Bob from engineering has been submitting project proposals for the past decade, and always underestimates total costs. Despite double-checking with vendors and colleagues ahead of time, his projects seem to constantly run longer and cost more than initially expected.

Bob isn’t purposely reckless, he just falls prey to a phenomenon that is common in business: optimism bias. 

Also called the “planning fallacy,” this heuristic states that people tend to be overly-optimistic about how much things will cost, and how quickly and smoothly projects will run. Instead of anticipating surprise delays and expenses, plans are made with the assumption that everything will work out exactly according to plan. The result: projects that run longer and cost more are met with surprise.

They shouldn’t be.

It’s human nature to be overly optimistic. The real question is: what can you do about it? Start by understanding how to manage bias, then turn to reference class forecasting. By forecasting with data from comparable projects that have actually been completed, versus making personal assumptions, project leaders can get a more fact-based handle on what is likely to occur and how much it will cost.

From the classroom to the office

Behavioral economics as a way to understand consumer behavior has been a buzzy topic in the last several years. Popular psychology books focused on anchoring, recency bias, loss aversion and other heuristics have revealed to many how our blind spots can be exploited, for better or for worse. How bias impacts corporate finance, however, has been sequestered in academia.

Slowly but surely, however, these concepts are entering the world of business.

Any CFO will tell you that accurate forecasting is one of the most important functions their teams undertake. Misguided assumptions can throw even the best strategic plan off-track.

Even though precise predictions are a core expectation of finance teams, their forecasts tend to assume lower expenses and quicker timelines than reality. While misaligned incentives can be a driver for these undershoots, the planning fallacy is often the culprit. In fact, even when forecasters are shown their forecasting track records, they often continue to undershoot, assuming that the next time will go smoother than the last.

This isn’t a case of poor management or inadequate employees—even the largest of companies, like Sony, have fallen victim to these biases.

Sony’s infamous Chromatron shows the planning fallacy at work

Hersch Shefrin is a Canadian economist and pioneer in the world of behavioral finance. In his paper “Behavioral Corporate Finance,” he tells the story of Sony’s failed color TV venture in the 1960s. To summarize, one of the founders, Akio Morita, came across the best TV picture he’d ever seen at a trade show in New York. The screen was powered by a color tube called the Chromatron. Morita quickly obtained a license to produce a TV using the special tube, and began a multiyear effort to develop a prototype and scale production. 

Despite optimistic projections of costs falling dramatically with scale, and Morita’s steadfast confidence, the product’s cost held stubbornly at more than double the retail price. Even though they lost money on every sale, Sony pumped out more than ten thousand of these TVs. Only when the company was nearly underwater did the Chromatron project get scrapped.

This example illustrates the harm of the planning and sunk-cost fallacies: not only was Morita overly confident before the project began, but he continued to double down despite evidence that his assumptions were wrong. If he had cut his losses when the project started to go south, the losses would have been a fraction of the actual total.

The Chromatron story shows that even the best and brightest leaders can fall victim to natural human biases. As a way to control for these shortcomings, many companies are now adopting new forecasting models that are more dependent on data.

Reference class forecasting as an antidote to bias​

Instead of building a project plan based on assumptions, reference class forecasting starts with a review of projects with similar characteristics that have already been completed – including type and scope of project, duration, technical complexity, strategic objective, business unit, etc. Beginning a project forecast by looking at analogous past projects increases the likelihood that assumptions are rooted in actual past outcomes. 

The ability to do reference class forecasting is exceedingly difficult for companies still storing their project data on spreadsheets and disparate systems. But with Finario, a purpose-built capital planning tool, it’s not only possible — it’s a built-in feature.

Called Finario Predict, the system’s AI automatically queries the company’s project database and selects the historical projects that are most likely to be more predictive of a candidate project’s performance and provides a cost and ROI prediction for the proposed project based on that data. Needless to say, the more project data your company has to reference, the better.

Bob, the engineer in our example to start this piece, is human. He is prone to biased outlooks because he wants his projects to be funded. He wants his proposals to be viewed as being well conceived and researched. He wants to be successful.

The irony is, when projects turn out as they are “supposed to,” or even better, everyone wins. Which is why letting the data help do the deciding early on can be such a game-changer.

Interested in learning more about behavioral corporate finance? Watch the replay of our webinar, Eliminating Bias in Capex Budgeting and Forecasting. Victor Riccardi joined us – a visiting professor of finance and editor of seven Social Science Research Network (SSRN) journals on the topics of behavioral finance, behavioral economics, decision making under risk, and uncertainty, and more. 

Explore More Finario Resources