AI-Powered Information Increases Efficiency in Project Delivery
The entire notion of a design and construct (D&C) contract is propagated on the idea of reducing the initial cost, but it often results in little regard paid to building quality and performance. As a result, some general contractors and suppliers make an entire business of finding fault and value engineering opportunities in the design to deliver them at a lower initial cost. The industry should stop wasting time with such an inefficient process and adopt a better process to reduce capex and opex.
In the short term, finding flaws and redesign opportunities can reduce capex, but it can vastly increase opex because all those changes require additional time and cost at the outset and throughout the building lifecycle due to poor quality. In an industry plagued with risk, litigation and time and cost overruns, the construction industry isn't helping itself by continually adopting inefficient processes. The cost of redesign work can add as much as 21% to the cost of a project, according to a study in the "Journal of Engineering."
The problem lies in the information disconnect between designers and the general contractors, even in a D&C contract where the two are supposed to be working closely together. The initial designers don’t have good information about the availability and pricing of components, producing nothing more than design intent, so when it comes to D&C contract execution, rework and changes are required. This highlights that the entire notion is nothing more than a false economy at the outset, without even factoring in the added lifecycle costs due to poor quality.
Data must be available to every stakeholder throughout the entire process, especially during the preconstruction phase. After all, how ludicrous is it that designers create the instructions to build a structure in a BIM platform, without knowing whether materials are available and priced within a range that won’t throw the budget out of scope? And when they do specify a well-resolved solution in the design, they have no idea whether it will actually be built that way. Would Samsung design a new phone without a complete understanding of the manufacturing process and method that they’d need to use? And yet, even in D&C contracts, a designer is working almost entirely in the dark regarding the process the general contractors will use to build and the status of the required components.
Designers certainly have good intentions—they’re working to create designs that can be easily and efficiently realized. But without ample, early, high-quality information from suppliers and contractors, they can’t possibly design around challenges that they don’t understand.
Preconstruction requires everyone, regardless of whether it is a D&C contract, to invest large amounts of time, money and resources to manage the process. It is therefore worth placing far more emphasis on streamlining this process than it is embarking on a process that emphasizes inefficiencies and claws back value in building quality.
A product approach that leverages modern data and AI technologies can substantially streamline the process and produce a design that general contractors are much more likely to be able to build without making substantial changes. Essentially, this is “Process as a Product,” using automated processes to ingest design intent in the form of 2D and 3D geometry—and automatically pull in the relevant data, design and cost-relevant information. Providing this information early in a project’s lifecycle significantly streamlines the process because it identifies issues when they can be easily and inexpensively addressed by the very people designing the building.
The Promise of AI and Data Delivery
AI plays a critical role in eliminating the need for D&C contracts and avoiding the expensive redesign that frequently results in these deals. AI can find relevant information for designers so they can be much more dynamic. Instead of using computational design scripts, AI can take the geometry (i.e., design intent) into account, assess a situation and provide several possible design solutions that fit the set criteria.
AI isn’t enough, however. The data that AI unearths is worthless unless it is delivered to the right people in time to make a difference in the design. When AI is deployed in concert with strong computational tools that make data readily available, designers become empowered to apply their experience and knowledge within the context of multi-criteria data analyses that reveal key information like:
- The physics of moving specific materials;
- Requirements for resources and labor to construct design elements; and
- Pricing and availability of materials.
As a result, technology can unlock the full potential by enabling true design for constructability with a process that combines AI/machine learning and computational tools to provide relevant, timely data. In this way, construction can eliminate the need for a “finding fault” business model, because the designs will be ready for construction, without the need for significant changes for constructability.