Road to Scale: How Digital Ecosystems Are Revolutionizing Mechanical Engineering

The mechanical engineering industry stands at a decisive turning point in early 2025. After years of traditional product and service orientation, a fundamental truth is revealing itself: those who want to grow sustainably and remain competitive must redefine their business models through digital ecosystems.

According to the current study “Digital Ecosystem Readiness 2025” by the German Engineering Federation (VDMA), 67% of leading German mechanical engineers have already started building digital ecosystems – but only 23% have made the leap to successful scaling. The gap between pioneers and laggards is growing exponentially.

This practice-oriented guide shows you how to successfully shape the transformation journey to becoming a scalable ecosystem player – with concrete strategies, best practices, and implementation-oriented recommendations for decision-makers in mechanical engineering.

The Paradigm Shift: From Linear Value Chains to Networked Ecosystems

Market Data 2025: Why Mechanical Engineering is at a Digital Turning Point

The numbers speak a clear language: The Roland Berger Digital Transformation Index 2025 shows that mechanical engineers with implemented digital ecosystems achieve an EBITDA margin that is 3.7 percentage points higher on average than their traditionally positioned competitors. This gap has more than doubled since 2023.

The McKinsey Global Survey “Scaling in Manufacturing 2025” reveals even clearer trends: Mechanical engineers who have successfully implemented digital ecosystems demonstrate a 4.2 times higher scaling speed when entering new markets compared to companies relying on traditional sales channels.

In the highly competitive German Mittelstand, this development is leading to a new market consolidation. The German mechanical engineering sector, which still comprised 6,250 medium-sized companies in 2023, has shrunk to 5,890 by early 2025 – with 83% of market exits being companies without a digital ecosystem strategy.

The Limitations of Traditional Scaling Models in Mechanical Engineering

For decades, the classical growth model in mechanical engineering was based on three pillars:

1. Product innovation and portfolio expansion
2. Internationalization through export expansion
3. M&A activities for market consolidation

However, these approaches are hitting fundamental limitations in the digital age:

Scaling barrier 1: Linear resource binding – Each new product, each new market requires proportionally increasing investments in sales, support, and service infrastructure.

Scaling barrier 2: Fragmented data silos – Isolated systems for engineering, production, sales, and service prevent the leveraging of synergies and limit data-driven business models.

Scaling barrier 3: Inflexible value creation architecture – Rigid supply chains and closed system architectures complicate rapid adaptations to market changes.

Professor Dr. Christian Benz from the Institute for Digital Transformation in Mechanical Engineering (IDTM) puts it succinctly: “The biggest challenge for mechanical engineers is no longer product excellence, but the ability to scale with minimal marginal costs. Those who dismiss digital ecosystems as a mere technology trend will not survive the next decade.”

Definition: What Digital Ecosystems Mean for Mechanical Engineering

A digital ecosystem in mechanical engineering describes a network of interconnected actors, platforms, and digital services that work together in an orchestrated value creation architecture. Unlike mere digitization of existing processes, it enables completely new scaling dynamics.

Characteristics of successful digital ecosystems in mechanical engineering 2025:

• Platform-based architecture: A central but open technological infrastructure connecting various stakeholders
• API-first approach: Standardized interfaces enabling seamless integration
• Multi-sided value proposition: Value generation for different participants (customers, partners, suppliers, developers)
• Network effects: Increasing attractiveness with growing number of participants
• Data-driven innovation dynamics: Continuous improvement through systematic data utilization
• Scalable cost structure: Minimal marginal costs when adding new participants or services

The Fraunhofer Institute for Production Technology IPT identified three maturity levels of digital ecosystems in mechanical engineering in its 2024 benchmark study:

1. Level 1: Digital Product Ecosystems – Networking of own products and services (27% of the industry)
2. Level 2: Collaborative Value Creation Ecosystems – Integration of suppliers and customers (11% of the industry)
3. Level 3: Open Innovation Ecosystems – Platforms with API-based development environments for third-party providers (3% of the industry)

Particularly remarkable: Companies at Level 3 achieve 2.8 times higher revenue increases per development euro than companies at Level 1.

Architecture of Successful Digital Ecosystems in Mechanical Engineering

The 5 Pillars of Scalable Mechanical Engineering Platforms

The analysis of 78 successful digital ecosystems in global mechanical engineering by the MIT Center for Digital Business reveals five architectural core elements present in all successful scaling examples:

1. Connected Products Layer
   • Intelligent, communicating machines and components
   • Standardized connectivity protocols (OPC UA, MQTT, etc.)
   • Edge computing capabilities for local data processing
   • Secure remote maintenance and update mechanisms
2. Digital Backbone
   • Centralized data platform with unified data model
   • Industrial IoT platform with scalable cloud infrastructure
   • Integrated identity and access management
   • Orchestration of microservices and APIs
3. Application Ecosystem
   • Modular applications for different user groups
   • Self-service portals for customers and partners
   • Developer portals with SDK and development environments
   • Marketplace for third-party applications
4. Analytics & AI Layer
   • Real-time processing capabilities for streaming data
   • Machine learning models for predictive maintenance
   • Digital twin integration for simulation and optimization
   • Performance dashboards and business intelligence
5. Governance & Monetization Framework
   • Defined rules for ecosystem participants
   • Transparent revenue sharing models
   • Quality assurance processes for third parties
   • Comprehensive data sovereignty and privacy management

Professor Dr. Elena Müller from the Technical University of Munich emphasizes: “The success factor is not the mere existence of these five pillars, but their seamless integration and consistent alignment with business strategy. Too many mechanical engineers view these architectural elements in isolation and thus miss the full scaling potential.”

Integration Patterns and API Strategies for Maximum Connectivity

The Hyperscale Study 2025 by Accenture identifies three dominant API strategies in successful mechanical engineering ecosystems:

1. Inside-Out Integration: Internal systems (ERP, PLM, MES) are opened to the outside through APIs to integrate partners and customers. This strategy is pursued by 62% of ecosystem champions and reduces integration costs for new participants by an average of 73%.
2. Outside-In Integration: External data and services (suppliers, marketplaces, cloud services) are integrated into one’s own infrastructure. This strategy accelerates time-to-market for new functions by an average of 67%.
3. Federated Integration: A decentralized approach in which various participants provide their own APIs that are interoperable through a common standard. This most advanced strategy is implemented by only 17% of companies but achieves the highest network effects.

Standardized API management is essential and includes:

• API Design: Consistent interfaces based on REST or GraphQL standards
• API Gateway: Central contact point for all integrations
• API Marketplace: Catalog of available interfaces for partners
• API Analytics: Usage and performance monitoring
• API Governance: Versioning, deprecation management, and SLAs

Concrete practical integration examples show impressive results:

By implementing an API-first strategy, the medium-sized machine tool manufacturer Maschinen Müller GmbH has reduced its integration time for customer machines from an average of 14 weeks to 3 days – while simultaneously reducing integration costs by 84%.

