EDP Strategy 2026 · EPIC I – Quartalsweise Trendanalyse
RP
Engineering & Digital Factory Platforms

EDP Portfolio

Turning Engineering into Competitive Advantage.

„We enable industrial companies to lead through innovation and engineering excellence."

From digital silos
to integrated engineering backbone
From AI use cases
to implemented value
From capabilities
to governed platforms
Our Core Focus

Four capability areas across the engineering value chain.

EDP positioniert sich als zentrale Säule im Bereich Engineering Systems – verbunden mit Product Strategy, Digital Factory und Supply Chain.

01

Product Strategy & Innovation

Define product strategy and innovation through digital product definitions and system architectures to enable differentiated and scalable product development.

02

Engineering and PLM Ecosystems

Enable connected, scalable and AI-driven engineering and PLM execution through integrated processes, platforms and data foundations.

Core Focus
03

Digital Factory

Turn engineering intent into efficient, automated and scalable production execution.

04

Supply Chain

Ensure resilient, scalable and transparent supply chain execution through integrated planning and collaboration.

EDP Value Proposition

Drive → Execute → Accelerate.

Drei Säulen. Eine zusammenhängende Transformationsgeschichte.

1 · DRIVE
Engagement & Enablement

Set the transformation up to succeed.

Transformation strategy & value case · Capability Management · Transformation Roadmap · PLM Assessment · Delivery orchestration

Service Lines: SL1 – SL3
2 · EXECUTE
PLM Transformation

Build the integrated engineering backbone.

Engineering landscape assessments · Platform strategy & architecture · Digital thread design · Solution architecture · Platform rollout governance · Engineering and PLM concept implementation

Service Lines: SL4 – SL7
3 · ACCELERATE
AI Automation & Integration

Embed AI where it creates governed value.

AI-supported engineering processes · Engineering knowledge intelligence · Automation of engineering workflows · Low-code engineering applications

Service Lines: SL8 · SL9
Service Lines

Neun Service Lines – über den gesamten Transformationslebenszyklus.

DRIVEEngagement & Enablement
SL · 1Platform Strategy

Platform Strategy, Assessment & Roadmap Design

From pain to roadmap – fact-based, not slideware.

Modules
  • ■PLM Capability Assessment (AI-augmented)
  • ■Engineering Value Chain Analysis
  • ■Toolchain & Architecture Review
  • ■Target Architecture & PLM North Star
  • ■Fit-Gap Analysis (current vs. target)
Key KPIs
<4 Wochen bis zu ersten Insights60 % schnelleres Data Mapping80 % weniger Analyseaufwand70 % AI-erzeugter Output
SL · 2Transformation Governance

Transformation Governance & Enablement

Make change stick across people, process & technology.

Modules
  • ■Transformation Governance Framework
  • ■Capability Management & Skills Mapping
  • ■PLM Academy (Enablement-Formate)
  • ■Stakeholder & Change Management
  • ■Program Steering & PMO
Key KPIs
Adoption Rate +30–50 %Time to Skill −40 %Konsistente Prozesse über Standorte
SL · 3Capabilities & Process

PLM Capability, Process and Organizational Design

Design the organization that runs PLM at scale.

Modules
  • ■PLM Capability Maps & Role Definitions
  • ■Engineering Process Design (end-to-end)
  • ■Organizational Design & Operating Model
  • ■PLM Service Levels & RACI
  • ■Skills Framework & Career Paths
Key KPIs
Process Cycle Time −15–20 %Rollenklarheit +40 %Prozessstandardisierung über BUs
EXECUTEPLM Transformation
SL · 4Engineering Concepts

xBOM-Management, (MB)SE & Digital Twin

Engineering concepts to scale mechatronic, software-defined products.

Modules
  • ■xBOM Management (EBOM / MBOM / SBOM / ServBOM)
  • ■Model-Based Systems Engineering (MBSE)
  • ■Digital Twin Architecture & Foundation
  • ■Variant & Configuration Concepts
  • ■Requirements Architecture
Key KPIs
BOM-Konsistenz +40–60 %Mechatronik-Integrationszyklus −30 %Engineering Rework −25 %
SL · 5Technological Solution

PLM Architecture & Solution Design

Architecture, governance & data model – designed to scale.

Modules
  • ■PLM Solution Architecture
  • ■Data Model & Information Architecture
  • ■Change & Release Process Design
  • ■Variant & Configuration Management
  • ■Governance & Operating Model
Key KPIs
Development Lead Time −15–20 %Datenfehler −40 %Variantenfehler −50 %
SL · 6PLM Integration & Cloud

PLM Integration & Cloud Transformation

De-risked move to SaaS – built on facts.

Modules
  • ■Cloud Readiness Assessment (AI-driven)
  • ■Cloud Target Architecture
  • ■Migration Strategy & Sequencing
  • ■Customization Review (Keep / Replace / Retire)
  • ■AI-Powered Data Migration
Key KPIs
IT-Betriebskosten −15–25 %Integrationsaufwand −30 %70 % AI-erzeugter Output90 Tage bis Produktion
SL · 7Integration & Thread

Engineering Integration & Digital Thread

End-to-end data flow – from requirements to service.

