the vocabulary, systems and realities of machinery and plant engineering firms.
Compare OEE across global plants side by side.
Roll-out in 14 weeks · +5–7% on pilot lines
Open use case 06Predictive service with engineering depth.
MTTR −30% · service margin +€1.8 million
Open use case 02Variant margin visible in real time.
Forecast refresh 2 weeks instead of 8 · confidence ±3%
Open use case 03Tool consolidation with an engineering audit.
€4–8 million p.a. · 18 → 7 tools in pilot
Open use case 05You would be the first lighthouse client in machine building? Exactly for this the architecture stands ready.
Request a workshop
Asset, regulator, prosumer — one view.
Energy utilities juggle the asset lifecycle, regulatory reporting and the new business with prosumers and the flexibility market. Three worlds, three speeds, one question: what can we see live?
Asset lifecycle is history, not current state.
Installation, maintenance and sensor data live separately. "How old is transformer line X?" is an Excel question instead of a layer answer.
Regulation grows faster than data maintenance.
BNetzA, EU taxonomy, ESG — new reporting requirements arrive every quarter. Whoever finds the source data wins.
Prosumer & flex market — no pilot system is enough.
The new business needs live contracts, live generation, live load flows — on the same layer as the asset.
What d.AP would build first in the energy sector.
Three scenarios — asset lifecycle as a layer, regulatory reporting on source transactions, prosumer contracts with live load flow.
Asset condition on one layer, not in three systems.
Maintenance MTTR −25% · audit trail per asset
Open use case 06Reporting on source transactions, not Excel.
Audit effort −50% · certification in 4 weeks
Open use case 03Prosumer contracts with live load flow.
Time-to-onboarding −40% · margin +€1.4 million p.a.
Open use case 04First energy utility as a lighthouse? We build your asset-lifecycle pilot in 12 weeks.
Request a workshop
Order margin — live, not in the quarterly report.
Multi-echelon inventories, hub networks, Scope 3 obligations — logistics is the discipline where every day is a data question. Four scenarios that change that.
The multi-echelon view is Excel acrobatics.
Stock levels, consumption and orders live per site. “Where is the bottleneck?” takes hours today, not seconds.
Order margin is calculated backwards.
Freight costs, hubs, volatility — margin per order shows up in the monthly close, not on the day of acceptance.
Scope 3 is an assumption.
CO₂ commitments need live supplier, freight-route and modal data. Today every figure sits on a different Excel tab.
What d.AP would build first in logistics.
Four scenarios: multi-echelon live, order margin on the day of acceptance, hub networks as a layer, Scope 3 on source transactions.
Multi-echelon bottleneck in seconds.
Time-to-detect −65% · 7 days' lead time
Open use case 01Order margin at order acceptance.
EBIT contribution +€3.2M · top margin +4.1%
Open use case 03Hub reallocation on the live load flow.
Utilisation +11% · on-time delivery +6.4 pp
Open use case 06Scope 3 as an auditable live figure.
Reporting 100% auditable · Q-cycle −50%
Open use case 05We are looking for the first logistics lighthouse — a live order-margin pipeline in 11 weeks.
Request a workshop
OSS/BSS, NPS, fibre — a single view.
Telcos deliver at two speeds: day-to-day network operations and a multi-year fibre roll-out. Both demand live data — and both are spread across ten systems today.
OSS/BSS are islands.
Network data, service tickets, contracts, billing — four worlds. Whoever wants to see the customer whole builds bridges instead of using them.
NPS drivers live in gut feeling, not in the data.
Which service experience moves NPS? Today, a guess. With the layer: live correlation across touchpoints.
Fibre-optic roll-out — plan meets reality.
Permits, civil works, activation live per region. A live view of the roll-out against plan is missing — and that costs quarters.
What d.AP would build first in telco.
Three scenarios — OSS/BSS as a layer, NPS drivers live, fibre roll-out against plan in real time.
The customer on one layer, not four islands.
Service MTTR −35% · time-to-quote −50%
Open use case 05NPS drivers from live touchpoints.
NPS +8 pp · churn −1.3 pp
Open use case 04Roll-out against plan in real time.
Slippage −40% · activation rate +6 pp
Open use case 06A telco lighthouse for d.AP? We start with an OSS/BSS pilot track in 12 weeks.
Request a workshopYour industry is on board. What's missing is your use case.
In one industry we deliver live; in four more, the architecture is waiting for its first lighthouse customer. Both are an invitation — either to the live reference, or to the first entry in your industry chapter.