Particularly forward-looking: Industry consortia such as the Open Industry 4.0 Alliance or the Industrial Digital Twin Association (IDTA) are developing standardized API specifications that are now supported by over 60% of German mechanical engineers – a crucial step towards overcoming integration barriers.

Data Sovereignty and Security Architecture as a Basic Prerequisite

The European Data Protection Survey 2025 shows an alarming result: 67% of medium-sized mechanical engineers in the DACH region report that concerns about data security and sovereignty are the main obstacle to entering digital ecosystems.

A future-proof security architecture therefore includes the following elements:

1. Data Governance Framework
   • Clear data classification and ownership regulations
   • Transparent data usage agreements
   • Granular access controls at the data field level
   • Compliance with industry and regional standards (GDPR, IDSA, Gaia-X)
2. Multi-Layer Security Concept
   • Zero-trust networking with continuous authentication
   • End-to-end encryption for all data streams
   • Hardware-based security anchors (TPM, Secure Elements)
   • Anomaly detection through AI-supported systems
3. Resilient Infrastructure
   • Geo-redundant data storage with multi-cloud strategy
   • Degradation management for critical functions
   • Air-gap backup concept against ransomware attacks
   • Regular penetration tests and red team exercises

A 2024 study by the German Federal Office for Information Security (BSI) confirms: Mechanical engineering companies that have invested in comprehensive security architectures experience 76% fewer successful cyber attacks and can resume normal operations 82% faster in case of damage.

Particularly noteworthy is the trend towards sovereign cloud solutions: 72% of German mechanical engineers now rely on Gaia-X compliant infrastructures or hybrid operations with local edge components.

Dr. Thomas Wagner, CISO at a leading German mechanical engineering company, summarizes: “Data sovereignty is no longer a mere compliance issue, but a strategic competitive advantage. Our customers increasingly choose partners who can demonstrably guarantee data sovereignty. We see a clear trend towards ‘Security and Sovereignty by Design’.”

The Business Case: Quantifiable Benefits of Digital Ecosystems

ROI Analysis: Investment Costs vs. Long-Term Returns

Developing digital ecosystems requires substantial investment. The Deloitte Digital Manufacturing Study 2025 quantifies the average investment costs for German mechanical engineers:

• Initial investment: 2.1% to 3.8% of annual revenue for the basic infrastructure
• Ongoing costs: 0.7% to 1.5% of annual revenue for operations and continuous development

However, these investments are offset by significant revenue potentials:

The average payback period is 18.3 months, with significant differences depending on the implementation approach:

• Big Bang Implementation: 24.5 months payback period
• Modular, Focused Approach: 14.8 months payback period

Dr. Michael Brandt from the Institute for Production Management and Technology (IPMT) explains: “Successful mechanical engineers start with clearly defined use cases that deliver quick results. These quick wins then finance further stages of ecosystem development. Companies that try to implement the complete ecosystem immediately often fail due to complexity or lose management support because of too-long ROI horizons.”

Particularly impressive: Mechanical engineers with successfully established digital ecosystems achieve an average EBITDA increase of 5.7 percentage points within three years – significantly above the industry average of 1.9 percentage points in the same period.

Scaling Effects and Network Economics in Mechanical Engineering

The transformative power of digital ecosystems lies in their ability to achieve non-linear growth effects. Boston Consulting Group identified three central scaling levers in 2024:

1. Globalization Without Physical Expansion
   • Digital ecosystems enable market entry without local offices
   • Remote monitoring and maintenance reduce on-site personnel requirements
   • Example: A German packaging machinery manufacturer was able to expand into 14 new markets through its digital ecosystem – with only 17% of the costs of a traditional expansion
2. Zero Marginal Cost Services
   • Digital services, once developed, scale without proportional cost increases
   • Machine KPIs, benchmarking, and performance analyses are available to any number of customers without additional effort
   • Example: A medium-sized printing press manufacturer generates 23% of its revenue with digital analytics services at marginal provisioning costs
3. Indirect Network Effects
   • With each new participant, the value of the ecosystem increases for all involved
   • Data volume and quality continuously improve predictive algorithms
   • Example: A leading machine tool manufacturer was able to increase the prediction accuracy of its predictive maintenance solutions from an initial 76% to 94% – solely by expanding the data basis in the growing ecosystem

The Accenture Industry X.0 Study (2025) shows: Every euro invested in ecosystem expansion generates an average return of 4.3 euros in established platforms – compared to 1.7 euros for traditional product investments.

Professor Dr. Julia Schmidt from RWTH Aachen emphasizes: “The beauty of digital ecosystems lies in their self-reinforcing dynamics. Once a critical mass of about 100 active participants and 1,000 connected machines is reached, a self-reinforcing growth cycle begins. However, reaching this point requires strategic patience and consistent commitment from top management.”

New Business Models Through Data Monetization and X-as-a-Service

Digital ecosystems not only revolutionize existing business models but enable completely new revenue sources. The PwC Manufacturing 4.0 Survey 2025 identifies seven emerging business models in mechanical engineering:

1. Equipment-as-a-Service (EaaS)
   • Usage-based billing instead of one-time sales
   • Risk transfer to the manufacturer, but higher customer lifetime value
   • Market penetration 2025: 38% of German mechanical engineers (2023: 17%)
2. Performance-as-a-Service
   • Guaranteed productivity metrics instead of machine sales
   • Billing based on actual produced quantity or quality
   • Market penetration 2025: 22% (2023: 9%)
3. Capacity-as-a-Service
   • Flexible production capacities depending on utilization
   • Billing for actually used capacity
   • Market penetration 2025: 15% (2023: 3%)
4. Data-as-a-Service
   • Sale of aggregated and anonymized operational data
   • Benchmarking and industry analyses as value-added service
   • Market penetration 2025: 28% (2023: 12%)
5. Platform-as-a-Service
   • Provision of development environments for third-party providers
   • Revenue sharing on developed applications
   • Market penetration 2025: 11% (2023: 2%)
6. Knowledge-as-a-Service
   • Automated consulting through AI-supported assistance systems
   • Knowledge transfer and best practice sharing in the ecosystem
   • Market penetration 2025: 19% (2023: 7%)
7. Outcome-as-a-Service
   • Complete takeover of production processes with result guarantee
   • Full risk and responsibility assumption
   • Market penetration 2025: 8% (2023: 1%)

Particularly noteworthy: Mechanical engineers who have implemented X-as-a-Service models show 2.3 times higher customer retention rates and 3.1 times higher customer lifetime value.

The tax and accounting aspects of these new business models, however, require a shift in thinking. While one-time sales generate immediate revenue, subscription models lead to more consistent but initially lower cash flows. The KPMG study “Financial Transformation in Manufacturing” (2024) shows that 64% of mechanical engineers had to adapt their financing and working capital strategy to manage the transition.

Dr. Sarah Müller, CFO of a leading special machine manufacturer, reports: “The transition to as-a-service models was a challenge for our finance department. We not only had to realign our controlling, but also convince investors and banks of the long-term value creation potential. After 24 months, we now see significantly more stable cash flows and a tripling of our company value.”

