A few years ago, we made it our mission to change the design and delivery process of the built environment. It started with acknowledging the antiquated way our industry has designed for centuries: the manual design, review, analyze, re-design process. Rinse and repeat. Our journey to improve the process evolved by identifying efficiencies in iteration and design delivery. And in recent years, we’ve watched the real estate, construction, and architectural industries face major challenges getting projects off the ground due to financing, inflated material and construction costs, and labor/trade shortages. Combined, these issues mean that many highly anticipated projects fail before they even begin, disappointing all involved: stakeholders, community, client, and design partners.
To prevent future failure-to-launch projects, we began to look at ways we could better inform the design process at the earliest stages. We know that responsive design results in impactful architecture, but revolutionary design doesn’t mean “design-only.” Our first change was our perspective on what type of information could influence design decisions from the smallest data set (like size requirements) to the most complex inputs (like minimum investment returns).
Then we began to advance our technical approach. We manipulated our software so things like unit mix, construction costs, and revenue projections could be outputs instead of just floor plans and renderings. Simultaneously we began to think like our clients: project investors, developers, and the like. We adopted a financial mindset. Combined, we were able to develop tools to help us analyze a project’s economic feasibility and market implications before formal design even started.
WHY IS THIS THE FUTURE OF DESIGN?
When clients are evaluating a project’s feasibility, it’s traditional for proforma development, design, and stakeholder engagement to be siloed from one another. This leads to a flawed proforma and inaccurate assumptions about cost, market/community and user needs. In turn, the architect creates design solutions based on incomplete data: solutions that ultimately aren’t in line with the budget and don’t address actual spatial needs.
When a project’s proforma and design are in complete alignment, the collaboration between client, architect, and contractor is set up for efficiency from the very beginning. And by leveraging computational tools throughout this part of the pre-design process, we can develop realistic and effective design solutions, rather than aiming for pie-in-the-sky ideas that will never get built because they don’t address project constraints.
In the case of The MID in Detroit, our team was able to quickly iterate many different options for the client. For example, total rentable area vs. total gross area could be changed and evaluated in real time, eliminating unnecessary back-and-forth. Based on this data, we could dive into design with a complete understanding of each solution’s feasibility. This approach led to more accurate feasibility that helped us produce several iterations for the client in a fraction of the time. We were able to be intentional with their time, focusing on the design-forward, realistic solutions our client expected and deserved.
The Mid 4-acre mixed use development. Credit: INFORM Studio
An algorithm we developed for unit optimization. Credit: INFORM Studio.
what does this mean for the future of construction?
This data-first approach means each project has better speed-to-market potential. In the case of the construction of the 250+ facade panels on the Van Leesten Memorial Bridge, we created an algorithm to enable real time design adjustment. Data was stored in a way that SITU, the fabricator, didn’t have to start from scratch when recreating these panels in their software of choice. Working this way prevented process waste and quality issues, and construction was completed nearly 7 months ahead of schedule.
Providence Pedestrian Bridge. Credit: Kroo Photography.
When designing the East Residence Hall at Lawrence Technological University, data manipulation helped us quickly determine facade options that maximized views while reducing the quantity and type of openings. This saved money in construction costs, while reducing material waste. Additionally, optimizing the programming module reduced the original square footage by 12%, while still allowing the finished building to maintain a standard of design excellence on campus.
Lawrence Technological University. Credit: Kroo Photography.
When designing Cauley Ferrari in West Bloomfield Township, we determined that we would need to narrow the gap between digital representation and the physical manifestation of the complex three-dimensional surface that made up the building’s signature facade. The point cloud scan technology allowed for increased precision while providing tighter tolerances during prefabrication of the metal panels. The panels were fabricated and assembled in a shop-controlled environment, shipped and installed on site, and coordinated with the Construction Manager to ensure proper sequence of construction. The technology tools enabled a better design output, allowing the team to achieve an iconic exterior without wasting materials or billable time.
Cauley Ferrari of Detroit. Credit: Kroo Photography.
how does this affect future processes?
Since developing and consistently reevaluating our toolkit, we’ve been able to work with our clients more collaboratively, and with better results. More advanced cost modeling and smarter proformas mean we have more information up front to inform our designs, while clients can better prepare for potential challenges ahead of time. Our data-driven process targets specific issues before we even begin design: stakeholder collaboration, process waste, cost modeling, and trend analysis to name a few. While traditional design processes are siloed, our approach allows us to identify input and output data and target solutions accordingly. Our approach helps ensure no one – clients, stakeholders, or architects – wastes time pursuing an idea that won’t get built.
The process we developed is iterative. It is flexible, adapting to project and client needs in real time. Evaluating data before we even put pencil to paper means we can design smarter, faster, and more thoughtfully, and that’s what we’re about.
When a project’s proforma and design are in complete alignment, the collaboration between client, architect, and contractor is set up for efficiency from the very beginning. And by leveraging computational tools throughout this part of the pre-design process, we can develop realistic and effective design solutions, rather than aiming for pie-in-the-sky ideas that will never get built because they don’t address project constraints.
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