Need to understand our Biases in Demand Planning
One premise of economics is that when faced with choices of varying value, people will choose the greatest value.
When they don’t, they are deemed irrational.
The reality is that all of us make irrational decisions all the time, because decision-making is rarely clear-cut. Indeed, research over the last 40 years, including the work of Nobel Laureates Daniel Kahneman and Richard Thaler, has demonstrated that decision making is much more informed by unconscious processes than we are aware.
The study of these processes in decision making is known as Behavioral Economics.
In Judgment Under Uncertainty, Kahneman and Amos Tversky began to uncover how the disparity between model and real-world decision-making is ultimately linked to the evolution of the human brain.
The Pre-Frontal Cortex, which is responsible for, among other things, abstract thought, risk/ reward and future value considerations—all key elements of planning—is unique to humans and is a late evolutionary development. It takes more than twenty years to finish developing, and even then, it takes practice and conscious effort to engage it.
Consequently, many of the decisions that we assume are being made rationally are actually being made by the most primitive structures of the brain.
This quick-thinking part of the brain serves us well when nearby dangers require immediate action, but in situations requiring careful and abstract consideration, the heuristic or instinct-based reactions often lead to suboptimal and heavily-biased results.
These mental short-cuts, are the very process that allows us to navigate the roughly 30,000 decisions we have to process daily, but when objectivity and critical thinking are required, they can often fall short.
What does this mean for demand planning?
To begin with, there is a certainty that without proper intervention and screening, every aspect of demand planning will be flawed. But it also means that by properly understanding the prevalence and causes of these biases, every aspect of demand planning has the potential to see improvement.
Any guesses about what are the popular biases that abound in the domain of Demand planning?