Technological Enablers for Scalable Mechanical Engineering Ecosystems

From IoT to AIoT: Intelligent Components as Ecosystem Building Blocks

The convergence of IoT and AI has led to the emergence of Artificial Intelligence of Things (AIoT) – a key technology for scalable ecosystems in mechanical engineering. According to the study “AIoT in Manufacturing 2025” by Fraunhofer IOSB, four technological developments are currently shaping this area:

1. Highly Integrated Edge AI Hardware
   • Energy-efficient processors with dedicated Neural Processing Units (NPUs)
   • Real-time inference of complex ML models directly at the machine
   • Example: The latest generation of industrial edge devices handles up to 25 TOPS (Tera Operations Per Second) at under 15 watts of power consumption
2. Federated Learning for Distributed Intelligence
   • Trains AI models decentrally across all connected machines without transferring raw data
   • Solves data protection and bandwidth problems in large machine fleets
   • Example: A leading robotics manufacturer improves motion control algorithms across 12,000 installed systems without centralizing sensitive production data
3. Tiny ML for Resource-Constrained Environments
   • Highly compressed AI models for use in sensors and small controllers
   • Anomaly detection and classification directly at the sensor
   • Example: Intelligent vibration sensors with integrated error classification that work with less than 100 KB of memory space
4. Self-Learning Systems for Autonomous Optimization
   • Reinforcement learning for continuous process improvement
   • Machines optimize their parameters independently based on quality and efficiency metrics
   • Example: Injection molding machines that automatically adapt their process parameters to fluctuating material properties while reducing energy consumption by an average of 23%

The most significant development since 2023 is the transition from isolated smart products to fully networked AIoT ecosystems. The IDC Manufacturing Insights Study 2025 shows: 47% of installed intelligent machines already exchange data and insights with other machines – an increase of 31 percentage points compared to 2023.

Dr. Andreas Weber from the Competence Center Industry 4.0 explains: “The real revolution lies not in the intelligence of individual machines, but in the collective intelligence of networked systems. We observe an emergent optimization capability that goes beyond the sum of individual systems.”

In particular, the integration of Large Language Models (LLMs) into industrial control systems represents a quantum leap. Natural Language Interfaces increasingly enable workers to control complex machine interactions via natural language, reducing training times by an average of 63% and increasing machine availability by 17%.

Cloud-Edge Hybrid Architectures for Global Scaling

The Accenture Cloud First Manufacturing Study 2025 identifies a clear trend: 78% of German mechanical engineers have evolved their initial “Cloud First” strategy to a more nuanced “Cloud Smart” approach. This hybrid approach combines the strengths of cloud and edge computing in a seamless architecture.

The core components of modern cloud-edge hybrid architectures in mechanical engineering:

1. Multi-Tier Edge Computing
   • Device Edge: Directly at sensors and actuators (latency <10ms)
   • Local Edge: At machine or cell level (latency <50ms)
   • Regional Edge: At factory or campus level (latency <100ms)
   • Enables real-time control while connecting to cloud services
2. Sovereignty-Preserving Cloud Strategy
   • Use of European cloud infrastructures (Gaia-X, Sovereign Cloud Stack)
   • Clear data classification with differentiated hosting policies
   • Implementation of confidential computing for sensitive workloads
   • 73% of German mechanical engineers now rely on Sovereign Cloud solutions
3. Event-Driven Architecture
   • Reactive systems with asynchronous communication
   • High resilience through loose coupling
   • Scalability through event-based processing
   • Enables flexible integration of new ecosystem participants
4. Dynamic Workload Placement
   • Intelligent distribution of computing tasks according to requirements
   • Automatic migration between edge and cloud
   • Optimization according to factors such as latency, costs, and data sovereignty
   • Reduces cloud costs by an average of 37% while increasing performance

The Microsoft Azure IoT Manufacturing Study 2025 quantifies the benefits of these hybrid approaches: Mechanical engineers with implemented cloud-edge hybrid architectures show 42% higher data processing speed, 67% lower latency times, and 58% lower data transfer costs compared to pure cloud solutions.

Prof. Dr. Stefan Schulz from TU Darmstadt emphasizes: “The decisive success factor is an intelligent orchestration concept that automatically decides which workloads are processed where. We see a clear correlation between the sophistication of this orchestration concept and the scalability of the overall ecosystem.”

Particularly noteworthy is the trend towards industry-specific cloud solutions: 82% of leading mechanical engineers now rely on specialized Industrial IoT Platforms such as Siemens MindSphere, PTC ThingWorx, or the SAP Business Technology Platform instead of generic hyperscaler offerings – an increase of 34 percentage points since 2023.

Digital Twin and Simulation as Innovation Accelerators

Digital Twins have evolved from isolated virtual models to integrated key components of digital ecosystems. Gartner Digital Twin Analytics 2025 distinguishes four evolutionary stages that exist in parallel in mechanical engineering:

1. Component Twins (Maturity Level 1)
   • Virtual representation of individual components or machines
   • Focus on technical parameters and condition monitoring
   • Implementation level 2025: 87% of mechanical engineers
2. System Twins (Maturity Level 2)
   • Integration of multiple Component Twins into functional systems
   • Mapping of interdependencies and system behavior
   • Implementation level 2025: 58% of mechanical engineers
3. Process Twins (Maturity Level 3)
   • Simulation of complete production processes including material and energy flows
   • Optimization of throughput times and resource efficiency
   • Implementation level 2025: 36% of mechanical engineers
4. Ecosystem Twins (Maturity Level 4)
   • Virtual representation of complete value-creation networks
   • End-to-end optimization across company boundaries
   • Implementation level 2025: 12% of mechanical engineers

The strategic importance of this technology is underscored by investment figures: According to IDC, German mechanical engineers will invest an average of 4.7% of their IT budget in digital twin technologies in 2025 – more than twice as much as in 2023.

Dr. Lisa Chen, CTO of a leading simulation software provider, explains: “Digital Twins have evolved from pure visualization technology to becoming the central innovation driver. In advanced implementations, they function as a virtual laboratory where new business models can be tested with low risk before being rolled out in the real world.”

Concrete use cases demonstrate the transformative potential:

• Virtual Commissioning: Reduces commissioning time for complex systems by an average of 63% and lowers error rates by 78%
• Performance Optimization: AI-supported simulation identifies optimization potentials that increase overall equipment effectiveness (OEE) by an average of 14.2%
• Virtual Product and Service Development: Shortens development cycles by 47% through early virtual validation and error detection
• Scenario Planning: Enables evaluation of different business models and market scenarios with 87% lower costs than physical pilot projects

Particularly forward-looking is the convergence of Digital Twins and Metaverse technologies: 31% of leading mechanical engineers are already experimenting with immersive collaboration environments where teams can work together globally on virtual machines and systems – with measurable efficiency gains of 27% on average for international development projects.

