With more data than ever before, and an industry increasingly turning towards greater collaboration, will it finally be possible for contractors and owners to find the sweet spot between scope, schedule, and cost which has so often eluded projects of the past? Catie Williams, Vice President of Product Development at InEight, discusses.
COLLABORATION AND THE CONSTRUCTION DATA SWEET SPOT
The relationship between asset owners, designers and contractors is changing.
While previous contract methods had clear responsibility delineations and hand-offs, more projects are being built with shared risk and collaboration methods.
These collaborative methods pave the way for greater transparency, more equitable risk sharing, and enhanced visibility of project data, moving the industry away from historic patterns of disconnect, misinformation, and contention between parties.
Although the industry is still finetuning the process of collaborative working, it is a trend that is here to stay.
The use of integrated project delivery (IPD) models is expected to rise by nine percent in the next three to five years, according to this year’s Global Capital Projects Outlook (GCPO), with contractors recognizing its huge potential to improve stakeholder communications and unlock opportunities for AI and data analytics.
THE SCOPE-SCHEDULE-COST SWEET SPOT
Results from InEight’s most recent GCPO report suggest that this may well be the case, with construction professionals surveyed using collaborative project delivery approaches tending to complete projects on schedule more frequently.
That said, it also found a correlation in the data that saw those professionals reporting staying on schedule the most also reporting the highest overspend.
When risk is shared, as it typically is in more collaborative ways of working, both parties work together on coming up with solutions versus it being one sided, whether that be changes to the scope, resource challenges, or supply change issues.
Teamed with an integrated approach to construction project management, the contractor and owner both have the data and analytic prowess to forecast the various implications of most scope-schedule-cost scenarios and collaborate on making a decision that is in the best interest of the project.
GETTING A DATA FOUNDATION IN PLACE
Being able to make reliable decisions based on these predictions relies on the quality of the underlying data – something that many in the industry are still getting to grips with.
Anecdotally, it seems only a small minority of construction organizations have a consistent data structure, automatically collect data from real time entries, and analyze that data for regular reporting. Without data integrity, it is difficult to be confident in the insights provided.
A key challenge is the unavoidable fact that projects are so unique and decentralized, driven by different specifications, owners, and project teams.
Data collection has rarely been standardized. While most companies recognize the need to collect and report on some standard elements, it takes years to build up the volume of data needed for predictive purposes.
Some organizations may not be ready to think about predictive analytics, but the advice is to digitalize any remaining processes soon and begin building a data foundation, so it’s ready for analysis when they are.
The effort required to put a data foundation in place should not be underestimated. It goes right to the very core of the business and requires comprehensive change management with strong executive leadership.
For a data foundation to be built successfully, consensus needs to be reached on the business processes that will support it. Project teams, typically operating in siloes, will execute these processes in different ways, and deciding how to move forward as one is often harder than most people anticipate.
Finding the scope-schedule-cost sweet spot is becoming increasingly possible as the construction industry digitizes and becomes more collaborative in its approach.
However, quality data is absolutely vital and can take longer to establish than most would think. Building a healthy data foundation has a long lead time, and while your organization may not be ready for predictive analytics yet, your future self will thank you for establishing consistent data processes now which you will come to rely upon in the future.
