DiCtion, Digitalizing Construction Workflows, is an Aalto University–led R&D project. It aims at a construction project in which digital data flow freely across processes and between participants. The project has developed a data model, an ontology, that makes data interoperable and enables automation. As a result, new opportunities for collaboration and shared use of data emerge.
One area where data sharing could create new value is quality management. Companies are typically quite myopic when it comes to quality management. They focus on their internal processes and collect data about them. They have little understanding of quality at the whole project level, leading to costly problems that repeat project after project. Companies deal with these issues reactively, with claims and mutual agreements.
DiCtion is now looking for ways to solve the predicament with data-driven project-level quality management, the digital quality footprint.
Introducing the digital quality footprint
DiCtion has studied the processes and data flows of precast concrete production extensively. The process involves a chain of several organizations, adding complexity to quality management.
The researchers have interviewed designers, a manufacturer, and general contractors. They have found that quality issues regarding prefabricated concrete panels and structural elements are often not evident until the installation at the construction site.
“We wondered if we should start developing practices that help manage quality deviations between participants,” said Professor Antti Peltokorpi of Aalto University. “The concept could materialize as a process and a quality database, an observation repository, that enables quality management between contributors and passes on learnings from one project to the next.”
The digital quality footprint could help the whole industry learn collectively. It would reveal, for example, which structures or design details pose a higher risk of failure than others. Furthermore, it could promote best practices and functional product specifications.
Quality issues are not equal
Markku Kiviniemi, a senior scientist at VTT, discussed two types of quality issues. First, there are obvious errors, for example, a missing reservation for an electrical outlet in a prefabricated structure. These lead to claims and monetary compensations. Second, a more frequent set of issues may not conclude in an official claim, but still has to be fixed. These are the deviations that DiCtion would especially want to collect and share between project participants. The target is to have self-learning feedback loops.
“In precast concrete construction, tolerances are vital. An interplay of tolerances can lead to a situation where a complex panel simply does not fit, and the design has to be revised,” Kiviniemi explained. “Another problem, which contractors have pointed out, is that the tolerances allow too much variation in panel surfaces. As a consequence, they require additional work for finishing. The cure would be to diminish tolerances.”
All companies interviewed by researchers had quite well-established quality management practices. Especially in manufacturing, quality assurance is an essential part of the production process. Quality observations are systematic, and organizations try to learn internally. However, that is not always enough.