Prof. Dr. Markus Weber from the Virtual Engineering Center summarizes: “We are only at the beginning of a development in which physical and virtual reality merge into a seamless continuum. We see the most promising approaches where Digital Twins serve not only as a reflection of reality but become the primary interaction point for humans and machines.”

International Success Stories: Digital Ecosystem Champions in Mechanical Engineering

Mittelstand Study: How German Mechanical Engineers Scale with Digital Ecosystems

The “Mittelstand Digital Transformation Benchmark Study 2025” by VDMA analyzes 127 German medium-sized mechanical engineering companies that have successfully implemented digital ecosystems. Surprisingly, the biggest success stories come not from industry giants but from agile medium-sized companies between 250 and 1,000 employees.

Case Study 1: Müller Präzisionstechnik GmbH

The family-owned company, founded in 1978 with 380 employees and annual revenue of 87 million euros, has built a comprehensive digital ecosystem for special machine tools within three years:

• Initial Situation (2022): Traditional machine tool manufacturer with 90% revenue from hardware, stagnant growth, and margin pressure
• Transformation Strategy: Consistent conversion to a Performance-as-a-Service model with guaranteed productivity metrics
• Technological Enabler: Proprietary IoT platform with AI-supported process optimization
• Ecosystem Approach: Integration of tool manufacturers, CAM software providers, and material suppliers into a closed value creation network
• Results (2025):
  • Revenue growth from 87 to 142 million euros (+63%)
  • EBIT margin increase from 6.3% to 14.8%
  • Share of recurring revenues: 47% (2022: 8%)
  • Internationalization: Expansion into 7 new markets without physical presence

The CEO, Dr. Thorsten Müller, explains the success: “The key was the insight that we are no longer in the machine building business, but in the business of guaranteeing productive manufacturing hours to our customers. This perspective shift has changed everything – from product development to sales strategy to our financing models.”

Case Study 2: WeCoBot Systems AG

The robotics startup founded in 2015, now with 210 employees, has achieved market disruption in the field of collaborative robotics through its open ecosystem model:

• Initial Situation (2022): Innovative robotics startup with unique technology but limited market reach and resources
• Transformation Strategy: Building an open robotics platform with standardized APIs for third-party developers
• Technological Enabler: Modular robot architecture with open interfaces and SDK
• Ecosystem Approach: Democratization of robotics by involving hundreds of developers creating applications for various industries
• Results (2025):
  • Growth from 24 to 97 million euros in revenue (+304%)
  • Over 650 applications in the app marketplace
  • Community of 12,700 active developers
  • Market leadership in 4 vertical industry segments

Particularly impressive: WeCoBot itself employs only 17 application engineers but can offer industry-specific complete solutions for 28 different industrial sectors through its ecosystem – a perfect example of the leverage effect of digital ecosystems.

Success factors of successful Mittelstand companies according to the VDMA study:

1. Focused niche strategy instead of trying to compete with large corporations
2. Involving early adopter customers as co-innovators
3. Strategic partnerships with complementary technology providers
4. Step-by-step implementation with quick successes
5. Cultural change as a top priority with executives as role models

The Asian Approach: Lessons from High-Growth Markets

The KPMG Global Manufacturing Outlook 2025 analyzes the differences between European and Asian ecosystem strategies in mechanical engineering. Particularly insightful are the case studies from China, Japan, and South Korea, where sometimes radically different approaches are pursued.

Case Study: Yantai Intelligent Manufacturing Corporation (China)

The company founded in 2018 has achieved a dominant market position in the CNC machine tool sector within 7 years through an aggressive ecosystem strategy:

• Strategic Approach: “Hypervertical Integration” – complete control over all value creation stages from components to cloud services
• Ecosystem Model: Closed system with proprietary standards, but highest integration depth
• Financing Model: State-supported consortium of 7 specialized companies
• Go-to-Market: Radical price disruption through cross-subsidization – hardware at cost price, monetization through services
• Results: Market share from 0% to 37% in 7 years, complete displacement of Western providers in the home market

Case Study: Daewoo Precision Collective (South Korea)

A consortium of medium-sized specialists has gained significant market share in the high-tech machine segment through an ecosystem federation:

• Strategic Approach: “Coopetition” – cooperation on platform technologies, competition in applications
• Ecosystem Model: Federated system with shared technology base but individual market development
• Financing Model: Joint R&D company supported by 12 independent companies
• Go-to-Market: Common cloud backbone, differentiated hardware offerings
• Results: Cost savings of 46% in R&D, time-to-market reduction of 61%

Case Study: Omron Industrial Automation Network (Japan)

The established automation specialist has transformed its existing product portfolio through an open ecosystem model:

• Strategic Approach: “Controlled Openness” – defined interfaces for partners, but strict quality control
• Ecosystem Model: Hybrid of proprietary core and open extensions
• Financing Model: Corporate venture capital for ecosystem startups
• Go-to-Market: Industry-specific solution packages with partners
• Results: Doubling of service revenue, 37% higher customer retention rate

Dr. Mei Zhang, Head of the Institute for Industrial Economics in Shanghai, summarizes the differences: “While European mechanical engineers often act too cautiously and incrementally, Asian competitors pursue more disruptive strategies. The willingness to cannibalize established business models and establish radically new price structures is a decisive difference.”

Relevant learning effects from Asian models for European providers:

1. Speed over perfection – Market introduction with MVP approach (Minimum Viable Product)
2. Radical pricing models as a competitive tool – hardware as an enabler, not as a profit center
3. Strategic collaboration instead of isolated competition
4. Strong industrial policy embedding with targeted state support

Ecosystem Metrics: Benchmark Analysis of Top Performers

The Roland Berger Digital Manufacturing Excellence Study 2025 provides detailed benchmark data on digital ecosystems in mechanical engineering. Particularly revealing are the metrics of top-quartile performers compared to the industry average:

Particularly significant: The top performers do not invest more in their digital ecosystems than the industry average (3.2% vs. 3.4% of revenue), yet achieve significantly better results – an indication that success depends less on investment amount than on implementation strategy.

Dr. Robert Fischer, Partner at Roland Berger, explains: “The crucial insight from our study is that the top performers are not simply ‘more digital’ but have a fundamentally different mindset. They don’t view their ecosystem as a technological add-on, but as the core of their business model.”

Three key characteristics distinguish the top performers from the rest:

1. API-First Strategy: APIs are not implemented retrospectively, but are an integral part of the product architecture from the beginning
2. Consistent Product-Service Integration: Hardware, software, and services are conceived and offered as an inseparable unit
3. Data-Driven Decision Making: Comprehensive monitoring of all ecosystem metrics with automated feedback loops for continuous optimization

The long-term perspective is particularly encouraging: Companies that have consistently invested in digital ecosystems for at least three years achieve an average 2.7 times higher value increase (Enterprise Value) than the industry average – a clear signal to investors and capital markets.

