Contractors are selected with care. Roles are assigned. Goals are clear and incentivized. Because of this, construction projects launch awash in optimism. Yet, optimism can become a project’s undoing when it becomes a source of unconsciously biased decisions.
Optimism in construction requires realism.
Optimists tend to be driven, cool under pressure and resilient. These are excellent traits for project leads. However, research shows that project leaders are largely unaware of the potential negative impact optimism bias can have on project budgets and deadlines. Common blind spots include potential labor disputes, site-specific challenges and regulatory complications.
This isn’t to say that projects should be managed by pessimists. Biases of any kind can be problematic, especially when unacknowledged. Organizational, leadership, group and human biases are most likely to affect projects. While it’s impossible to entirely remove bias, raising awareness is the first step to mitigating its impact. The second is challenging it.
The easiest way to manage project bias is to check it against measurable information. If optimistic budget assumptions are proven by the numbers, then data-supported optimism may be warranted. If not, there may still be time to adjust the budget. Managing optimism bias is less about managing a disposition than about managing risk.
The best construction insights are data-driven.
Shared, real-time data forms the foundation for performance monitoring that’s trusted. Diverse project stakeholders are more likely to make decisions based on resultant insights when they have a data platform that:
- Acts as a central project data repository to aid potential dispute resolutions.
- Standardizes data among users to ensure accuracy and real-time monitoring.
- Enables proactive contingency planning for “what if” scenarios.
The most relevant data for supporting or correcting optimism ties directly to either the project schedule or budget. Think of detail and accuracy as necessary counterparts to the potential distortion of unwarranted optimism, starting with highly detailed data results in more accurate analysis, forecasting and progress tracking. Accuracy starts with project setup and data collection practices.
“Fundamentally, if your quantities in scope are wrong, your forecast is inherently going to be wrong as well,” Kiewit Industrial Vice President Justin Terminalla says. “Quantities drive everything. So at Kiewit, a lot of our standardized processes revolve around that controlled management of pure quantities and making sure they’re in the system at the right time and that everything else is a by-product of the quantity management.”
Another useful control mechanism is reference class forecasting. Comparing historical, similar project data with current project data can make forecasting and scheduling more accurate and can be a good resource if budgets or schedules must shift. Software systems that include benchmarking, quick access to historical and as-built data and natively support sophisticated work demands can help. Advanced performance management practices, such as earned value management and schedule performance index, also support realistic project assessments.
“General rule of thumb, I’d say that 75 to 80% of the effort of forecasting really happens in the system. And then based on project knowledge, the team tweaks and fine-tunes by applying context,” CCC Group Director of Project Controls Ricardo Filho says. If, for example, the remaining work is more complex than previous projects, the CCC Group will further scrutinize the previous forecast.
The right data makes for great project outcomes.
When addressing optimism bias, consider the many items that are helpful to track. They’ll include change orders, RFIs, contract deliverables and quantity claims. It also will include budgetary items such as timesheets, payments and billings. A thorough assessment of data needs should be followed closely by a clear-eyed look at what it takes to collect accurate and detailed information.
“You have to find ways to connect your estimate, your budget, and your schedule to your short interval planning and controls effort,” Redpath Mining Vice President, Project Services, Rouan du Rand says.
“And the reason for that is that you can focus your construction teams and the construction effort on the work that they must be doing and not evolve into things that they like to do. You’ve got to collect your data as close as possible to the workplace, because that’s where the work gets done,” du Rand says.
Excellent data sets serve multiple downstream use cases. They can be used for integrated forecasting, ERT scheduling, managing contract life cycles, estimating financial impacts and collaborating among stakeholders, among other things. Optimism may be warranted, but it never hurts to back it up with a quick check on the data.