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The three-year Arrowhead fPVN project (Flexible Production Value Networks) funded by EU Chips JU and Business Finland ended at the end of August 2026. The project brought together nearly 40 organizations from 12 countries to improve productivity in production value networks. For THTH it has been the most important shared development theme of recent years.

Finnish project participants from among THTH members were Stora Enso, AFRY, Valmet Flow Control, Endress+Hauser, Semantum, Cadmatic, VTT, Tampere University and Aalto University. The work of the Finnish parties focused on pilot use cases within the process industry, with Stora Enso acting as the unifying entity. Some Finnish companies also participated in a similar use case involving Stora Enso in Sweden, which focused on an industrial digital twin (see the description below).

At the end of September, Stora Enso (Finland), AFRY, Valmet and THTH held a final review of the pilot demonstrations for the investment project and supplier service use cases. Participants agreed that the implementations had their challenges, but the results opened their eyes to new ways of putting digitalization into practice across the plant lifecycle.

Key results and observations from the Finnish pilots

  1. Interoperable data models. The project’s Norwegian-led work package reviewed and tested major standards for use across industries, including ISO 23726 (Ontology Based Interoperability and the Industrial Data Ontology, IDO), ISO 15926, ISO 10303 (STEP), DEXPI, CFIHOS and SysML v2. Ontologies based on these standards and shared across the business ecosystem make data interoperable over the whole plant lifecycle. Such structured data is also the foundation for using AI. A PCA RDL service agreement is now in place for THTH and SEIIA members in Finland and Sweden, allowing members to manage and share industrial terminology and reference data in a consistent, machine-readable way across organizations and systems.

  2. Information Exchange Platform (IEP) concept. An IEP is a collaboration platform managed by an owner-operator. It lets owner-operators, engineering companies and suppliers share technical information effectively. The use cases used Sharecat as the pilot IEP. Today’s solutions are built on legacy tools, and future ones are expected to move towards semantic technologies such as RDF and graph databases.

  3. Digital business processes - Purchase-to-Pay automation. The demonstrations linked the exchange of technical information through the IEP to commercial processes based on the Peppol standard – two processes that are currently separate and manual became one automated flow. Stora Enso sent an order, and Valmet returned an order response as Peppol messages, followed by delivery of equipment data and automatic invoicing. This was shown both for a new investment project and for a maintenance or overhaul service case. Automatically integrating the exchange of technical data into the workflow via IEP offers significant benefits to the supplier as well—an outcome Valmet also anticipated from the pilot project.

  4. Lessons from engineering data. In the pilot use cases, AFRY acted as an engineering consultant for Stora Enso, preparing functional and other requirements for the equipment to be procured. AFRY opened access to its detailed engineering data and 3D models for the pilot via APIs and mapped the data to CFIHOS and DEXPI specifications. Owner-operators’ capabilities to manage engineering and design information vary a lot between companies and even individual sites, which was also the situation regarding the pilot. The main lessons were
    • Engineering data should be delivered in agreed standard formats, so that it does not depend on which IEP technology the customer uses.
    • Asset hierarchies vary widely between owner-operators, and even between sites of the same company, which makes direct integration harder.
    • An asset often gets its identity (Tag ID) during the investment project, so that unambiguous identity needs to reach every party’s systems early.
    • As working methods shift from document-centric information exchange to data-centric exchange, projects must agree on whether the deliverables will be documents, datasets, or both.
    • Dependability information is also part of engineering and design information.
  5. Digital Product Passport (DPP) as a driver. With the upcoming DPP regulation, the use of product type and serial number identifiers—previously created by manufacturers using their own methods—will shift to a standardized approach for generating and managing identifiers as defined by DPP standards. Over time this will reduce the work of harmonizing material items at plants and allow each piece of equipment to be tracked individually from commissioning onwards.

Industrial Digital Twin maintenance workflow – the Swedish part

The Swedish Stora Enso pilot demonstrated a complete Digital Twin maintenance process spanning operations, maintenance and engineering. A fault detected by automation system sensor data automatically generated a maintenance request, which was linked through semantic technologies to engineering, operational and asset lifecycle information. Engineers could investigate the issue using a 3D Digital Twin environment, understand system topology and maintenance history, identify replacement alternatives and update maintenance work orders. Once the maintenance activity was completed, the asset history and Digital Twin were automatically updated, demonstrating a closed-loop lifecycle process enabled by semantic interoperability and knowledge graphs.

Next steps

THTH will continue the work on a development roadmap agreed within the business ecosystem, together with its Nordic and international partners (PCA, SEIIA, DEXPI, CFIHOS/IOGP, USPI and DDCC). Planned activities include:

  • continuing to take part in national and international standards development
  • participating in the implementation, development, and adaptation of PCA RDL services to meet the needs of Finnish companies (together with SEIIA and PCA)
  • joining the Implementor Forum for Industrial Semantic Interoperability
  • preparing new national and international follow-up projects, extending the scope to construction engineering information, OT data and industrial digital twins
  • promoting the adoption of AI by improving the quality, interoperability, and semantic consistency of industrial data, enabling AI-driven engineering, operations, and decision-making applications across the process industry

Let’s redefine information management together! Join the digital transformation of the process industry!