The Implementation Roadmap: From Pilot Project to Scaled Ecosystem

The 4-Phase Plan for Gradual Ecosystem Integration

Based on 87 successful digitalization projects in mechanical engineering, Boston Consulting Group has developed a 4-phase model that has proven particularly effective for the Mittelstand:

Phase 1: Connectivity Foundation (3-6 months)

• Goal: Create basic connectivity and data foundation
• Core Activities:
  • IoT enablement of existing product lines
  • Implementation of a central IoT backbone
  • Definition of a unified data architecture
  • Piloting with selected existing customers
• Success Indicators:
  • At least 30% of active installation base connected
  • Dashboard with real-time data from the field
  • First data-based insights into usage patterns

Phase 2: Value Creation (6-12 months)

• Goal: Develop first monetizable services
• Core Activities:
  • Implementation of predictive maintenance functions
  • Building a Customer Success Team
  • Development of first paid value-added services
  • Piloting new pricing models (e.g., pay-per-use)
• Success Indicators:
  • At least 3 productive digital services
  • First service revenues generated
  • Reduction of service calls by at least 20%
  • Measurable increase in customer satisfaction

Phase 3: Ecosystem Expansion (12-24 months)

• Goal: Opening to partners and scaling
• Core Activities:
  • Development of an API strategy and developer portal
  • Onboarding first strategic partners
  • Implementation of marketplace functionalities
  • Building a Partner Success Program
• Success Indicators:
  • At least 10 active ecosystem partners
  • First-party and third-party APIs productive
  • First revenues generated through partners
  • Joint innovation projects with partners

Phase 4: Business Model Transformation (24+ months)

• Goal: Complete transformation to ecosystem player
• Core Activities:
  • Conversion to XaaS business models
  • Development of own digital products
  • International scaling of the ecosystem
  • M&A for ecosystem expansion
• Success Indicators:
  • At least 30% recurring revenues
  • Positive unit economics in service business
  • Growth rate significantly above industry average
  • Significantly increased company valuation

Dr. Christian Meier, Chief Digital Officer of a medium-sized special machine manufacturer, reports: “The phased approach was crucial to our success. We started with a small, focused team and only scaled after initial successes. This allowed us to continually convince the board with positive results and secure the necessary investments for the next phase.”

Notable: 82% of failed ecosystem initiatives tried to implement Phase 3 or 4 without having solidly established the foundations in Phases 1 and 2.

Resource Planning and Critical Success Factors

The PwC Digital Manufacturing Study 2025 quantifies the resource requirements for successful ecosystem implementations in mechanical engineering. Surprisingly, the technology portion is significantly lower than often assumed:

Typical resource allocation of successful projects:

• Technology platform and infrastructure: 28%
• Application and service development: 23%
• Change management and organizational development: 22%
• Partner management and ecosystem building: 17%
• Data analysis and AI: 10%

Personnel resources over time (for a typical mechanical engineer with 500 employees):

Dr. Julia Bergmann, Digital Board Member of a medium-sized mechanical engineering company, shares her experiences: “The most common mistake is underestimating the non-technical components. Initially, we invested 80% of our budget in technology and only 20% in people and processes. Today, we would do exactly the opposite.”

The Deloitte Manufacturing Digital Maturity Study 2025 identifies seven critical success factors that were present in over 90% of successful implementations:

1. Executive Sponsorship: Active and continuous support from the CEO/Managing Director
2. Dedicated Transformation Team: Interdisciplinary team with direct reporting structure to management
3. Customer-Centric Approach: Consistent focus on measurable customer value
4. Data-Driven Culture: Establishment of a data-based decision-making culture
5. Agile Governance Model: Flexible control processes for quick adjustments
6. Skills Development Program: Systematic building of digital competencies
7. Balanced KPI Framework: Balanced metrics system with short- and long-term metrics

Particularly revealing is the analysis of reasons for failed transformation projects:

Prof. Dr. Martin Schulz from WHU – Otto Beisheim School of Management summarizes: “Implementing digital ecosystems is 20% a technological challenge and 80% a question of organizational transformation. Companies that don’t understand this ratio will fail with high probability – regardless of their technological maturity level.”

Strategic Partner Selection to Accelerate Time-to-Market

Strategic partner selection significantly determines the speed and success of ecosystem implementation. The Capgemini Invent Manufacturing Partnership Study 2025 identifies five key partner types for successful ecosystems in mechanical engineering:

1. Technology Platform Providers
   • Role: Providing the technological foundation (IIoT platform, cloud infrastructure)
   • Typical Partners: Microsoft Azure IoT, AWS IoT, Siemens MindSphere, PTC ThingWorx
   • Selection Criteria: Industry-specific functions, scalability, data sovereignty
   • Case Example: A medium-sized vacuum pump manufacturer reduced its time-to-market for a digital service offering from 24 to 9 months through partnership with PTC
2. Connectivity & Edge Specialists
   • Role: Secure connection of the installed base, edge computing solutions
   • Typical Partners: HMS Networks, Eurotech, Dell Edge, INSYS icom
   • Selection Criteria: Industry experience, security standards, global availability
   • Case Example: A machine tool manufacturer achieved 78% connectivity level in its installed machine base within 6 months through partnership with a connectivity specialist
3. Analytics & AI Partners
   • Role: Advanced data analysis, predictive maintenance models, process optimization
   • Typical Partners: SAS, Dataiku, specialized AI startups
   • Selection Criteria: Industry-specific AI models, interpretability, adaptability
   • Case Example: A printing press manufacturer increased waste reduction for customers by 37% through AI-based process optimization
4. Vertical Industry Partners
   • Role: Industry-specific know-how, access to customer applications
   • Typical Partners: Industry leaders in customer industries, material suppliers, tool manufacturers
   • Selection Criteria: Market reach, complementary strengths, cultural fit
   • Case Example: A packaging machinery manufacturer accessed a completely new market segment with 28% higher margins through partnership with a food processor
5. Implementation & Service Partners
   • Role: Global implementation, customer support, local presence
   • Typical Partners: System integrators, value-added resellers, local service providers
   • Selection Criteria: Global reach, technical know-how, customer proximity
   • Case Example: A special machine manufacturer reduced response time for service cases by 76% while reducing costs by 42% through a global service partner network

The Capgemini study clearly shows: The most successful ecosystem strategies rely on complementary partners rather than isolated in-house implementation. Companies that cover more than 40% of their ecosystem functionality through partners achieve 2.3 times higher time-to-market speed and 3.1 times lower implementation costs.

Dr. Andreas Schmidt, Head of Digitalization at a leading printing press manufacturer, shares his experiences: “The decisive moment in our transformation was the realization that we don’t have to develop everything ourselves. Through strategic partnerships, we were able to concentrate our resources on areas where we can really differentiate.”

Particularly successful are hybrid partnership models that combine different forms of integration:

• White-label integrations: Partner technologies offered under own brand (68% of successful ecosystems)
• Co-branding models: Joint market presence with strong partners (54%)
• Revenue-sharing models: Revenue participation instead of classic licensing (41%)
• Joint ventures: Joint company founding for strategic initiatives (24%)

Dr. Martina Fischer, Partner at Capgemini Invent, summarizes: “The ability to orchestrate effective partnerships is developing into a core competence of successful mechanical engineers. We see a clear trend away from the ‘not-invented-here syndrome’ towards a pragmatic ‘best-of-breed’ strategy.”

