Navigating Uncertainty: Probabilistic vs. Deterministic Forecasting

The quest for a “sure thing”

Everyone loves a "sure thing" - a seemingly predictable outcome with little doubt or risk. Whether it’s a financial investment or your favorite sports team’s odds of winning the championship, the “sure thing” promises reassurance in a world of uncertainty.

In the dynamic business landscape – as in much of life - the notion of a “sure thing” is elusive since unpredictable circumstances can disrupt even the most outwardly secure outcomes. It’s no surprise that forecasting plays such a pivotal role in business planning across every industry and every business process. Whether it’s predicting customer demand, financial outcomes, or the impact of a new marketing campaign, businesses rely on forecasting to make informed, timely decisions. But what kind of forecasting are you betting your business on:  deterministic or probabilistic?

Common forecasting approaches explained

Deterministic forecasting

Traditional deterministic forecasting and planning methods attempt to predict a single, best-case outcome, using historical data, known variables, and mathematical models. They work well in stable situations where predictable patterns can be observed, and where small variations don't have major implications.

But what if inflation spikes? Or you enter a new market? What if there’s disruption to your supply chain? Deterministic forecasting is unable to adapt to rapid shifts in supply and demand, which can prove catastrophic to a business. Even relatively small forecasting misses can equate to millions of dollars in losses due to poor resource allocation, lost sales, high carrying costs, missed opportunities, and poor customer satisfaction.

Probabilistic forecasting

Recognizing that the world we live in is more uncertain than certain, probabilistic forecasting looks at a range of potential outcomes with their associated probabilities to allow for more accurate decision-making. This range of variability more closely reflects fluctuating demand patterns, enabling businesses to accurately plan for a variety of scenarios.  

In a classic risk vs. reward situation, probabilistic planning lets business planners evaluate tradeoffs between different business constraints to determine the best course of action for their given use case. For example, by investing more in inventory, the business reduces the risk of stock outages but increases their cash outlay, carrying costs, and potential markdowns if products don’t sell as expected. But on the flip side, trimming inventory costs elevates the chance of stockouts, leading to dissatisfied and perhaps even lost customers. This tight interlock of forecasting and variability across any possible set of outcomes and constraints is the foundation for operating excellence in an uncertain environment.

Choosing the right approach

The choice between deterministic and probabilistic forecasting depends on several factors, including the level of uncertainty in the data and the complexity of the forecasting problem.

Deterministic forecasting provides a single, most likely outcome without considering uncertainty or variability. This approach offers clear-cut predictions, making it suitable for scenarios where historical patterns are stable, and the underlying factors are well understood.

For scenarios which present a high degree of uncertainty or variability in the data, probabilistic forecasting provides a more realistic assessment of possible outcomes. In our global, digital economy where goods and services are procured, manufactured, distributed, and consumed across broad geographies and channels, uncertainty is certain. Without proper contingency planning, businesses can fail in the face of unexpected conditions such as rising inflation, supply chain disruptions, or cyber attacks. Probabilistic forecasting acknowledges uncertainty and provides a range of possible outcomes along with their probabilities, giving businesses the insights they need to plan for the unexpected.

In many cases, the best choice may be to use a combination of deterministic and probabilistic approaches, leveraging the strengths of each as appropriate for the given use case.

‍Conclusion

By understanding the differences between these two approaches and considering the specific context of each forecasting task, businesses can create more informed, accurate, and actionable predictions for their business. And while there’s rarely such a thing as a “sure thing” in today’s dynamic business environment, capitalizing on the benefits of both deterministic and probabilistic forecasting and planning provides the best approach in an uncertain world.

For a deep dive and ROI comparisons of deterministic versus probabilistic forecasting and planning, read this whitepaper.

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