Modules
  • ■PLM-ERP-Integration
  • ■ALM-PLM-Integration (software-defined products)
  • ■Data Synchronization & Master Data Governance
  • ■Integration Architecture (API-first, event-based)
  • ■EBOM → MBOM → SBOM Mapping
Key KPIs
Produktionshochlauf bis 20 % schnellerAbstimmungsaufwand −40–60 %Wiederkehrende Fehler −20–30 %
ACCELERATEAI Automation & Integration
SL · 8Engineering AI

Engineering Intelligence & AI Automation

From copilots to governed autonomy.

Modules
  • ■AI Use Case Development & Prioritization
  • ■Knowledge Graph & Graph RAG
  • ■Engineering AI Search (semantisch)
  • ■Agentic Workflow Orchestration (n8n)
  • ■AI-Supported Change Impact Analysis
Key KPIs
Suchzeit −50–70 %Change-Analysezeit −80 %Anforderungsfehler −25–40 %Compliance-Aufwand −40–60 %
SL · 9Engineering & PLM Ecosystems

Engineering Supplier Ecosystems & Intelligence

Supplier transparency, risk & regulation – orchestrated.

Modules
  • ■Supplier Monitoring & Risk Intelligence
  • ■cplace Risk Radar Setup
  • ■Regulation Tracking (end-to-end)
  • ■Supplier Data Integration
  • ■Supplier Collaboration Platform
Key KPIs
Lieferantenabstimmung −30–50 %Compliance-Audit-Aufwand −40–60 %Weniger Audit-Findings
From Customer Needs to Engineering Values

Vier Dimensionen, sechzehn Driver.

SPEED

Faster cycles and execution.

Time-to-Market, iteration speed and faster engineering change cycles.

Requirements & Change Flow
Accelerate requirements refinement and engineering change processes through better structure and downstream coordination.
Engineering Automation
Use automation to reduce manual effort, increase consistency and speed up engineering operations.
Collaboration & Workflow
Improve cross-functional execution by reducing handover friction across engineering, manufacturing, quality and service.
Validation & Release
Shorten validation, approval and release cycles while maintaining quality, compliance and governance.

CAPACITY

More output per engineer.

More output per engineer through reuse, automation and productivity enablers.

Portfolio Visibility
Create transparency on demand, bottlenecks and delivery performance to improve prioritization and steering.
Reuse & Standardization
Enable reuse of engineering knowledge, standards and proven solutions across products and teams.
AI Copilots
Support engineers with AI-driven search, analysis and decision preparation in daily work.
Engineering Productivity
Reduce non-value-adding effort and simplify workflows to increase effective output per engineer.

SCALABILITY

Platforms and complexity at scale.

Managing complexity at scale through robust platforms, rollout logic and integration.

Config & Variant Management
Manage product complexity through structured configuration logic, variant rules and dependencies.
Platform Modernization
Modernize PLM and engineering environments into scalable, cloud-ready and future-fit platforms.
Rollout Governance
Scale platforms and processes consistently through templates, governance and rollout structures.
Digital Thread & Digital Twin
Connect systems, data and processes across domains to build an integrated engineering backbone.

INTELLIGENCE

Traceability, analytics and feedback.

Better decisions through traceability, analytics, feedback and data-driven engineering.

Traceability & Impact Analysis
Enable end-to-end traceability and faster impact assessment across requirements, changes and releases.
Product Data & Intelligence
Turn product structures, BOMs and engineering data into reliable, decision-ready information.
Closed Loop Quality
Connect engineering with feedback from quality, manufacturing and service for continuous improvement.
Engineering Analytics
Use analytics to improve engineering steering, identify bottlenecks and prioritize actions based on data.
Crosscut · Technologies & Partners

Vendor-agnostisch – mit dem passenden Plattform-Mix.

PLM Core
  • Siemens Teamcenter / Teamcenter X
  • Dassault 3DEXPERIENCE
  • PTC Windchill / Windchill+
  • Aras Innovator
  • Contact CIM Database
ERP / Adjacent
  • SAP S/4HANA
  • SAP VC/AVC
  • SAP Supplier Network
ALM Systems
  • Polarion
  • Codebeamer
  • IBM DOORS Next
Integration
  • n8n (low-code agentic)
  • SAP BTP
  • Custom API/Event-based
  • Knowledge Graphs (Neo4j, RDF)
AI & Cloud
  • Anthropic Claude (Sonnet, Opus)
  • Microsoft Copilot
  • Azure OpenAI
  • AWS Bedrock
  • Vector DBs
  • Graph RAG
Governance
  • ServiceNow (CMDB, GRC, Workflow)
  • cplace
CAD / ECAD
  • NX, CATIA, Creo, SolidWorks
  • ECAD: Cadence, Altium
Manufacturing
  • Siemens DELMIA / 3DEXPERIENCE Manufacturing
  • MES-Plattformen
Crosscut · Personas

Wer kauft was.

CEO
CIO
CTO
AI-Lead / CDO
Head of Engineering
Head of R&D
Head of Data & AI
Product Manager
Engineering Manager

9 Service Lines × 9 Personas – die Relevanz-Matrix aus der Unterlage bildet ab, welche Rolle welches Angebot kauft.