Risk Management and Challenges of Digital Transformations

The 7 Most Common Pitfalls in Ecosystem Development

The McKinsey Digital Manufacturing Risk Study 2025 analyzes 174 digital transformation projects in mechanical engineering and identifies seven critical risk factors that were present in over 80% of failed projects:

1. Strategic Incoherence
   • Symptom: Digital initiatives without clear reference to corporate strategy
   • Frequency: 74% of failed projects
   • Early warning signal: Parallel, uncoordinated digital projects without overarching narrative
   • Countermeasure: Executive alignment workshop and clear target vision definition before project start
2. Digital-Physical Disconnect
   • Symptom: Decoupling of digital initiatives from core business
   • Frequency: 68% of failed projects
   • Early warning signal: Digital services not co-sold by core product sales
   • Countermeasure: Integrated product-service strategies with shared KPIs
3. Legacy System Paralysis
   • Symptom: Incompatible legacy systems blocking transformation
   • Frequency: 63% of failed projects
   • Early warning signal: More than 60% of IT budget flows into maintenance
   • Countermeasure: Bimodal IT strategy with separation of innovation and legacy
4. Skills Gap Trap
   • Symptom: Lack of digital competencies slowing progress
   • Frequency: 59% of failed projects
   • Early warning signal: High dependency on external consultants for core technologies
   • Countermeasure: Strategic competency building with “Digital Lighthouse Teams”
5. Data Quality Dilemma
   • Symptom: Insufficient data quality preventing value-creating analyses
   • Frequency: 57% of failed projects
   • Early warning signal: Data scientists spend >70% of their time on data cleaning
   • Countermeasure: Early implementation of a data governance framework
6. Cultural Resistance
   • Symptom: Rejection of new ways of working and business models
   • Frequency: 54% of failed projects
   • Early warning signal: Employees don’t use new tools or bypass digital processes
   • Countermeasure: Change management with clear WIIFM communication (“What’s In It For Me”)
7. Perfectionism Trap
   • Symptom: Over-engineering and delay due to excessive requirements
   • Frequency: 51% of failed projects
   • Early warning signal: Production launches repeatedly postponed
   • Countermeasure: Agile methodology with MVP approach and iterative improvement

Dr. Sarah Weber, who experienced three failed digital projects and one successful transformation, shares her experiences: “The key is to achieve small successes early and communicate them broadly. In our failed project, we worked for two years on the ‘perfect solution’ – but by the time it was finally ready, the world had moved on.”

The Accenture Digital Resilience Study 2025 adds: Companies that implement a dedicated risk management methodology for their digital transformation show a 3.4 times higher success rate and 2.7 times lower budget and time overruns.

Particularly effective: The use of Digital Transformation Health Checks with a standardized set of risk early indicators. 89% of successful transformations conduct such assessments at least quarterly – compared to only 14% of failed initiatives.

Data Sovereignty and Legal Framework Conditions (EU AI Act & Co)

The regulatory landscape for digital ecosystems in mechanical engineering has changed dramatically since 2023. The Deloitte Regulatory Impact Study 2025 identifies four central regulatory clusters that mechanical engineers must consider:

1. EU AI Act (in force since 2024)
   • Core impacts: Risk-based classification of AI systems, extensive transparency and documentation obligations
   • Particular relevance for mechanical engineering: Predictive maintenance, autonomous systems, quality control
   • Compliance effort: High (3-7% of AI implementation costs)
   • Practical example: A leading robot manufacturer had to completely reconstruct its AI-supported collision avoidance system to meet the requirements for transparency and explainability
2. European Data Act (in force since 2023)
   • Core impacts: Users’ right to access their machine data, obligation for data portability
   • Particular relevance for mechanical engineering: IoT platforms, digital twins, performance monitoring
   • Compliance effort: Medium (2-5% of platform implementation costs)
   • Practical example: A machine tool manufacturer had to restructure its entire data model to cleanly separate customer data from its own analytical data
3. EU Cyber Resilience Act (in force since 2024)
   • Core impacts: Mandatory security standards for connected products, regular security updates
   • Particular relevance for mechanical engineering: All connected machines and components
   • Compliance effort: High (5-9% of product development costs)
   • Practical example: A manufacturer of automation components had to redesign its entire development process according to security-by-design principles
4. Digital Services & Digital Markets Act (in force since 2023)
   • Core impacts: Special obligations for platform operators, prevention of lock-in effects
   • Particular relevance for mechanical engineering: Digital marketplaces, app stores for industrial applications
   • Compliance effort: Low to medium (1-3% of platform operating costs)
   • Practical example: A provider of an industrial IoT platform had to design its pricing model for third-party providers in a transparent and non-discriminatory way

Dr. Thomas Weber, Head of Digital Compliance at a leading German mechanical engineering company, explains: “Regulation has become a decisive design factor for digital ecosystems. Those who only consider it retrospectively risk costly redesigns or even market access restrictions.”

The BSI study “Digital Resilience in Mechanical Engineering 2025” shows: Companies that consider compliance already in the conception phase incur 67% lower implementation costs for regulatory requirements than those that have to adapt subsequently.

Particularly forward-looking: The trend toward “Compliance-as-a-Service” within digital ecosystems. Leading platform operators now offer standardized compliance modules that make it easier for smaller ecosystem participants to comply with regulatory requirements – another network effect of established ecosystems.

Safeguarding Strategies Against Platform Dependency and Vendor Lock-in

The growing importance of digital ecosystems also brings new strategic risks, particularly the danger of one-sided dependencies. The Forrester Vendor Strategy Report 2025 identifies four central safeguarding strategies implemented by leading mechanical engineers:

1. Multi-Platform Strategy
   • Approach: Parallel use of multiple platform providers for critical functions
   • Implementation level: 42% of Tier-1 mechanical engineers
   • Advantages: Highest negotiating power, maximum flexibility
   • Disadvantages: High complexity, increased integration costs
   • Application example: A leading machine tool manufacturer runs applications in parallel on Azure IoT and AWS IoT to minimize dependencies
2. Abstraction Layer Approach
   • Approach: Implementation of own middleware to decouple from base platforms
   • Implementation level: 56% of Tier-1 mechanical engineers
   • Advantages: Good balance of flexibility and effort, easier migration
   • Disadvantages: Additional complexity level, potential performance losses
   • Application example: A packaging machinery manufacturer has developed its own abstraction layer that would enable a platform change within 3 months
3. Open-Source-First Strategy
   • Approach: Prioritization of open standards and open-source components
   • Implementation level: 37% of Tier-1 mechanical engineers
   • Advantages: Maximum independence, broad ecosystem
   • Disadvantages: Higher integration effort, less commercial support
   • Application example: A medium-sized special machine builder builds its entire digital ecosystem on the Eclipse IoT Stack (Ditto, Hono, etc.)
4. Strategic Partnerships with Exit Clauses
   • Approach: Deep integration with selected partners, but contractually secured exit scenarios
   • Implementation level: 74% of Tier-1 mechanical engineers
   • Advantages: Simple implementation, clear responsibilities
   • Disadvantages: Limited negotiating power, de facto lock-ins despite legal safeguards
   • Application example: A printing press manufacturer has agreed detailed exit management plans with its IoT platform provider, including data transfer and transition services

Particularly relevant for the Mittelstand: The Gartner Vendor Risk Study 2025 shows that medium-sized mechanical engineers often underestimate the danger of long-term dependencies. 67% of the companies studied have no formal strategy against vendor lock-in – and 43% have difficulties evaluating their existing platform partnerships.

