Data-driven engineering and construction

CADMATIC Business Development Directors Jari Pynnönen and Tommy Norström share their views on data-driven engineering and construction

Antti Leikas

Posted on June 29, 2020

Process industries across the globe and currently undergoing a digital revolu­tion that is creating and transforming business models. They are increasingly embracing the immense possibilities offered by advanced digitalization in the plant engineering and construc­tion life cycle. But what is all the fuss about? I asked CADMATIC Business Development Directors Jari Pynnönen and Tommy Norström for their perspectives on da­ta-driven engineering and construction.

Each design and construction phase begins with data collection. Data is also derived from the preliminary de­sign process, specifications and customer’s needs map­ping, and nowadays also increasingly from various open data sources. Statistics, maps, photos, traffic statistics and practically any other relevant sources of information in the various phases of the design process provide use­ful information. All this data should be stored in an easily manageable format and in databases where the con­stantly increasing amount of updated data is easily avail­able in all phases of the project without any delays.

This is quite a challenge and how can this be achieved? Is data-driven engineering and construc­tion maybe something new?

“Not really” answers Jari Pynnönen, Business Development Director (EIA) of CADMATIC Oy. “Data-based engineering started already in the 1990’s, meaning that the data content of various design docu­ments has evolved hand in hand with technical devel­opment. The goals have remained unchanged all these years, and progress has been systematic. The aim is to make the design process more effective by utilising data from previous projects, enable better change manage­ment and share information between the various parties.”

Jari stops to take a breath as the actual list is quite a bit longer. 

“However, at the beginning of this century the prevailing term, at least for a moment, was BIM which stands for Building Information Modelling. But unfortunately, this caused even more confusion when data models, product models, modelling, product data models and all possible other terms got mixed up. This was true particu­larly when people assumed data models refer to a 3D image.” Jari Pynnönen shakes his head. “And more data just kept coming in all the time.”
Business Development Director (EIA) Jari Pynnönen says that that data-driven engineering started in the 1990s.

Business Development Director (EIA) Jari Pynnönen says that that data-driven engineering started in the 1990s.

What is data used for?

Mixing up the terms ‘data model’ and ‘3D image’ is understandable, since it is true that a modern 3D document is at the core of the data-driven approach. But this is not in any case a simple three-di­mensional image, but instead a database that includes all neces­sary data for the project. 

“A data model may also include 2D images, and basically almost anything,” Jari explains. “Also, the model becomes enriched and ex­panded during the project, as more data is received when the project is being carried out. This way, the model becomes a kind of digital twin, or a kind of data-based clone of the existing reality.”

Why go through so much trou­ble? What are the benefits? Tommy Norström, Business Development Director (Process & Industry) of CADMATIC, provides some ex­amples.

“A data-driven data model enables feeding data in the pre­fabrication process which, in turn, helps to avoid problems between production and construction. Similarly, the model provides in­structions for the correct installa­tion sequence, and practically for the whole construction process,” Norström adds. “And all the data acquired during the process is fed back to the data model, which again enables close monitoring of the project’s progress.”
Business Development Director (Process & Industry) Tommy Norström says that data fed into the prefabrication process helps to avoid problems between production and construction.

Business Development Director (Process & Industry) Tommy Norström says that data fed into the prefabrication process helps to avoid problems between production and construction.

At the other end there is the beginning of the design process, which is always the starting point.

“Both the designers’ skills and the properties of the tools they use should be on the level that enables operating a data-driven design process” Norström says. “We have learned that it is possible to carry out even the most challenging projects efficiently when the re­quired precise competence for every project is selected carefully in advance. In practise, members of the project organisation and teams may be located almost any­where in the world. However, this means that both the designers and the project managers are able to meet the challenges related to this kind of organisation, and also see the benefits it provides,” says Tommy.

When the all design takes place in a single centralised database, all teams have up-to-date information on the progress, and all necessary data at hand. When change management and documentation are added to this well-managed process, the dura­tion of the project will be reduced significantly.

“One of our bigger clients told us that the duration of their projects has been reduced on average by 30.%” Jari Pynnönen adds. “But we cannot reveal the name of this client because their previous operation model would look quite inefficient when com­pared to these figures.”

Data is similar to oil – what happens to it when it is being processed?

More and more companies are now employing data analysts who participate in the design process alongside designers, programmers and project managers. Their task is to filter the necessary and useful data for the project from enormous amounts of data.

Unlike oil, data is a renewable natural resource, which does not run out. The data created during the design process needs to be processed to avoid a huge mess that might be called hazardous waste. Employees and tools with the necessary data management characteristics for processing, storing and rotating massive amounts of data as efficiently as possible are of key importance in this process.