FOR BETTER CAPITAL PROJECT OUTCOMES, BANISH OPTIMISM BIAS
Learn how evidence-based practices increase accuracy and set the stage for on-time, on-budget projects
It’s human nature to expect the best when starting ambitious, complex endeavors. This is no less true in capital construction projects, where optimism bias—the tendency to overestimate benefits and underestimate costs—contributes to over half of projects delivered late and over budget. Increasingly, project leaders see the value of replacing optimism bias with data-driven optimism, which uses real-world, standardized project data to make risk-adjusted plans that can flex in response to ongoing analysis and feedback. Integrated project controls software can help by translating the flow of project data into actionable insights and supporting unbiased feedback gathering processes across teams. Optimism bias can’t be eliminated, but it can be managed. The right data, properly standardized and analyzed, gives project teams the confidence to deliver on time and on budget—not through wishful thinking, but through evidence-based decision-making.
OPTIMISM BIAS PUTS CAPITAL CONSTRUCTION AT RISK
At the outset of large capital construction projects, teams often face intense competition to bid competitively with as low a budget and fast a schedule as possible. For example, a team might set a timeline based on the best unit or man-hour rates that they have ever achieved on other projects. But being unrealistically optimistic during the bidding phase could snowball into larger problems later. For example, even small delays could impact a very tight schedule. And the team might lack coverage for project risks with such a low contingency in their estimate. Without a proper risk identification process, the project could be headed for difficult times during execution. This scenario is just one example of how optimism bias can impact the success of large capital construction projects, over half of which are delivered late and over budget. “In terms of its consequences for decisions, the optimistic bias may well be the most significant of the cognitive biases,” writes psychologist and Nobel Prize winner Daniel Kahneman in Thinking, Fast and Slow.[1] He notes that while optimism can be beneficial and a driving force for leaders who depend on others’ confidence in their ideas, “executives too easily fall victim to the planning fallacy. In its grip, they make decisions based on delusional optimism rather than on a rational weighting of gains, losses, and probabilities. They overestimate benefits and underestimate costs.”[2]
[1] Kahneman, Daniel. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011. 387. Boundless by Baker & Taylor, https://sjpl.boundless.baker-taylor.com/ng/view/search/title/0009894968/9781429969352.
[2] Ibid, 383.
DATA-DRIVEN OPTIMISM LEADS TO REALISTIC CAPITAL PROJECT MANAGEMENT
Project leaders, including project controllers, need optimism to a certain degree—when managing capital projects, optimism can give them the confidence they need to overcome obstacles and take risks head on. The answer is thus not to adopt a pessimistic outlook but to embrace data-driven optimism. This mindset can break through bias, using past project and benchmarking data to improve decision making and risk mitigation for the present and future. To illustrate the difference between data-driven optimism and optimism bias, consider a traveler estimating how long it might take to drive to the airport at rush hour. Rather than base travel time on how long the drive would take on a weekend morning, the traveler can use a GPS-directed navigational app that accounts for real-time road and traffic conditions. In other words, instead of making plans based on irrelevant experience and a hunch, the traveler can use predictive analytics drawn from real-time patterns to make the flight. One tool for achieving data-driven optimism is reference class forecasting, or RCF, where risk is calculated based on relevant outside data. Research is increasingly showing that RCF leads to more project certainty. After introducing RCF in infrastructure projects, the average cost overrun declined from 38% to 5% in one UK study evaluating the outcomes of 107 major projects.
Average cost overrun declined from 38% to 5% after introducing approaches that support data-driven optimism, says a UK study
REAL-TIME DATA TURNS PROJECT ASSUMPTIONS INTO EVIDENCE-BASED DECISIONS
Embracing data-driven optimism is the first step towards counteracting the human tendency to be overly optimistic. But savvy project leaders need strategic steps to realistically assess project possibilities and mitigate potential risks. Today, technology makes it possible to capture more data than ever to predict and track construction progress. Machine learning models analyze historical and real-time project data to predict costs at each stage more accurately. Digital twins, which are often informed by video capture data, simulate construction sequences, test “what-if” scenarios, and predict how design changes will impact budget and schedules. How should a project team determine what level of detail to use in planning and execution? The level and type of data to analyze or rely on is contextual, says Scott Valdez, Director of Project Controls at MasTec. Highlighting a core strategy for improving capital construction outcomes, Valdez says that it’s crucial to consider what’s being measured, what project information is needed, and who is the end user of that information. “If I’m on a project and it’s Monday morning and I just want to know how I did last week, then I don’t need to be to the nth decimal to figure that out. If I’m talking to an owner and trying to bill, then that’s a very different situation,” says Valdez.
“It should never come down to ‘Well, this is what the project manager is saying and here’s what the data is saying.’”
- Cliff Massey, Senior Principal of Technology Solutions at MasTec
Define your scope precisely and break up the project into small, manageable chunks. Consider the simplest process for meeting each bite-sized goal. “What I like to think about at the beginning of these projects is, ‘Hey, there’s all of these requirements that we have to meet, but at the end of the day, what’s the simplest way that we can meet them?’” says Valdez. Evaluating schedule performance index (SPI) metrics from past projects can also be helpful during the estimation and scheduling stages. SPI fluctuations show clues of when and what risk factors occurred and the degree to which they affected the timeline. These can be used to create reality-based, risk-adjusted schedules for future estimates. Having the right up-to-date datapoints and a clear idea of your project’s scope, goals, and progress reduces organizational pressures to yield to certain voices over others—which could be biased. “It should never come down to ‘Well, this is what the project manager is saying and here’s what the data is saying,’” says Cliff Massey, Senior Principal of Technology Solutions at MasTec.