Dr. Michael Berger from Munich University of Applied Sciences emphasizes: “The real danger lies not in the dependency itself, but in entering into this dependency unconsciously. Many medium-sized mechanical engineers only realize after years how deeply they have tied themselves to individual technology partners – often with significant financial consequences.”

Concrete measures to safeguard against vendor lock-in:

• Data sovereignty strategy: Legally secured guarantees for data ownership and portability
• API-first development: Consistent decoupling of frontend and backend
• Containerization: Use of standardized container technologies for maximum portability
• Source code escrow: Deposit of critical code base with neutral third parties
• Contract design: Exit clauses with concrete transition scenarios and SLAs

The PwC Digital Risk Study 2025 quantifies the benefits of forward-looking risk management: Mechanical engineers with implemented anti-lock-in strategies reduce their technology-dependent license costs by an average of 32% and achieve 47% better terms on contract renewals.

Future Perspectives: Mechanical Engineering in the Digital Ecosystem 2030

Emerging Technologies and Their Disruptive Potential

The Gartner Emerging Technology Hype Cycle for Manufacturing 2025 identifies eight technologies that will have transformative impacts on digital ecosystems in mechanical engineering by 2030:

1. Spatial Computing & Extended Reality
   • Disruption potential: Fundamentally changes human-machine interaction
   • Core relevance for mechanical engineering: Remote maintenance, assembly, training, design collaboration
   • Maturity level 2025: Early adoption (7-12% market penetration)
   • Expected market maturity: 2027-2028
   • Pioneer example: A German special machine manufacturer reduces service response time by 78% and technician travel activity by 64% through AR-supported remote maintenance
2. Quantum Computing for Industrial Optimization
   • Disruption potential: Enables breakthroughs in materials science and optimization problems
   • Core relevance for mechanical engineering: Product development, manufacturing optimization, supply chain planning
   • Maturity level 2025: Experiments and pilots (2-3% market penetration)
   • Expected market maturity: 2028-2030
   • Pioneer example: A leading automation specialist uses quantum computing for topology optimization of complex components and achieves 37% weight reduction with simultaneous performance increase
3. Autonomous Systems and Swarm Intelligence
   • Disruption potential: Self-organizing machines revolutionize manufacturing concepts
   • Core relevance for mechanical engineering: Flexible manufacturing, autonomous intralogistics, adaptive production systems
   • Maturity level 2025: Early applications (5-8% market penetration)
   • Expected market maturity: 2026-2028
   • Pioneer example: A packaging machinery manufacturer implements self-organizing module concepts that adapt to new product formats without programming
4. Edge AI and Neuromorphic Computing
   • Disruption potential: Brings human-like cognitive capabilities to machines
   • Core relevance for mechanical engineering: Real-time analysis, autonomous decision-making, energy-efficient AI
   • Maturity level 2025: Early adoption (9-14% market penetration)
   • Expected market maturity: 2026-2027
   • Pioneer example: A manufacturer of quality assurance systems uses neuromorphic chips for ultra-fast visual inspection with 90% lower energy consumption
5. Digital Agents and Robotics Process Automation 2.0
   • Disruption potential: Automates complex cognitive processes in development and service
   • Core relevance for mechanical engineering: Design, customer support, predictive engineering
   • Maturity level 2025: Early applications (10-15% market penetration)
   • Expected market maturity: 2026-2027
   • Pioneer example: A machine tool manufacturer automates 73% of its CAD design processes through AI agents that learn from historical designs
6. 6G and Network-as-a-Service
   • Disruption potential: Enables true real-time networking with guaranteed latency times
   • Core relevance for mechanical engineering: Precision control, distributed systems, remote operations
   • Maturity level 2025: First test environments (1-2% market penetration)
   • Expected market maturity: 2028-2029
   • Pioneer example: A precision machinery manufacturer is experimenting with 6G campus networks for sub-millisecond-precise synchronization of distributed robot cells
7. Synthetic Data for Machine Learning
   • Disruption potential: Overcomes data shortage for rare error conditions
   • Core relevance for mechanical engineering: Quality assurance, predictive maintenance, anomaly detection
   • Maturity level 2025: Growing adoption (15-20% market penetration)
   • Expected market maturity: 2025-2026
   • Pioneer example: A manufacturer of die-casting machines trains AI models with synthetic error data and increases the detection rate of rare defects by 213%
8. Post-Quantum Cryptography for Industrial Security
   • Disruption potential: Protects industrial systems against quantum computer-based attacks
   • Core relevance for mechanical engineering: Long-lived industrial equipment, IPR protection, supply chain security
   • Maturity level 2025: Beginning implementation (5-7% market penetration)
   • Expected market maturity: 2026-2027
   • Pioneer example: A manufacturer of security systems for critical infrastructure implements quantum-resistant algorithms for machines with 20+ years of lifecycle

Dr. Andreas Schmidt from the Digital Manufacturing Lab emphasizes: “The real revolution lies not in the individual technologies, but in their convergence. When autonomous systems, edge AI, and spatial computing come together, entirely new paradigms of industrial production and value creation emerge.”

The McKinsey Future of Manufacturing Study 2025 quantifies the disruption potential: Mechanical engineering companies that invest early in emerging technologies achieve EBITDA margins that are 3.7 percentage points higher on average and 2.3 times higher revenue growth than late adopters.

From Vertical to Horizontal Ecosystems: Industry Convergence

A fundamental paradigm shift is emerging in the structure of digital ecosystems. The Accenture Industry Convergence Study 2025 identifies a clear trend from vertical, industry-specific ecosystems toward horizontal, cross-industry value creation networks.

