Planning is prediction.
Forecasting is prediction.
Risk and resource estimation are prediction.
In all planning cases, predicting the future is the central theme. In project management our whole job is to predict execution (plan), and then execute on that prediction. We predict how many resources it will take, we predict risk, and we predict how long it will take to complete an activity. Then we put those predictions in a baseline and perform to that baseline. The problem is that only half of our projects meet schedule and cost objectives, based on the predicted work and how that work actually turned out as compared to how we predicted it would (PMI, 2016). However, there are processes from neuroscience and the behavioral sciences that we can implement today that will significantly improve prediction.
So, what is going on?
As a discipline we rely heavily on technical processes. However, prediction is way more than technical processes; the majority of what matters in prediction is what’s between the ears. We use all of these technical project management processes under the assumption that everything going on in the brain is rational and objective, then take these prediction outputs and stake people’s livelihoods, careers, and the project itself on that assumption. And what happens? Well, we already saw the failure stats on projects.
Now you’re asking “what do we about it”? The solutions are not as elusive as one might expect. There is neuroscience, behavioral science, judgment and decision-making sciences, and a whole host of other sciences that project management hasn’t tapped into. They’ve been doing research for decades, and some of the best research has been done in the last decade or so, with observed behavioral data and brain scans via fMRI that show how the brain responds.
The science of prediction is much more about what’s going on in your head. Think of your brain as a computer. It takes inputs from senses, processes them via a vast array of criteria, and then outputs a decision and course of action based on its calculations. The issue is that this computer is biased toward its own experiences and what it has observed, it makes decisions based on self and energy preservation, and calculates options to maintain this state. It is not as objective as you hoped. And it does most of this in an automatic mode known as System 1, without consciously informing you of its automatic processing. This automatic mode informs decisions 95 percent of our day, as the brain attempts to save energy (Croskerry, Singhal, & Mamede, 2013). To further exacerbate the automatic processing problem, time pressure (or a time constraint) causes even more automatic processing as well.
Now, there is hope for improving prediction accuracy, and thus planning and forecasting accuracy. As an example, Dr. Tetlock, an expert in prediction and researcher for the U.S. government Intelligence Community, found the following regarding prediction accuracy (Tetlock, 2015):
- Forecasters with no training: 36% more accurate than a coin toss
- Forecasters with training: 41% more accurate than a coin toss
- Forecasters with training in teams: 44% more accurate than a coin toss
- Elite forecasting teams: 66% more accurate than a coin toss
In addition to works such as Tetlock’s forecasting, the project management discipline can start informing its processes by tapping into multidisciplinary fields that are already using science to improve outcomes in both prediction and other decision-making. Below is an example of some of the sciences and where they apply across project management phases.
It’s no longer good enough to just input a piece of data into software and expect that somehow the process in and of itself will remove errors. I think we unrealistically expect that risk analysis will do this for us as well. But risk takes its inputs from humans. And humans produce errors; and way more errors than we are usually aware of. If you want to see an example of just how many errors we are capable of (and usually unaware of), check out this list of cognitive biases.
And what about Artificial Intelligence (AI)I? If we as project planners do not start getting better at prediction, AI will replace our jobs with more objective and realistic computers that are capable of accurately predicting project schedule, risk, and cost.
The intelligent planners of the future will be informed on how their brain operates, will plan and forecast more accurately, and their projects will have a reputation for completing closer to schedule and cost objectives. This is even more relevant for those projects that need accurate completion dates, such as in defense, aerospace, and hi-tech projects where time alignment is of utmost importance. Companies that are the most innovative and value a reputation for accurate and lower risk prediction will be on the cutting edge of adopting neuroscience and the behavioral sciences in project management.
The time is now to start adopting neuroscience and the behavioral sciences as a new underlying foundation to project management processes. They have done it with Behavioral Economics, Behavioral Finance, Behavioral Operations, and Behavioral Supply Chain Management. Now, it’s Project Management’s turn.
Croskerry, P., Singhal, G., & Mamede, S. (September 18, 2013). Cognitive debiasing 1: origins of bias and theory of debiasing. Bmj Quality & Safety, 22.
Project Management Institute. (2016). The High cost of Low Performance | Pulse of the Profession 2016. Retrieved from www.pmi.org: http://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/pulse-of-the-profession-2016.pdf
Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. New York: Crown.
Tell me your thoughts in the comments and let’s open a dialog. I would be excited to hear other opinions on this topic.
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Josh Ramirez, PMP, MSM-PM, is the Founder and President of the Institute for Neuro-Behavioral Project Management (www.nbpmi.com), and owner and consultant at www.evanclaer.com and www.behavioralprojectplanning.