Improve asset lifecycle management by creating and integrating interoperable information models

The objective of this project was to model digital assets and demonstrate seamless collaboration between engineering and operator companies using standardized asset information models. The project aimed to automate data exchange, reduce manual input and enhance efficiency. The Proof of Concept delivered in December 2022 showcased significant improvements in reducing human errors and operational costs.

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Introduction

The project sought to improve asset lifecycle management by creating and integrating interoperable information models. The challenge was to enable different engineering and operating companies to work together seamlessly, using standardized information models to ensure consistency and accuracy.

Problem statement: the need for more efficient exchange of asset information

Engineering and operator companies often face challenges when integrating various parts of a model due to different formats, tools and processes. This lack of standardization leads to inefficiencies, manual errors and increased operational costs. The need for a unified approach to manage and exchange asset information is critical.

Figure 1 illustrates the problem of today’s way of working. The figure shows the logical flow of value creation during an industrial investment and development project. The actual execution schedule will have many overlaps and iterations that are intentionally left out.

Figure 1: Current documentation practice during an industrial investment and development project

When an asset is developed today, the work begins by defining the overall requirements and functionality (DG0). The result is typically contained in a few documents, which means that at this stage a holistic description is feasible ❶. As the work progresses into the design phase of the asset, more specialized discipline expertise is necessary (DG1 and DG2). Since the way of working is document-based, the result is an increasing number of documents. This leads to a fragmentation of information spread across documents, due to the inherent features of their format. Because of this fragmentation, it becomes increasingly difficult to maintain a holistic description of the facility asset. The result is extensive interface coordination between discipline experts ❷. 

When the investment decision is made to execute the construction of the asset (DG3) ❸, the number of documents produced grows exponentially as the supply chain involving contractors, suppliers, and manufacturers ramp up their deliveries for the construction, installation, and commissioning of the facility asset ❹. At this stage, and probably earlier in the CVP, a lot of information in documents is duplicated, resulting in several sources of the same information. The consequence of this is labour-intensive work to prevent quality deviations and HSE incidents. When the operation (DG4) and handover to the operator take place ❺, information is fragmented and lacks relational information. This often results in a need to ‘re-engineer’ the solution to establish the holistic view necessary to maintain, control, and evolve the asset ❻. Reduced information quality can reduce the decision quality and is often costly and inefficient to manage.

Solution

Standardized asset information models for seamless asset information exchange.

The project was divided into two main parts, with two engineering companies responsible for developing their sections of information models. These sections were reviewed and approved by two different operators. The goal was to create, mature and integrate models using a common industry service of information modelling framework (IMF) types and standardized reference data libraries. The key objectives were to automate data exchange, avoid manual input and enable seamless collaboration. 

Figure 2 illustrates a new way of working using information models through the logical flow of value creation during an industrial investment and development project. The actual execution schedule will have many overlaps and iterations that are intentionally left out.

Figure 2: Using information models instead of documents during an industrial investment and development project

The overall requirements for an asset (DG0) can be captured in an information model from the beginning ❶. As the design phase progresses, detailed information models are created by discipline experts to mature the design (DG1 and DG2) ❷. These models, in a machine-readable format, can be continuously checked for design flaws and integrated to ensure a consistent and holistic description of the asset. 

When the decision is made to construct the asset (DG3) ❸, numerous information models are produced by the supply chain. As the project approaches operation and handover (DG4) ❹, the resulting information model becomes a comprehensive "model-of-models," containing historical context and all design decisions from DG0 ❺. This holistic description is available at any granular level needed for operation, control, maintenance, and future modifications ❻. Access to information is unrestricted by documents and formatting, allowing for simple navigation and efficient maintenance ❼.

Implementation of IMF types and standardized reference data libraries

The project delivered a Proof of Concept demonstrating the following: 

• Digital asset creation: Both engineering companies created digital assets representing their parts of the model using IMF types hosted by a third-party organization. 
• Defining new IMF types: The companies defined new IMF types (e.g., motor or transmitter) and made them available to the other party from the common library for review. 
• Digital verification: All participants verified their own flow and connection digitally. 
• Model sharing and review: The asset information models were shared with operators for review and their feedback was digitally incorporated. 
• Seamless integration: The asset information models were seamlessly integrated, correcting errors and connecting the correct instantiation positions.

Benefits and impact of implementing standardized asset information models

• Efficiency: Automated data exchange reduced manual work and operational costs.
• Accuracy: Standardized models minimized human errors and improved data integrity.
• Collaboration: Seamless integration enabled effective collaboration between different companies.
• Cost savings: Significant reduction in costs associated with correcting human errors.
• Less human errors: By moving from document-based to data-driven processes, the project demonstrated a potential reduction in human errors by at least 50%.