Overview

A practical guide to understanding collaborative asset management (Co-AM) and using asset lifecycle data for shared value creation.

TipColife vision

Business partners collaborate equally in and over phases of the lifecycle of an industrial plant. They promote common benefits including environmental, social, and economic factors. This is facilitated by fair data sharing and new operating practices.

NoteAbout the Colife project

The Colife research focused on Data Economy, Digitalisation, and Asset Management, exploring the business potential when combining these competencies.

This material presents the results of the COLIFE Co-Research project. Through close collaboration between industry and research partners, COLIFE established practical foundations for data sharing, collaborative asset management, and data-driven value creation. The authors sincerely thank all participating organizations, particularly Business Finland, for their valuable contributions and support.

Key takeaways in the playbook sections

Key takeaways – Review the basics

  • This Playbook first establishes a common understanding of collaborative asset management (Co-AM) so that all actors (asset owners, OEMs, service providers, etc.) operate with aligned terminology, logic, and expectations.
    • A common conceptual foundation is essential for Co-AM.
    • Value creation depends on understanding assets and data across the full lifecycle and system hierarchy.
    • Effective AM increasingly depends on different types of shared lifecycle data.
    • Standards provide the common reference needed for interoperability and trust.

Key takeaways – Towards Co-AM environment

  • The playbook introduces the concept and main principles of Co-AM, and what changes when transitioning towards Co-AM.
    • AM shifts from single-organization to ecosystem-level coordination.
    • Successful Co-AM requires aligned asset management systems, shared governance, interoperable data, common risks and KPIs, and coordinated lifecycle responsibilities.
    • Compared with traditional AM, more collaboration is required for value creation in Co-AM.
    • AM ecosystems involve diverse stakeholders, such as asset owners, OEMs, maintenance providers, technology partners, service providers, and ecosystem coordinators.
    • Principal elements of Co-AM include data availability and quality, systems interoperability, data security and trust, people and culture, value creation from data, and business renewal.

Key takeaways – Examine Co-AM in your context

  • The Playbook assesses how Co-AM applies to your organization by identifying context-specific opportunities, challenges, and readiness using structured elements and checklists.
    • Co-AM should be evaluated in your own operational and ecosystem context; it is not universally applicable for all contexts.
    • Context analysis is systematic and based on defined dimensions and structured reflection.
    • Understanding Co-AM is not enough; you should identify gaps between potential and current capabilities.
    • Co-AM should be evaluated holistically from the perspectives of business value, governance, data quality, and interoperability.
    • The checklist acts as a practical tool for assessing readiness and guiding next steps.

Key takeaways – Identify your business opportunities

  • Translate shared asset data and collaboration into concrete value creation opportunities in your business.
    • Data alone does not create value -> value emerges when data is linked with decisions, operations and offering.
    • Sustainable data-driven business demands the alignment of organizational readiness and ecosystem dynamics​
    • Focus on specific use cases instead of just data-sharing.
    • Define a clear value proposition and ecosystem logic, not just internal benefits.
    • Business opportunities must be viable for multiple actors or collaboration may fail.

Key takeaways – Develop your data governance capabilities

  • Build the governance, quality/interoperability, and technological capabilities required to securely and effectively share and utilize data across stakeholders.
    • Governance is essential for trust and collaboration.
    • Data ownership and rights must be explicitly defined.
    • Effective ecosystems require common data-sharing rules and agreed practices.
    • Data sharing requires sufficient inter-organizational trust.
    • Governance models must be scalable.
    • Contracts should enable value creation, not only risk minimization.
    • Governance is tightly linked to business and data capabilities.

Key takeaways – Examine examples

  • Learn from practical cases (e.g., upgrade project, federated learning) to understand how Co-AM can be implemented in real-world settings.
    • Successful Co-AM in the upgrade project requires shared project data environment and standardized data practices.
      • In the upgrade project case, success depends on strong stakeholder involvement, shared platforms, standardization, and trust-based collaboration, and overcoming structural challenges such as data quality, inconsistent identifiers, outdated baseline data, integration gaps, and late data delivery.
    • Federated Learning (FL) enables collaboration without sharing raw data and is well suited for distributed asset lifecycle ecosystems, enabling learning from fragmented data sources.
      • It supports privacy-preserving collaboration. However, it does not eliminate privacy, security, or compliance issues.
      • FL feasibility depends on clear joint value from shared learning (Business), agreements on model ownership, roles, liabilities (Governance), harmonized identifiers, usable data (Data quality & interoperability) and local compute, secure communication, orchestration (Technical).