Reusing information is a common need in the Architecture, Engineering, Construction, and Operations (AECO) industries. Every project starts with a statement of requirements: an exhaustive list of specifications for the asset to meet. The design, contract, and final product have to comply with all requirements from the standards and be verified in the end.

However, the industry still relies heavily on paper and text documents, such as PDFs and Word files. This poses a significant challenge as computers cannot interpret the meaning of the text within these documents. Resulting in time-consuming manual interpretation, copy-pasting, and the potential for inconsistencies and redundancies.

The consequences of relying on textual requirements can be severe, ranging from miscommunication to costly mistakes. To overcome these challenges, requirement managers and designers need digital requirements that meet specific criteria:

1. SMART: Digital requirements should be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART). This guarantees unambiguous implementation and wide accessibility, enhancing collaboration and operational efficiency.

2. Centrally Managed: Requirements should be managed centrally to allow for reuse in multiple projects, saving time and effort.

3. Machine Readable: Requirements should be made available in a neutral format to various applications, promoting interoperability, which reduces miscommunication and costly mistakes.

Once your requirements meet the aforementioned criteria, you can imagine transforming textual requirements into structured data. Thereby bridging the gap between human-readable text and machine-interpretable data. One crucial aspect to consider before moving on is the depth of meaning (richness of semantics) within each requirement.

When structured requirements meet the needed criteria, storing them is necessary. Building libraries of of them reveals a spectrum of explicitness. This spectrum ranges from single pieces of text, to text linked with topics such as subjects, and in which phase of the process it is relevant to. 

The level of automation required in a specific use case dictates the level of interoperability and the level of explicitness. Simply making requirements, SMART is insufficient for achieving interoperability. A well-defined semantic standard is essential, expressing concepts such as ‘requirement,’ ‘text,’ ‘characteristic,’ and ‘unit of measure’. This enables both software developers and domain experts to exchange them more easily and process them in applications.

The explicitness level should align with the desired level of automation. In basic use cases, explicit text parts and cohesion with single subjects are sufficient. However, as use cases become more complex and demand deeper reasoning, explicit qualitative and quantitative characteristics, accompanied by essential metadata, become necessary. Software needs to comprehend concepts like ‘length’ and assess for example, if ‘the length of this beam is within predefined boundaries’ using decimal numbers.

By leveraging model-driven approaches, organizations can transform SMART requirements into digital ones. This revolutionizes the management and interpretation of digital requirements, enabling automation, reducing errors, and streamlining processes across the AECO industry.

In summary, the paradigm shift from textual requirements to model-driven approaches holds immense potential for improving the efficiency and accuracy of the AECO industry. By embracing digital requirements that are SMART, centrally managed, and accessible to various applications, organizations can overcome the limitations of textual documents . This leads to improved collaboration, reduced errors, and ultimately, enhanced project outcomes.

Want to read more about this topic or better understand the background? Download the white paper ‘Next Level Smart Standards: requirement(s) management in the AECO industry’.