Phase 1: Vertical Ecosystems (Status Quo 2025)

• Characteristics: Industry-focused platforms within traditional value chains
• Example: IoT platforms for specific machine types or manufacturing processes
• Implementation level: 74% of active digital ecosystems in mechanical engineering
• Limitation: Limited scalability, data exchange primarily within known value chains

Phase 2: Cross-Industry Ecosystems (Emergent 2025-2027)

• Characteristics: Connection of complementary industries with overlapping customer groups
• Example: Integration of manufacturing equipment, material suppliers, and logistics service providers
• Implementation level: 21% of active digital ecosystems in mechanical engineering
• Added value: Holistic process optimization, supply chain integration, new business models at industry interfaces

Phase 3: Horizontal Meta-Ecosystems (Predicted 2027-2030)

• Characteristics: Industry-independent platforms for universal capability layers
• Example: Open platforms for sustainability, circular economy, or decentralized production
• Implementation level: 5% of active digital ecosystems in mechanical engineering (pioneers)
• Disruption potential: Fundamental reordering of industry boundaries and value distribution

Particularly interesting examples of emergent cross-industry ecosystems:

1. Circular Economy Platforms
   • Integrate mechanical engineering, recycling technology, material tracking, and product design
   • Example: The European Circular Manufacturing Consortium connects 37 companies from 8 industries
   • Added value: 34% higher material efficiency, 28% reduced compliance costs, new revenue streams
2. Digital Manufacturing Networks
   • Connect machine capacities, design software, material suppliers, and logistics
   • Example: The Manufacturing-as-a-Service platform of a German machine tool manufacturer
   • Added value: 87% higher machine utilization, 63% faster production ramp-up times
3. Sustainable Value Chain Platforms
   • Integrate CO2 tracking, energy management, sustainable materials, and certification
   • Example: The Net-Zero Manufacturing Ecosystem with over 120 partners
   • Added value: 42% lower costs for sustainability compliance, competitive advantages through verified climate neutrality

Dr. Julia Schmidt, Professor for Digital Ecosystems, explains: “The true disruption comes through horizontal ecosystems that transcend traditional industry boundaries. We already see that the most innovative business models emerge at the interface of different industries.”

The BCG Cross-Industry Ecosystem Study 2025 shows: Mechanical engineers who actively participate in cross-industry ecosystems tap into an average of 3.2 new revenue streams and achieve a 2.7 times higher innovation rate than companies that remain in vertical systems.

Particularly noteworthy: The trend toward “Ecosystem of Ecosystems” – meta-platforms that connect various specialized ecosystems through uniform data standards and interoperability protocols. The European Gaia-X initiative and the Industrial Digital Twin Association (IDTA) are early examples of this approach, which could become the dominant model by 2030.

The Action Plan for Decision-Makers: Concrete Next Steps

The Bain & Company Executive Decision Framework Study 2025 offers a practice-oriented action plan for decision-makers in mechanical engineering, structured by company size and digital maturity level:

For Digital Beginners (Digital Maturity Level 1-2)

1. Within the next 3 months:
   • Conduct a Digital Maturity Assessment with external support
   • Identify 2-3 concrete use cases with quick ROI
   • Build a small, cross-functional Digital Innovation Team (3-5 people)
2. Within the next 6 months:
   • Implement a cloud-based IoT base platform
   • Pilot the identified use cases with 5-10 key customers
   • Develop a Digital Capability Roadmap for the next 24 months
3. Within the next 12 months:
   • Roll out the first digital services to existing customer base
   • Build basic data analysis capabilities
   • Systematically evaluate strategic technology partners

For Digital Intermediates (Digital Maturity Level 3-4)

1. Within the next 3 months:
   • Assess existing platform architecture for scalability
   • Define an API strategy for ecosystem expansion
   • Establish a Digital Business Development Team for new business models
2. Within the next 6 months:
   • Implement a partner onboarding process
   • Develop first data-centric business models
   • Pilot XaaS offerings with selected customers
3. Within the next 12 months:
   • Launch a developer portal for third-party providers
   • Internationalize the digital offerings
   • Build a Digital Venture Board for disruptive innovations

For Digital Pioneers (Digital Maturity Level 5)

1. Within the next 3 months:
   • Evaluate cross-industry ecosystem opportunities
   • Develop a data monetization strategy
   • Identify potential M&A targets for ecosystem expansion
2. Within the next 6 months:
   • Pilot cross-industry ecosystem initiatives
   • Implement predictive business models
   • Evaluate disruptive technologies for next-generation ecosystem
3. Within the next 12 months:
   • Establish an Open Innovation Hub for ecosystem expansion
   • Found dedicated venture units for new business areas
   • Actively shape industry standards and regulation

Dr. Michael Weber, Digital Transformation Officer of a medium-sized mechanical engineering company, shares his experience: “The most important advice I can give: Start small, but think big. Begin with a focused pilot that brings quick successes, but develop in parallel an ambitious vision of where the journey should go long-term.”

The BCG Digital Transformation Survey 2025 underscores the importance of this approach: 73% of successful transformations began with small, dedicated teams and clearly defined use cases – compared to only 14% of failed initiatives.

Particularly critical for success: The right governance structure. The McKinsey Agile Organization Study 2025 shows that implementing a bimodal organizational structure – with fast, agile digital teams parallel to the established organization – increases the probability of success of digital transformations by 2.7 times.

5 universal success principles for every development stage:

1. Customer-Centricity: Every initiative must address concrete customer problems
2. Incremental Implementation: Rapid prototyping and MVPs instead of big-bang approaches
3. Data-First Mentality: Systematic data strategy as the foundation for all initiatives
4. Culture of Continuous Learning: Mistakes as learning opportunities, not as failure
5. Executive Sponsorship: Active and visible support from the leadership level

The PwC Digital Leadership Study 2025 comes to an encouraging conclusion: While only 27% of mechanical engineers rated their digital transformation as successful in 2023, this figure has risen to 43% by 2025 – a clear sign that the industry is increasingly mastering the success factors of digital ecosystems.

Conclusion: The Path to a Scalable Digital Ecosystem in Mechanical Engineering

The transformation from a product-centric mechanical engineer to an orchestrator of a digital ecosystem is no longer a luxury but a vital necessity. The numbers tell a clear story: Companies with established digital ecosystems achieve 63% higher growth on average, 5.7 percentage points higher EBITDA margins, and 3.1 times higher customer retention rates.

The key to success lies not in isolated digital initiatives but in a holistic transformation approach that equally encompasses technology, organization, and business models. The most important success factors:

• A clear digital strategy with measurable business goals
• An open, scalable technology architecture
• The systematic development of digital competencies
• A consistent focus on customer value
• A step-by-step but consistent implementation approach

Particularly remarkable: The democratization of digital ecosystems. What was reserved for large corporations just a few years ago is now achievable for medium-sized mechanical engineers through cloud technologies, low-code platforms, and specialized technology partners.

The time to act is now. Market consolidation in mechanical engineering is accelerating, and the gap between digital champions and laggards is growing exponentially. As Dr. Thomas Müller, CEO of a leading mechanical engineering company, puts it: “In five years, there will be two types of mechanical engineers: those who successfully orchestrate digital ecosystems – and those who are part of someone else’s ecosystem.”

The good news: The path to the digital ecosystem is clearly mapped, the success factors are known, and numerous examples prove the feasibility even for medium-sized companies. The only remaining question is: Will you be among the shapers or the participants of the digital transformation?

Want to know how to build your own digital ecosystem? Contact our experts for individual consultation and learn how Brixon Group can support you on your journey to a scalable platform business.

FAQs on Digital Ecosystems in Mechanical Engineering