BALANCE HUMAN EXPERIENCE AND AI FOR UNBIASED DECISION MAKING
Artificial intelligence (AI) capabilities are still evolving in capital construction. But AI is already equipping teams with data and planning recommendations that can counteract bias. For example, machine learning, a subset of AI that enables systems to learn and improve without explicit instructions, can recognize project patterns and perform “what-if” analyses on cost uncertainty, plan sequencing, risk events, and more. The key is combining these machine-driven insights with human subject matter expertise and judgment. “Using data to provide suggestions in conjunction with human subject matter expert decision-making is really where managing bias becomes something of a reality,” says Jordan Brooks, Director of Products at InEight, while discussing the balance of data and human judgement in managing bias in project controls.
STANDARDIZED DATA ENSURES REALISTIC ASSESSMENTS ACROSS CAPITAL PROJECTS
Once project leaders agree on the right level of data to use, it’s important to collect, organize, and track that data consistently. For example, should equipment utilization be tracked in hours, minutes or days? Should materials be ordered in cubic yards or cubic meters? Even simple specifications like formats for dates and times ensure that scheduling software calculates critical path correctly. Standardizing your data also ensures seamless collaboration and tracking across your teams. When you connect estimate, schedule, and progress tracking via standardized data structures, you set up a feedback loop helping estimators evaluate how their estimate performed in real life conditions. “Knowing what’s in your units and having a standardized way of conforming your estimate into a budget is really critical for not only successful execution but also making that a timely process post award,” says Erik Wilson, Director, Project Controls, Austin Industrial. “Standardization of how these estimating activities and codes roll up to different accounts in your budget is really critical. And then of course, you can leverage that standardization when passing actuals back, and you can give your estimating team some sanity checkpoints to see how the estimating norms are aligning with our execution methodology.”
UNIFIED PROJECT DATA DELIVERS ACCURATE, ALIGNED CAPITAL PLANNING
After project leaders have developed a standardized way to use data, it’s time to build out a plan and revise it on an ongoing basis. To reduce optimism bias throughout this complex process, everyone across a project needs to work from the same data and knowledge library. All project work—from estimation to execution—has to be connected in one repository, which everyone knows to reference for all documentation and data from a project’s life cycle. This is where integrated project controls software can help ingest the many data points generated by a team and make plan suggestions for everyone to evaluate together. Such software also serves as a platform for soliciting feedback from multiple people asynchronously—which can reduce the pressure to conform to groupthink in live conversation, for example. “Someone in a leadership position can impact a group, but how can a group impact individual thinking?” says Brooks, underscoring how leadership and open communication are central to managing bias in project environments. “It becomes less about the loudest voice in the room and more about feedback that’s not being relayed because someone doesn’t want to have a dissenting opinion, which we see quite often in the industry today.”
LEADERSHIP AND OPEN COMMUNICATION ARE CENTRAL TO MANAGING BIAS IN PROJECT ENVIRONMENTS
Next, use SPI (schedule performance index) metrics as real-time reality checks. SPI is never a static value; it’s always changing in response to internal and external variables. When it’s within its acceptable operating range, SPI provides justifiable confidence in a project’s performance to a certain point. When SPI begins fluctuating outside of this range, managers have the time and opportunity to investigate why and make an informed decision about how to get the project back on track. Finally, having teams rely on checklists can also help align everyone on agreed-upon project milestones and success criteria. Checklists act as ongoing reality checks, providing evidence of how well a project is progressing and meeting construction schedules and client expectations. They can validate the planning or, as with SPI, turn a discovered error, defect, or risk factor into an opportunity to course correct. Ultimately, all of these recommendations create a plan that’s flexible, where all team members can question assumptions or predictions developed at the outset. A successful project plan adapts based on ongoing inputs, including risk simulations with multiple data points, validated feedback, and deliverables.
CHOOSE PROJECT CONTROLS SOFTWARE THAT REDUCES OPTIMISM BIAS IN CAPITAL CONSTRUCTION
Optimism bias thrives in data silos and disconnected systems where each team member sees only incomplete or inaccurate information. The right project controls software can replace hopeful assumptions with evidence-based reality. It’s crucial to look for software that:
- Converts the constant flow of data into a standardized structure: The right software enforces a standardized structure, resulting in unified performance metrics. The goal is to make it impossible to cherry-pick optimistic numbers and miss warning signs.
- Integrates historical data: Historical performance data validates current estimates and forecasts. This enables the team to easily see how current metrics compare to those from prior projects.
- Provides a single source of truth for all project data: Version control chaos and information scattered across emails, drives, and devices can lead teams to misinterpret or overlook inconvenient data.
- Detects risks in real time: Automated monitoring provides an early warning system against developing risks. Dashboards provide real-time visibility into cost performance indices, schedule variance, and productivity metrics that respond immediately to internal and external risk factors, from lagging productivity to materials pricing fluctuations.
Learn more about InEight’s integrated project controls platform, or explore InEight Project Controls.
COMBINE REALISM WITH CONFIDENCE TO ACHIEVE CERTAINTY IN CAPITAL PROJECT DELIVERY
It’s impossible to fully remove optimism bias—it’s human nature. But project teams can reduce bias by embracing data-driven optimism. With the right real-world data, standardized across teams and consolidated in a central place, project leaders can build risk-adjusted plans that evolve based on feedback and changing conditions.
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