Forecast and prediction

“Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.” – Warren Buffett

AT the onset of every year, we always search and consume a multitude of headlines such as “Forecasting the world in 2020,” or “Predictions in 2020 and beyond.” Companies attend various seminars on economic forecasts and technology predictions in the current year and beyond only to understand what lies ahead — they forecast market demand, exchange rates, raw material prices, and others. Then based on these, build new production plants, create new entities, or launch new products.

The problem with these predictions or forecast, if we take them at face value, is that they may be either self-serving to those who purvey them such as analysts attached to vendors, or tinged with overconfidence and bias. Some of the most spectacularly wrong predictions made by erudite industry leaders are that of former IBM chairman Thomas Watson who said in 1943 that “I think there is a world market for maybe five computers,” and former Microsoft chief executive officer Steve Ballmer who in 2007 predicted that “There’s no chance that the iPhone is going to get any significant market share. No chance.”

This is further evidenced by the shortening time spent by large-cap stocks in the benchmark index, declining, from 33 years on average in 1985 to 20 years as of 1990. This will get even smaller in the future, shrinking to 14 years by 2026, according to forecasts.

“The S&P 500 death rate is rising,” CLSA investment strategist Damian Kestel said in 2017.

“A period of relative stability is ending. An increasing number of corporate leaders will lose control of their firm’s future.”

Look at the top ten firms in the S&P 500 now — most of them are tech companies such as Google, Amazon, and Facebook; whereas more than a decade ago it was dominated by traditional firms in oil, banking and utilities.

These large organizations are well-staffed by corporate planning wizards and forecasters from Ivy League schools, but still they were unable to respond to the fast-changing environment. Then what is the reason for all of these?

The world has never been as complex as it is now. The Fourth Industrial Revolution is bringing forth unprecedented changes brought about by technological advances in artificial intelligence, big data, and quantum computing, among others. This makes tradition forecasting approaches abundantly flawed.

In fact, numerous studies reveal the failure of the forecasts of even so-called experts. As Wharton professor Philip Tetlock, co-author of Superforecasting: The Art and Science of Prediction, showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance.

That’s why organizations together with its executives and planning departments need to be critical of forecasts and predictions, and instead built competency in forecasting themselves with accuracy.

In general, forecasts are based on quantitative analysis, also called objective analysis, and quantitative forecasting, also referred to as managerial or judgemental analysis. The extensiveness of the use of these types of forecasts depend on two variables — time horizon (short to long term) and data availability (low to high).

Quantitative or objective forecasts operate mainly under short to mid time horizon with sufficiently available data, primarily used for operations. This is where the use of big data analytics (predictive and prescriptive) and artificial intelligence (AI) become extremely useful.

Predictive analytics can identify future probabilities and trends by providing information about what might happen in the future, such as how the business process outsourcing companies in the Philippines use predictive analytics to understand causes of employee absenteeism and low productivity, and predict who and how many will eventually leave the company. Prescriptive analytics coupled with AI is dedicated to finding the best course of action, given the certain parameters, and suggest decision options to best take advantage of a future opportunity or mitigate a future risk, such as the ones used by the oil and gas industry to optimize operations on where to explore new oil sites to predict performing and non-performing oil wells.

Managerial or judgemental forecast, on the other hand, is employed under mid- to long-time horizon with little information, such as in strategic planning and policy-making. This is where much of human error comes into play such as cognitive biases, desire to influence others’ thinking, lack of domain knowledge, concerns about reputation, and so on, resulting in forecasts that are no better than anyone’s guess.

To improve judgemental forecast accuracy, professor Tetlock, in his landmark well-researched book, prescribes training among forecasters to be aware of psychological factors that lead to biased probability estimates, such as training exercises to quiz participants and uncover how well they know what they don’t know. Another is to assemble a team of forecasters “who are cautious, humble, open-minded, analytical — and good with numbers.”

These, coupled with forecasting tools and techniques, can make judgemental forecasts closers to accuracy. In our consulting work, we employ “sense-making” as a judgemental forecasting tool to map future competitors, industry activities, and customer preferences.

Sense-making “refers to how we structure the unknown so as to be able to act in it. It involves coming up with a plausible understanding — a map — of a shifting world; testing this map with others through data collection, action and conversation; and then refining, or abandoning, the map depending on how credible it is. Sensemaking enables leaders to have a better grasp of what is going on in their environments, thus facilitating other leadership activities such as visioning, relating, and inventing” as elucidated by professor Deborah Ancona of the MIT Sloan School of Management.

Combining the power of analytics and AI with sound judgemental analysis can greatly improve forecast accuracy. But companies will seize this advantage only if business leaders champion the effort, by recognizing the value it brings and accepting “what they don’t know.”

The author is president and chief executive officer of Hungry Workhorse, a digital and culture transformation consulting firm. He teaches strategic management in the MBA Program of De La Salle University. He may be reached at