Research shows that 5 out of 4 business professionals do not understand statistics (pun intended). Most people don’t have a natural affinity for probability either. But probabilistic thinking lies behind predictive analytics, becoming a data driven organization, and the next wave of demand planning.

It’s not intuitive to most people, and we tend to think linearly or are still stuck in random thinking. Learning the mechanics of probabilistic thinking takes time and unless you are engrossed in it frequently, it does not come naturally.

## Most Of Us Think Linearly, And That’s A Problem In Planning

For example, let’s take a hypothetical planner named Lauren who forecasts we will sell 93 units for the next period. We may know that this is only a single best estimate and may present it as such, but what other people still hear is blah blah blah 93 units. I.e, they think the forecast is just a single number even though predicting an exact figure with any degree of confidence is impossible. People, and therefore most companies, think linearly and focus on a number when they should be thinking about the risk or opportunity associated with that number.

## But Other Functions Need An Exact Number

But in many ways, we are providing what others want and need. Individuals have difficulty comprehending multiple truths and therefore have a desire for “the” answer. Most MRP systems and supply chains require a discrete signal to drive supply planning and operations, and struggle with uncertainty. Finance is looking for “the” number of what sales will be, so they can plan their P&L to the penny. To accommodate everyone and cater to people’s lack of understanding of probabilities, demand planning provides a single point or deterministic forecast of the what will occur. A Deterministic Forecast is a forecast in which outcomes are a single point determined through known relationships or patterns, without any room for random variation.

## As Humans, We Tend To Struggle With Understanding Probabilities

Look at a common recurring miracle that happens in organizations every year. The forecast is 93 units and yes there is upside but the most we will ever sell is 153 units. This is with 95% confidence so there is less than 5% chance we would ever sell more than that. And then the miracle occurs where sales far exceeds expectations for a single period and everyone is shocked. Consider that with a 95% probability of an occurrence in a period of let’s say a week, that would be close to a 20% probability of the inverse (or more than that amount) in any given week during a 4-week period. There is just shy of a 50% chance over the quarter of seeing a miracle, and you should expect that 3.6 miracles may occur over the course of a year.

We all get caught in thinking the implausible is impossible by not comprehending probabilities

Whist this may be slightly confusing, and you may have to pause and reread that paragraph a couple of times, the point is we all get caught in thinking the implausible is impossible by not comprehending probabilities. This is just one subtle example of many I could come up with. The more egregious ones come in the form of confusing accuracy and probability – and no, 55% MAPE does not necessarily mean you could do better with a two-sided coin.

## Embrace Ambiguity In Your Forecasting & Planning

So, if so many do not understand it and so many are only asking for deterministic single, why is this important?

At the core it is what demand planning and predictive analytics does. Two of the golden rules of demand planning clearly state:

• Demand Plans are rarely precise.
• Demand Plans can be accurate and should include probability and/or an estimate of error.

We as demand planners live in the world of ambiguity and uncertainty, and transform it into insights the business can use. More than managing numbers, we manage assumptions and need to understand their individual contribution. We use weighting and ratios and work towards the best fit of our data sets to the right model to minimize error or uncertainty and provide answers.

Our world is changing as well, and we need to adapt. Predictive analytics and probabilities just may be the train that is taking us into the future. We have already seen a shift from traditional time series modeling to predictive analytics due to omnichannel and e-planning, much of which is driven by regression models or even more sophisticated machine learning and probabilistic forecasting.

## Black Swans Are Real, & Probabilistic Thinking Allows Us To Be Ready When They Happen

Most importantly, whether the business knows it or not, they need probabilities and to better understand likelihoods along with uncertainty. Black Swans are real and we need to understand what we can mitigate and what we will accept. Whilst this is being done already by many companies, data driven organizations are using analytics, demand planning, and most of all probabilities to drive advanced decision making. For supply chain, it is more than 93 units with a COV of 10% – it is understanding what to do when you have a 70% chance you can sell 5% less and a 30% chance you may sell 50% more (how would you plan for this?). For Finance, it is a better understanding of cash flow and for marketing, it is about identifying the micro-targeted ads that are likely to succeed.

Probabilistic forecasts allow us to identify the likelihood of a black swan event occurring, meaning we can be prepared when they do happen.

Even though there are great benefits, thinking in these terms is not natural, nor is it common. The good news is that probabilistic thinking is like a muscle and the the more you use it, the easier it becomes. Probabilistic thinking takes time and unless you are engrossed in it frequently, it does not come naturally. It is our responsibility as predictive analytics and demand planning professional to embrace and push others to see what we see – not only because it is our job and an important tool in our tool box, not only because it is becoming a necessity in the new e-planning environment, rather because we can help move the company towards being a data driven organization.