Design to Manufacturing in a Connected World

The Connected World

We live in a connected world. As the construction industry is gaining traction in its digitization journey, the line that has been separating the sector from the far more technologically advanced manufacturing is starting to blur. For years, the connection between design, construction and manufacturing has been hindered by the lack of collaborative and integrated workflows based on data.

Today, in the era of Industry 4.0, Building Information Modelling, digital twins, generative design technology, however, there are no more technological barriers to the synergy between these fields. Through technology, we can achieve streamlined construction processes with almost zero waste and almost no impact on the environment, we can eliminate human errors, we can even use AI algorithms to generate design solutions according to set criteria allowing us to create anything we can imagine.

In order to achieve these goals, however, technologies such as these need to become business as usual for everyone. A global sector needs to change. It needs to enter the data economy. It is our belief at Cobuilder, that in order to become a trusted asset in this economy data needs to be standardized.

digital twin

Why is the construction sector still at a stage of early adoption regarding the implementation of digital technologies?

Today, ‘BIM’ is seen as ‘Model’, but really BIM is about the management of information models – models that consist of geometry, documents and data.

Federating data is as important as federating models. Important prerequisites such as a common working environment and a common language that avoids clashes are not only limited to the modelling field. The situation in the construction industry today is that the geometry part of BIM has reached a high maturity level and its potential is understood, but the full potential of BIM is far from being utilized.

This is because to this day, the information (data) part, interoperability and implementation (e.g. workflow) remain immature.

Different BIM authoring actors have developed their data architecture (families, materials etc.) based on their own data structures.

This poses an industry discrepancy since manufacturers structure their data based on standards (CEN, ISO, ASTM etc.) that are mandated by law, and these are used in specification and manufacturing.

Other market actors use classification, private naming conventions and in this way also contribute to the creation of silos across the whole value chain.

The result is models that are of little use to contractors, manufactures and clients as the data definitions are not linked to any common sources of truth such as the standards manufacturers use. There is also the problem in the use of global properties (data not defined by standards) and local properties (data defined in standards) as standards vary across markets. So, how can we assume that it would be easy to produce something bespoke on the basis of a model when

the dissonance between model maturity and data management in the construction industry is striking?

This causes a great opportunity for change in the sector.

The solution

To remove the barriers to the full utilization of what today’s technologies are capable of, agreed common frameworks should be adopted. This is where standardisation of data comes in.

If we want more and more companies to start using smart technologies to do complex predictions, automate production on the basis of building models and to use AI for various purposes, we really have to think of how we can bring standardisation to the way we use data.

According to organisations such as ISO, ASTM International, the European standardisation body CEN, where policies and standards for digital construction are made, BIM is not only about the management of information models. It is about the management of consistent, traceable data that follows common structures ((P)DT), common definitions (IFD) and through common formats (IFC), and common methodology (IDM).

In order to create rules and automation paths for machines that can be applied globally, we need to study these standards and developed a knowledge library of trusted, connected data that anyone can use.

This is exactly what we have done.

Learn about it here.